If you're a developer who wants flexible SDKs and APIs, predictable pipelines, raw data access via SQL, and integrations that fit into their existing tooling – not a black box optimized for ad platforms – you're in the right place.

This guide compares the best CDPs for developers to help you collect, unify, query, and move customer data with confidence.

What features do you need in a CDP?

At a minimum, a CDP should include:

• Data collection from multiple sources (web, mobile, server, third-party tools)
• Identity resolution to stitch together anonymous and known users
• Audience segmentation and cohort building
• Integrations with downstream tools (CRMs, ad platforms, email, warehouses)
• Real-time or batch data syncing

The best CDPs go further and give you analytics capabilities so you can actually answer questions with your data:

• Event-based tracking, with full control over your data schema
• Funnels, retention, and cohort analysis, to understand user journeys
• Session replay and behavioral context, to see what users actually did
• SQL access and raw data exports, for custom queries and external tooling
• Data warehouse integration, so your CDP works with (not against) your existing stack
• Privacy-first design, to stay GDPR/CCPA compliant without extra work

Here's how some of the most popular CDPs compare:

You might notice some absences: Salesforce Data Cloud, Adobe Real-Time CDP, and Treasure Data are all major enterprise players. They're powerful, but they're also designed for large marketing orgs with dedicated implementation teams and big budgets to match.

This guide focuses on tools that developers can actually evaluate, set up, and get value from without a long-winded implementation project.

What's the best CDP with analytics for developers?

1. PostHog

PostHog takes a different approach to customer data: instead of being a CDP that bolts on analytics, it's an all-in-one platform where CDP functionality works together with product analytics, web analytics, session replay, feature flags, experiments, error tracking, LLM observability, surveys, and more natively.

PostHog's data pipeline provides full CDP capabilities through three core components:

• Sources: Ingest data from 20+ managed sources (Stripe, Hubspot, Salesforce, Snowflake, BigQuery, Google/Meta/LinkedIn/TikTok Ads, and more) or self-managed object storage (S3, GCS, Azure Blob). Data syncs automatically and can be joined with your product analytics data using the built-in data warehouse.
• Transformations: Clean and transform event data in real-time before it's stored – including GeoIP enrichment, PII scrubbing, bot filtering, property standardization, and custom transformations written in Hog. Transformations are completely free with no volume limits.
• Destinations: Send data to dozens of tools in real-time or schedule batch exports to warehouses like Snowflake, BigQuery, Redshift, Postgres, and Databricks.

Beyond data pipelines, PostHog's CDP also handles event capture from client-side SDKs and server-side APIs and, unlike standalone CDPs, all that data is immediately available for analysis – no export required. Every event, user, and session can be queried with SQL, visualized in funnels and retention charts, or connected to a specific session recording. You can build cohorts based on behavior, run experiments on segments, and see exactly what users did before churning – all in one place.

PostHog uses simple usage-based pricing with a generous free tier, so you can start collecting and analyzing data before committing to a long-term contract.

Strengths:

• CDP and analytics in one platform
• Event-based insights, funnels, and retention built in
• SQL access and raw data ownership
• Flexible destinations and transformations
• Strong developer tooling and APIs

Community:

• PostHog is fully open source under the MIT license, actively maintained on GitHub.
• The repository has 30k+ stars, 400+ contributors, and daily commits
• Development happens in public

2. Segment

Segment (now part of Twilio) is the original CDP and still the most widely adopted. It popularized the idea of a single API for customer data collection, and its tracking spec has become a de facto standard.

Segment's core strength is data routing: install one SDK, define your events, and Segment handles sending that data to 700+ destinations – analytics tools, ad platforms, CRMs, warehouses, and more. This dramatically simplifies instrumentation and makes it easy to add or remove tools without rewriting tracking code.

On the analytics side, the platform now includes Unify for identity resolution and profile building, Twilio Engage for audience activation and journey orchestration, and native integrations with analytics tools like Amplitude and Mixpanel. That being said, Segment still doesn't include built-in product analytics, funnels, or session replay – you'll need to pair it with another tool for that.

Pricing is based on Monthly Tracked Users (MTUs). There's a free tier with 1,000 MTUs and 500K/mo Reverse ETL Records, a Team plan starting at $120/month for 10,000 MTUs and 1M/mo Reverse ETL Records, and custom Business pricing for larger deployments. CDP features (Unify, Engage) require custom pricing.

Strengths:

• Industry-standard tracking spec with excellent documentation
• 700+ pre-built integrations
• Strong identity resolution with Unify
• Reliable, battle-tested infrastructure at scale
• Deep Twilio ecosystem integration (SMS, email, voice)

Community:

• SDKs are open source on GitHub
• Large ecosystem of agencies and consultants
• Active community forum

3. RudderStack

RudderStack is an open-source CDP built specifically for developers and data teams. It positions itself as a warehouse-native alternative to Segment, designed for teams that want their data warehouse (Snowflake, BigQuery, Redshift) to be the source of truth – not a vendor's platform.

RudderStack's architecture is different from traditional CDPs: instead of storing customer data in its own database, it routes data directly to your warehouse and lets you build identity resolution, audiences, and activation on top of your existing infrastructure. This means no data duplication, better governance, and lower vendor lock-in.

The platform includes event streaming (similar to Segment), transformations you can write in JavaScript or Python, identity stitching via Profiles, and reverse ETL to sync warehouse data back to tools. There's also a visual audience builder, though it's more limited than enterprise CDP offerings.

RudderStack offers a free tier for up to 250,000 events/month, with paid plans starting at $220/month for the Starter tier with 1M events/mo. Enterprise pricing is custom.

Strengths:

• Warehouse-native architecture – your data stays in your warehouse
• Developer-friendly with Git-based workflows and custom transformations
• Strong Segment compatibility (forked tracking specs)
• 200+ integrations including warehouses, streaming platforms, and tools
• Open source and self-hostable

Community:

• Open source on GitHub
• 4.3k+ stars, 100+ contributors
• Active community Slack

4. mParticle

mParticle is an enterprise CDP focused on real-time data orchestration, particularly for mobile-first companies. It's used by brands like HBO Max, JetBlue, and Marks & Spencer to unify customer data across apps, web, and backend systems.

mParticle's core differentiator is its real-time processing engine: data flows through in milliseconds, enabling immediate personalization, suppression, and audience activation. The platform also has strong mobile SDK support, which makes it popular with app-heavy businesses.

On the analytics side, mParticle includes built-in data quality monitoring, identity analytics, and an AI-powered audience builder that can create predictive segments (likelihood to purchase, churn risk). However, like Segment, it doesn't include product analytics – you'll typically pair it with a standalone analytics or BI tool.

mParticle uses credit-based pricing aimed at enterprise teams. Customers commit to an upfront pool of credits, which are then consumed based on usage of each feature. All features are included by default, with no per-feature tiers or hard caps. Rates vary based on the size of commitment, meaning costs scale with data volume but require working with sales to estimate and manage spend.

Strengths:

• Real-time data processing with sub-second latency
• Strong mobile SDK support
• AI-powered predictive audiences
• Robust data quality and governance tools
• Hundreds of integrations including major ad and marketing platforms

Community:

• Closed-source platform
• Active partner ecosystem
• Regular product updates via their blog

5. Hightouch

Hightouch pioneered the "composable CDP" – a fundamentally different architecture where your data warehouse is the CDP. Instead of collecting and storing data in a separate platform, Hightouch sits on top of Snowflake, BigQuery, Databricks, or Redshift and activates the data that's already there.

This approach has major advantages: no data duplication, no new data silos, full SQL access, and you can leverage all the modeling and transformation work your data team has already done. Hightouch syncs audiences, user attributes, and computed fields from your warehouse to 250+ destinations in real-time or on a schedule.

Hightouch has expanded beyond reverse ETL into a full composable CDP with Customer Studio (no-code audience builder), identity resolution, journey orchestration, and AI-powered decisioning. The platform is designed to give marketers self-service access to warehouse data without requiring SQL knowledge.

The free tier includes basic reverse ETL functionality. Paid plans (Composable CDP, AI Decisioning) are custom-priced based on usage.

Strengths:

• Warehouse-native – no data duplication or vendor lock-in
• Leverages existing data infrastructure and models
• No-code audience builder for non-technical users
• 250+ integrations
• Fast implementation (days/weeks, not months)

Community:

• Closed-source platform
• Backed by Snowflake and Databricks (strategic investors)

6. Tealium

Tealium is an enterprise CDP that grew out of tag management – and that heritage shows in its comprehensive data collection and integration capabilities.

Their Customer Data Hub consists of several products that work together delivering client-side data collection, server-side API data, identity resolution and audience activation, and machine learning-powered insights like churn prediction and purchase propensity.

The platform's standout feature is its integration library: 1,300+ pre-built connectors to marketing, analytics, and data tools. Tealium also has strong real-time capabilities with patented "visitor stitching" technology for identity resolution, and robust privacy/compliance features (HIPAA, ISO 27001, SOC 2).

Tealium doesn't include built-in product analytics, funnels, or session replay – you'll pair it with external analytics tools.

Pricing is based on events collected and is custom-quoted.

Strengths:

• 1,300+ pre-built integrations – largest in the CDP market
• Strong tag management heritage with both client-side and server-side collection
• Real-time identity resolution with patented visitor stitching
• Robust privacy and compliance certifications (HIPAA, ISO 27001, SOC 2)
• ML-powered predictive insights with Predict ML add-on
• CloudStream for warehouse-native activation without data duplication

Community:

• Closed-source platform
• Strong partner and consultant ecosystem

Which CDP should you choose?

• Want a CDP with built-in analytics features? PostHog.
• Want an industry-standard data layer with 700+ integrations? Segment.
• Want an open-source, warehouse-native alternative to Segment? RudderStack.
• Need real-time data orchestration for a mobile-first app? mParticle.
• Already have a data warehouse and want to activate it without building a new silo? Hightouch.
• Need the largest integration library and enterprise-grade compliance? Tealium.

Recommendations by team type

For startups and small teams

• PostHog if you want everything in one place – analytics, replay, flags, error tracking, and data pipelines – without managing multiple vendors
• Segment (free tier) if you're building a data layer and want maximum flexibility in tool choice

For developer-first teams

• PostHog for teams that SDKs in all their favorite languages, customizable destinations and transformations, and docs that make all this easy to self-serve
• RudderStack for teams that want a warehouse-native CDP with full control over transformations and routing

For teams with their own warehouses

• Segment if you have budget and want a mature, well-supported platform with broad integrations
• Hightouch if your data team has already invested in a warehouse and wants to activate that data

For enterprises

• Tealium for complex martech stacks, 1,300+ integrations, and strong compliance (HIPAA, ISO 27001)
• mParticle for real-time orchestration, strong governance, and enterprise support
• Segment (Business tier) for teams already in the Twilio ecosystem
• PostHog if you want an all-in-one platform with strong governance and raw data access

For warehouse-first teams

• Hightouch if you want to activate existing warehouse data without building a new silo
• RudderStack if you also need event collection and want open-source infrastructure

Is PostHog right for you?

Here's the (short) sales pitch.

We're biased, obviously, but PostHog is the best choice for a CDP if:

• You want data pipelines connected to analytics, session replay, feature flags, and more – all in one platform instead of stitching tools together
• You value open source and transparent pricing
• You want to try before you buy with 1 million events and 10K dispatches free every month

It's completely free to get started – no credit card required. Our setup wizard handles configuration in minutes, or you can check out our docs to do it yourself.

Frequently asked questions

A good error tracking tool will give you context, not just stack traces: it'll show you what the user did right before the exception, which release it came from, and what else broke as a result.

This guide compares the most popular error tracking tools for developers right now, what features they do and don't offer, and who they're built for, so you can decide which one is right for your needs.

What features do you need in your error tracking tool?

At a minimum, most good error tracking tools will offer things like:

• Real-time error capture and grouping
• Full stack traces, source maps, and contextual metadata
• Alerts and assignment workflows for triage
• SDKs for major languages and frameworks
• Integration with CI/CD, logs, and performance data

The absolute best error tools tend to go a bit further by including things like:

• Integration with session replay: Useful for linking session recordings to real user sessions, so you can better understand the context of when an error occurs and debug the steps.

• Integration with product analytics: Useful so you can correlate the impact of errors on product usage, conversion, and revenue.

• Very broad SDK and framework support: Support for the likes of Next.js, Python, and React are typical among all the tools in this list, but some tools go deeper by offering SDKs for less popular frameworks, and even video game engines like Unreal Engine and Unity.

Here's how some of the most popular error tracking tools compare at a glance:

What's the best error tracking tool for developers?

1. PostHog

PostHog is an all-in-one developer platform that goes beyond error tracking and stack traces by combining error tracking, session replay, analytics, and feature flags into one platform.

Each exception can be linked to the related session replay, user, and feature flag version. You can also view the user's actions, console output, and API calls leading up to the issue, then ship a fix behind a feature flag, test it, and measure the impact in analytics without ever having to switch between tools.

PostHog also supports autocapture of unhandled exceptions, filter by error type, and set event-based alerts that trigger when specific issues occur, and offers built in LLM analytics for teams that need to observe and optimize AI products as well.

Strengths:

• Replay-linked debugging
• Full user and session context
• Transparent usage-based pricing with configurable caps
• Unified suite: analytics, feature flags, surveys, experiments, LLM analytics and more

Community:

• PostHog is fully open source under the MIT license and actively maintained at https://github.com/PostHog/posthog.

• The repository has 29.8k+ stars, 360+ contributors, and sees daily commits from both the core team and community.

• Most development happens in public, including product discussions and roadmap planning.

2. Sentry

Sentry is a mature, battle-tested error and performance monitoring tool used across industries. It's stable, deeply integrated, and built for teams who value visibility over novelty.

Sentry has earned its reputation by being the tool developers turn to when uptime and reliability are non-negotiable. It provides rich grouping, detailed stack traces, breadcrumbs, release tracking, and performance monitoring across frontend, backend, and mobile SDKs.

It's highly scalable, with strong alerting and triage workflows.

Strengths

• Wide SDK coverage and proven reliability
• Performance tracing to identify slow transactions
• Self-host option available
• Mature ecosystem and integrations

Community

• Sentry is licensed under the Functional Source License (FSL), which they describe as eventually open source (it restricts certain commercial and competitive uses until it later converts to a permissive open-source license)

• It's one of the most-starred monitoring tools, with ~42k stars and 800+ contributors on GitHub.

• The team is highly active with weekly releases and there's a large selection of community supported SDKs as well.

3. Rollbar

Rollbar connects errors directly to deploys, releases, and regressions. It's built for teams who deploy constantly and need to know the moment something breaks in production.

Their superpower is speed. Rollbar specializes in tracking when new releases cause errors, and integrates with GitHub, GitLab, Jira, and Slack, automatically associating new exceptions with recent deployments.

If you value velocity and automation over depth, this is a tool that can help you ship multiple times a day without fear.

Strengths

• Release tracking and rollback detection
• Strong CI/CD and issue tracker integrations
• Automations for regression alerts and triage

Community

• Rollbar maintains several open SDK repositories across languages, including rollbar.js, rollbar-python, and rollbar-java.

• Each repo has hundreds of stars and regular maintenance, though the core product itself is proprietary.

4. Bugsnag

Bugsnag is a leading tool for mobile and frontend error monitoring focused on app stability metrics.

It was built around the insight that not every error is made equal; it measures crash-free sessions, calculates stability scores, and surfaces the most impactful issues first so teams can prioritize issues that have the greatest impact on real-world experience.

Their clean dashboards and SDK coverage across iOS, Unity, React Native, Android, and web make it a go-to for app developers.

Strengths

• Excellent mobile SDKs and coverage
• Stability and health metrics help prioritize fixes
• Integrates with common mobile CI/CD pipelines

Community

• Bugsnag's core platform is closed source, but it maintains open SDKs for most major platforms (JavaScript, Android, Unity, and others).

• Each repo has hundreds to a few thousand stars, and updates are frequent.

5. GlitchTip

GlitchTip is an open-source, privacy-friendly alternative to Sentry. It's a lightweight, predictable error tracker that's yours to run however you want.

Its Sentry API compatibility means you can often switch without changing SDKs. GlitchTip's simplicity is also its strength: no over-engineered dashboards, no surprise upgrades, no opaque billing – just a clean UI, grouped errors, and self-hosted reliability for small teams.

It's a compelling choice for smaller engineering teams or privacy-sensitive organizations.

Strengths

• Easy deployment with Docker
• Free and privacy-friendly
• Maintains Sentry protocol compatibility

Community

• GlitchTip is fully open source under the MIT license, with active contributions from the developer community.

• It's a lightweight alternative to Sentry with regular maintenance and transparent development.

6. SigNoz

SigNoz is an open-source observability platform built on OpenTelemetry.

It collects metrics, traces, and errors into one open-source platform designed to replace proprietary APM tools like Datadog or New Relic. It's self-hostable, cost-effective, and ideal for teams standardizing on open telemetry stacks.

Strengths

• Unified tracing, metrics, and error monitoring
• OpenTelemetry-native and vendor-neutral
• No licensing lock-in

Community

• SigNoz is open source under the Apache 2.0 license, with 24k+ stars and a rapidly growing contributor base.

• It's one of the most active OpenTelemetry-native observability projects and ships frequent updates.

Which error tracking tool should you choose?

• Want an all-in-one platform that connects errors to user sessions, analytics, feature flags, experiments, and more? Go with PostHog.
• Need deep stack traces, transaction tracing, and mature triage workflows? Sentry is the proven choice.
• Shipping constantly and need instant visibility into which release broke what? Choose Rollbar.
• Focused on mobile or frontend stability metrics like crash-free sessions? Bugsnag is a good fit.
• Want a simple, self-hosted option compatible with Sentry clients? Try GlitchTip.
• Prefer an open-source observability stack built on OpenTelemetry? SigNoz is probably the answer.

Is PostHog right for you?

Here's the (short) sales pitch.

We're biased, obviously, but PostHog is the best choice for error tracking if:

• You want errors connected to session replays, feature flags, and analytics so you can see not just what broke, but who was affected and what they were doing
• You value open source and transparent pricing
• You want to try before you buy with 100k exceptions free every month

It's completely free to get started – no credit card required. Our setup wizard handles configuration in minutes, or you can check out our docs to do it yourself.

FAQ

Maybe an edge case breaks on mobile, maybe the feature tanks conversion for a segment you didn't test, or maybe your new onboarding flow confuses more people than it helps.

This is where feature flags come in: you ship the code when it's ready, and you control who sees it separately.

This guide compares the best feature flag tools for developers – what they do well, where they fall short, and which one fits your workflow best.

What features do you need in your feature flag tool?

At its core, a feature flag tool lets you toggle features on or off for specific users, groups, or percentages of traffic without redeploying code. But the best tools are a lot more than flip switches.

Most solid feature flag tools include:

• Boolean and multivariate flag types
• Percentage-based and targeted rollouts
• Environment management (dev, staging, production)
• SDKs for major languages and frameworks
• A dashboard for everyone to manage flags

Developer-focused tools often go further with:

• Local evaluation so flag checks don't add latency or depend on network calls
• Built-in experimentation to measure the impact of a flag, not just toggle it
• Analytics integration so you can trace a conversion drop back to a specific flag variant
• Stale flag detection to prevent feature flags from becoming permanent technical debt
• Audit logs and approval workflows for governance in larger teams

What's the best feature flag tool for developers?

1. PostHog

PostHog is an all-in-one developer platform where feature flags aren't a standalone product – they're woven into a broader workflow that includes product analytics, session replay, experiments, error tracking, LLM analytics, logs, and more.

This means you can roll out a feature to 10% of users, watch session replays of those users, check whether the flag variant increased conversion, catch any errors it triggered, and get feedback about their experience – all without leaving the platform.

PostHog supports boolean and multivariate flags, percentage rollouts, user and group targeting, JSON payloads for remote configuration, and local evaluation for low-latency flag checks. It also powers PostHog's experimentation features, so you can go from "ship it behind a flag" to "measure its impact with statistical significance" in a few clicks.

PostHog uses usage-based pricing with a free tier of 1 million feature flag requests per month – enough for most early-stage teams. You can also set billing caps to prevent surprise charges, and eligible startups can get $50k in free credits through the PostHog for Startups program.

Strengths:

• Feature flags with full analytics, replay, and error context
• Boolean, multivariate, and JSON payload flags
• Local evaluation for low-latency server-side checks
• Usage-based pricing with generous free tier and billing caps
• Unified suite: analytics, experiments, error tracking, session replay, surveys, LLM analytics, and more

2. LaunchDarkly

LaunchDarkly is the most established feature flag platform and the go-to choice for many organizations. It was built specifically for feature management at scale, and it shows in the depth of its targeting rules, governance features, and SDK support.

LaunchDarkly supports complex boolean logic, multi-dimensional targeting (users, devices, organizations), progressive rollouts, approval workflows, and audit logging. It features a broad SDK coverage, and the streaming architecture delivers flag updates in near real-time without polling.

More recently, it has expanded into experimentation, release monitoring, and AI model configuration. It also offers "guarded releases" that automatically roll back changes when performance metrics degrade.

The trade-off is cost. Their pricing is based on client-side MAUs, and can scale quickly. It also doesn't include session replay or error tracking, so you'll need separate tools for those.

Strengths:

• Sophisticated targeting and governance features
• Broad SDK coverage across web, mobile, backend, and edge
• Streaming architecture for near real-time flag updates
• Guarded releases with automated rollback
• Strong enterprise compliance (SOC 2, HIPAA, FedRAMP)

3. GrowthBook

GrowthBook is an open-source platform built around the idea that every feature release should be measurable. It combines feature flags with a powerful experimentation engine that connects directly to your data warehouse, making it a strong pick for data-savvy teams.

Its feature flag capabilities include targeting rules, percentage rollouts, prerequisite flags, and JSON payloads. You can also turn any flag into an A/B test, define metrics via SQL, and analyze results using Bayesian or Frequentist statistical engines without exporting data or wiring up a separate tool.

GrowthBook is free to self-host; the cloud version has a free tier for up to 3 users, with paid plans starting at $40/user/month for features like multi-armed bandits, product analytics, access control, and more.

Strengths:

• Open source and self-hostable with no usage limits
• Warehouse-native experimentation engine
• Strong statistical analysis (Bayesian, Frequentist, CUPED)
• Lightweight SDK
• Transparent SQL-based metric definitions

4. Unleash

Unleash is an open-source feature flag platform designed for teams that need full control over their infrastructure. It’s used in production by large orgs, including Visa, where data sovereignty and compliance aren't optional.

Unleash focuses on doing feature flags well without trying to be an analytics or experimentation platform. It supports progressive rollouts, kill switches, custom activation strategies, and environment management. Under the hood, it's a Node.js and TypeScript backend that uses PostgreSQL for storage, and it's straightforward to deploy via Docker or Kubernetes.

Their OSS is positioned as a single-environment setup, and teams that need multiple environments with governance features typically upgrade to Enterprise for things like RBAC, audit logs, and change requests, plus SSO and SCIM.

Strengths:

• Open source and fully self-hostable
• Simple, reliable architecture (Node.js + PostgreSQL)
• Strong compliance and governance features on paid plans
• Broad SDK coverage including community-maintained options
• No vendor lock-in – your data stays in your infrastructure

5. Flagsmith

Flagsmith is an open-source feature flag and remote configuration platform that gives teams flexibility in how they deploy. You can run it as SaaS, on a private cloud, or fully on-premises – a rare combination that makes it a strong fit for security-conscious organizations.

It supports boolean and multivariate flags, user segments, percentage rollouts, A/B testing, remote config, and scheduled flags. Its Edge API routes requests to the nearest data center for low-latency evaluation, and it supports local evaluation for server-side SDKs.

One area where Flagsmith differs from platforms like PostHog is that it isn't trying to be a full product analytics suite.

It includes testing features, but if you want deep behavioral analysis, funnels, retention, or replay-led debugging, you'll typically pair it with your existing analytics stack. Their free tier includes 50,000 requests per month, and paid plans start at $45/month.

Strengths:

• Open source with flexible deployment options (SaaS, private cloud, on-prem)
• Remote configuration alongside feature flags
• Edge API for low-latency global flag delivery
• Clean, intuitive management interface
• Strong compliance posture for regulated industries

6. Statsig

Statsig was built by ex-Facebook engineers who wanted to bring Meta's internal experimentation infrastructure to every team. The result is a platform that combines feature flags, experimentation, analytics, and session replay in one unified system.

Statsig's standout is its experimentation engine. Every feature flag can be turned into an experiment with automatic metric tracking, CUPED variance reduction, and sequential testing. The platform also includes product analytics, funnels, and session replay, giving teams a full picture of how flags impact user behavior.

It markets unlimited flag and config checks on every tier, and the free tier includes 2M events per month plus session replay volume. In practice, that means you can run a lot of flags without paying for every flag check, costs scale mainly with how much you measure and replay.

Strengths:

• Feature flags are free and unlimited at every tier
• Advanced experimentation with CUPED and sequential testing
• Integrated analytics and session replay
• Warehouse-native deployment option for data control
• 30+ SDKs with edge computing support

7. DevCycle

DevCycle is a feature flag platform that prioritizes speed, simplicity, and tight integration with existing development workflows. It's designed for teams that want to manage flags the same way they manage code – with Git-based workflows, CLI tools, and IDE integrations.

DevCycle supports boolean and multivariate flags, percentage rollouts, user targeting, and environment management. It integrates with GitHub and Jira for flag lifecycle management, and includes built-in stale flag detection to help teams clean up technical debt.

What sets DevCycle apart is its obsession with developer workflow. It supports CLI and IDE-first ways to work with flags, plus integrations with tools like GitHub and Jira so flags show up naturally in code review and planning. And their edge and local-bucketing style options help keep evaluation fast without you managing a full flag platform yourself.

DevCycle offers a free tier for up to 1,000 MAUs, with paid plans based on MAU volume.

Strengths:

• Developer-first workflows with CLI and IDE integrations
• Git-based flag management
• Built-in stale flag detection and cleanup tools
• Edge-based evaluation for low latency
• Fast setup and lightweight SDK

Which feature flag tool should you choose?

• Want to ship behind a flag, measure the impact, and catch errors without switching tools? PostHog
• Need enterprise-grade governance, compliance, and the widest SDK coverage? LaunchDarkly
• Want open-source flags with a warehouse-native experimentation engine? GrowthBook
• Need self-hosted flags with full data sovereignty for regulated industries? Unleash
• Want deployment flexibility with remote config and an open-source core? Flagsmith
• Need advanced experimentation with free unlimited flags? Statsig
• Prefer a lightweight, developer-first tool with Git-based workflows? DevCycle

Recommendations by team type

For high-growth startups

• PostHog for transparent pricing, a generous free tier, and a single platform that replaces multiple SaaS tools
• GrowthBook if your team wants open-source experimentation connected to your warehouse

For enterprise and large engineering teams

• LaunchDarkly for the most sophisticated targeting, governance, and compliance features
• Unleash if you need self-hosted flags with enterprise-grade audit trails and RBAC
• Statsig for high-velocity experimentation at scale without paying per flag check

For developers and engineering-heavy teams

• PostHog if you want flags tightly integrated with analytics, replay, and error tracking
• DevCycle if you want something lightweight that fits into Git and CI/CD workflows
• GrowthBook if you want to self-host and measure every release with experiments

For teams in regulated industries

• Unleash for full self-hosting, data sovereignty, and compliance certifications
• Flagsmith for flexible deployment options (SaaS, private cloud, on-prem) with strong governance
• LaunchDarkly if you need SOC 2, HIPAA, or FedRAMP compliance from a managed platform

Is PostHog right for you?

Here's the (short) sales pitch.

We're biased, obviously, but PostHog is the best choice for feature flags if:

• You want flags connected to your entire suite of data tools, so you can roll out features safely and see exactly how they impact user behavior
• You value open source and transparent pricing
• You want to try before you buy with 1 million feature flag requests free every month

It's completely free to get started – no credit card required. Our setup wizard handles configuration in minutes, or you can check out our docs to do it yourself.

Frequently asked questions

This guide covers the seven best mobile session replay tools: what they do better, which frameworks they cover (iOS, Android, Flutter, React Native...), where they fall short, and who they're actually for.

1. PostHog

What is PostHog?

PostHog (that's us 👋) is an all-in-one developer platform built to enable engineers to create better products. It includes session replays and a whole bunch more, such as product analytics, LLM analytics, error tracking, experiments, and feature flags.

It's designed for product-minded engineers, founders, growth teams, and product managers who need to move fast and iterate based on reliable, actionable insights.

Supported features

How much does PostHog cost?

PostHog has transparent pricing based on usage. It's free to get started and completely free for the first 2,500 mobile recordings and 5,000 web ones per month.

After this, pricing starts at $0.0100 per mobile recording, and recordings cost progressively less the more you use. This makes PostHog significantly cheaper than all the other companies on this list (apart from Microsoft Clarity).

Why do companies use PostHog?

According to G2 reviews, companies use PostHog because:

1. It's many tools in one: PostHog can replace tools like Mixpanel (analytics), UXCam (session replay), and Statsig (A/B testing and feature flags). This simplifies workflows and ensures all product data is in one place.

2. They need a complete picture of users: PostHog includes every tool necessary to understand users and build better products. This means creating funnels to track conversion, watching replays to see where users get stuck, and testing solutions with A/B tests.

3. Pricing is transparent and scalable: Reviewers appreciate how PostHog's pricing scales as they grow. There's a generous free tier. Companies eligible for PostHog for Startups also get $50k in additional free credits.

Bottom line

PostHog is the best choice for teams who want mobile session replay connected to a complete suite of developer products – LLM analytics if you're building AI products, feature flags, error tracking, experiments, surveys, and more. It is especially worth it for startups and scaleups thanks to its generous free tier and powerful offerings.

2. LogRocket

What is LogRocket?

LogRocket is a product experience platform that focuses on helping product managers, engineers, and support teams fix issues. In addition to session replay, it provides tools to identify the most impactful issues, such as crash reports, stack traces, performance monitoring, and product analytics.

Supported features

How much does LogRocket cost?

LogRocket's free tier includes 1,000 sessions/month with 1 month data retention.

Its Team plan starts at $69/month for up to 10k sessions and includes most session replay features, but excludes other features such as product analytics, Galileo AI, and some error tracking features.

The Professional plan starts at $295/month and includes all their features, but requires an annual commitment.

Why do companies use LogRocket?

Reviewers on G2 use LogRocket for:

1. Identifying problems: LogRocket's combination of error tracking, performance monitoring, and session replay makes it uniquely powerful at finding bugs and issues.

2. Improves user experience: LogRocket helps reviews fix issues with their apps. Reviewers find it provides all the tools to improve their user experience, mostly by solving what's wrong rather than feedback and planning.

3. High usability: LogRocket provides a lot of functionality out of the box. It captures the details users need, provides useful visualizations, and automatically triages some issues.

Bottom line

LogRocket is the best choice for teams building React Native apps who need Redux state capture alongside session replay. Galileo AI also sets it apart by proactively surfacing struggle patterns across sessions, so you're not waiting to discover problems.

3. UXCam

What is UXCam?

UXCam is a platform built primarily for mobile apps (although they've recently added web support). It includes a full-suite of analytics features as well as session replays, heatmaps, crash analytics, and an integrated AI analyst.

It's designed to provide deep insights into user behavior, helping product managers understand how users interact with their apps.

Supported features

How much does UXCam cost?

UXCam's free plan includes 3,000 sessions/month with 6 months of replay retention. Paid plans require a sales conversation – no public pricing is available.

Why do companies use UXCam?

Looking at G2 reviews, companies choose UXCam to:

1. Insights on user behavior: Shows what they are doing, where their attention is going, and how they are spending their time. Users love the realizations they get with UXCam's tools.

2. It's easy to use: Reviewers note how easy UXCam is to set up and use. This means they can go from idea to actionable learnings quickly.

3. Heatmaps and scrollmaps: Seeing clicks and scroll depth works well and creates actionable insights for design and product teams. Helps improve UX and page designs.

Bottom line

UXCam is ideal for product managers who need in-depth insights into user behavior. Despite opaque pricing, its ease of use and actionable insights make it a valuable tool.

4. Microsoft Clarity

What is Microsoft Clarity?

Microsoft Clarity is a 100% free session replay tool that supports most replay features and heatmaps. It integrates with Google Analytics 4, so you can view your GA data in Clarity.

It includes most of the session replay features you would expect, but doesn't support any additional features that some of the other choices on this list do, like crash reports or debug logs.

Supported features

How much does Microsoft Clarity cost?

Clarity is completely free – no session limits, no paid tiers, ever.

The trade-off is worth understanding before you adopt it: Clarity is free because Microsoft uses the anonymized behavioral data it collects to improve its own machine learning models and products.

Why do companies use Microsoft Clarity?

According to reviews on G2 and Capterra, it's because:

1. It's easy to set up and use: Setting up Clarity doesn't require much technical knowledge and users mostly like the simple user interface.

2. It helps uncover how users navigate: Clarity users are mostly using it to understand how people navigate their app and identify opportunities for improvement.

3. It's free: The short 30-day retention limit is easy to forgive when it's free.

Bottom line

If you only care about session replay and dont need other features, Clarity is the obvious choice - especially if you're already using Google Analytics. On the other hand, software engineers will find Clarity's lack of debug logs disappointing.

5. Smartlook

What is Smartlook?

Smartlook combines session replays, product analytics, and crash reports to help teams understand user experience across both web and mobile.

It supports the broadest range of mobile SDKs on this list, making it one of the few tools that works for game developers as well as traditional app teams.

Wireframe mode is a standout feature for privacy-sensitive apps, replacing UI elements with abstract representations so you can analyze behavior without capturing sensitive content.

Supported features

How much does Smartlook cost?

Smartlook's free plan records up to 3k sessions and includes most of their replay features. Its pro plan includes all replay features and starts at $55/month for 5k sessions, and pricing becomes progressively cheaper with more monthly sessions.

Why do companies use Smartlook?

According to G2 reviewers, Smartlook users benefit from:

1. The integration between replays and events: Smartlook connects event-based analytics and sessions. This enables users to dive deeper into user behavior than a single tool provides.

2. Understanding visitor pain points: The analytics and visualizations make it easy to understand where users are running into trouble. Reviews use this to improve the user experience and conversion in these areas.

3. Real user monitoring: Smartlook shows how real users are using your app and monitors the quality of their experiences. Reviewers use it to figure out what areas are confusing or used improperly.

Bottom line

Smartlook's broad SDK support makes it the best choice for game developers and teams building on less common frameworks like Unity, Unreal Engine, or Xamarin. For standard iOS/Android teams, the retroactive event tracking and cross-platform journey tracking are the standout reasons to pick it over alternatives.

6. FullStory

What is FullStory?

FullStory describes itself as behavioral data analytics platform, which is code for session replay and mobile app analytics, with a side of product analytics.

It uses autocapture to record everything retroactively, and StoryAI to generate session summaries and surface behavioral opportunities automatically.

Supported features

How much does FullStory cost?

FullStory's free plan (FullStoryFree) includes 30,000 sessions/month with 12 months of data retention (but limited features). Paid plans aren't published publicly and require contacting sales

Why do companies use FullStory?

According to reviews on G2, companies use FullStory for:

1. Easier collaboration: As an accessible tool for non-technical users, FullStory facilitates collaboration between product, UX, and engineering teams by allowing all teams to access useful, reliable data.

2. Viewing user issues: Support teams use FullStory to replay sessions to understand user hard-to-replicate problems, and identify bugs that need fixing.

3. Improving conversion: FullStory users like to combine funnel insights with replays of user sessions to understand pain points and improve conversion.

Bottom line

For a complete behavioral analytics suite, FullStory is a solid choice. Users wanting more features, like error tracking or A/B testing should switch to other choices.

7. Contentsquare

What is Contentsquare?

Contentsquare is a digital experience analytics platform that combines heatmaps, customer journey analysis, frustration scoring, session replay, and AI-powered insights to help find and fix issues and friction points.

It acquired Hotjar in 2021 and Heap in 2023, making it one of the largest DX platforms in the market. It's built for large organizations that need to understand and optimize user behavior at scale across web and mobile.

Supported features

How much does Contentsquare cost?

Contentsquare offers a free plan that includes 200,000 monthly sessions, replays, heatmaps, funnels, error monitoring, and surveys. Only 5% of sessions are recorded on their free plan though (a max of 10k per month) with 1 month retention and limited features (like filters).

The Growth plan starts from 7,000 monthly sessions with 13 months of data access and adds zone-based heatmaps, journey analysis, impact quantification, and Sense AI.

Higher tiers (Pro, Enterprise) are available with custom pricing via sales.

Worth noting: product analytics is a separate add-on, not included in the core plans.

Why do companies use Contentsquare?

According to G2 reviews, people like Contentsquare because:

1. Data visualization and analysis: Users like that Contentsquare makes it easy to visualize and analyze large data sets. Advanced features make it easier to identify and fix problems big and small.

2. Improve your UI and UX: Contentsquare makes it easy to understand the impact of various design elements on a screen. This helps developers understand the impact of elements on conversion rate, revenue, and user journeys.

3. Detailed session replays: Users appreciate being able to match quantitative data with qualitative data, giving them a better understanding of how best to solve problems.

Bottom line

Contentsquare is purpose-built for large enterprises with high traffic volumes and dedicated analytics teams. For most teams, the cost and complexity are hard to justify – PostHog, Smartlook, or LogRocket offer most of the same capabilities at a fraction of the price.

Which mobile session replay tool should you choose?

• Want replay connected to product analytics, feature flags, error tracking, and more in one platform? PostHog.
• Need powerful debugging tools – console logs, network monitoring, Redux state capture? LogRocket.
• Building a mobile app and need retention, cohort analysis, and crash analytics alongside replay? UXCam.
• Want something completely free with no session limits? Microsoft Clarity.
• Need replay across web and mobile with retroactive event tracking – or building on Unity, Unreal Engine, or Xamarin? Smartlook.
• Want enterprise-grade replay with a generous free tier to evaluate? FullStory.
• Large enterprise needing zone-based heatmaps and impact quantification? Contentsquare.

Is PostHog right for you?

Here's the (short) sales pitch.

We're biased, obviously, but PostHog is the best choice for mobile session replay if:

• You want replay connected to analytics, feature flags, error tracking, and more in one platform
• You value open source and transparent pricing
• You want to try before you buy – 2,500 mobile replays free every month, no credit card required

It's completely free to get started – no credit card required. Our setup wizard handles configuration in minutes, or you can check out our docs to do it yourself.

Frequently asked questions

Free and open source business intelligence tools solve both these problems by making BI accessible and keeping you in control of your data.

The challenge becomes choosing the best one. To help you, we've put together this list of the top free and open source business intelligence tools you can use.

What are the best free and open source BI tools right now?

1. PostHog

• License: MIT
• GitHub stars: 32.1k as of March 2026
• PostHog on GitHub

PostHog is an all-in-one developer platform that combines business intelligence with several other developer-focused tools, such as product and web analytics, session replay, experiments, error tracking, LLM analytics, surveys, and more.

Its business intelligence stack contains three key pieces:

1. A data warehouse that stores data captured into PostHog and syncs with external sources you already use, like Stripe, Hubspot, Postgres, Google Sheets, S3, Snowflake, and more.

2. Product analytics which provides a simple but powerful interface for building dashboards and insights including trends, funnels, retention graphs, user paths, and more.

3. An SQL editor for direct access to your data along with its own set of visualizations and customization.

What makes PostHog special?

PostHog has the flexibility of a business intelligence tool, while including a full suite of tools product and engineering teams need to build successful products. You have less data spaghetti trying to connect them all together.

For example, not only can you analyze A/B tests from warehouse data in PostHog, but you can actually set up and run them natively using our experimentation and feature flag products. You don't need to set up four different products, just PostHog.

PostHog AI can also generate and fix SQL queries, answer questions about your data, and more. The MCP server enables your AI agents and tools to directly interact with PostHog's products.

PostHog comes with 1 million events and 1 million rows synced for free every month, and has transparent and usage-based pricing.

You can use the setup wizard to get started in minutes – no credit card required.

FAQ

2. Metabase

• License: AGPLv3
• GitHub stars: 46.5k as of March 2026
• Metabase on GitHub

Metabase is an open source analytics tool that easily connects to your existing database.

Once connected, Metabase offers an easy to use and powerful query builder. Visualizations can be built with or without SQL. Within these, users can drill down to filter, breakout, or view the source table.

Once built, Metabase then enables teams to build and organize with dashboards and collections. They also provide permissioning to ensure only the right people have access to sensitive insights.

What makes Metabase special?

Metabase is praised for its simplicity. It really is easy to connect to a data source and start querying fast. Its visualization builder is just as simple, with a clean UI that doesn't require a power user to figure out. This means non-analysts can self-serve their data needs.

FAQ

3. Superset

• License: Apache 2.0
• GitHub stars: 71k as of March 2026
• Superset on GitHub

Apache Superset is a modern, open source exploration and visualization platform. Superset formed out of a 2015 Airbnb hackathon project.

It provides both no-code and SQL options for queries and 40+ pre-installed visualizations. In terms of “modernity”, it's lightweight, scalable, and integrates with any SQL-based database, including cloud-native and petabyte-scale ones.

What makes Superset special?

Superset provides a few meta tools for making insight creation easier.

1. A semantic layer for quickly defining custom dimensions and metrics. These abstractions map SQL to be more human-friendly.
2. Datasets which are subsets of data that make chart creation easier and queries more performant. This aims to be a middle point between a query-centric approach and a semantic-centric one.

FAQ

4. Grafana

• License: AGPLv3
• GitHub stars: 72.7k as of March 2026
• Grafana on GitHub

Grafana is an open source observability platform that centralizes metrics, logs, traces, and profiles. It provides observability for big infrastructure providers, monitoring for popular services, and connectors for other tools.

Once all this data is captured or connected, Grafana provides over 100 data visualization options to build all sorts of dashboards. It also provides more tools for making use of this data like alerts, transformations, and annotations.

What makes Grafana special?

Grafana is one of the most mature tools on this list. It was founded in 2014, is valued at over $6B, has acquired companies like k6 and Amixr to add to its product suite, and has a massive library of data source and visualization plugins.

This means they are very popular with their core customer: infrastructure engineers. For these people, they provide a majority of tools they need like logging with Loki, tracing with Tempo, metrics with Mimir, and visualization with Grafana.

FAQ

5. Evidence

• License: MIT
• GitHub stars: 6k as of March 2026
• Evidence on GitHub

Evidence is a modern frontend for data work. It focuses on providing a modern design and dev experience for building polished and performant data products like articles and dashboards.

It has focused on ensuring its query engine provides high performance on huge queries, and its data products provide a high quality experience for both internal and external teams.

The cloud version also has SOC 2 certification, row-level security, and other features enterprises expect from tools like these.

What makes Evidence special?

In Evidence, everything is code. This means it combines SQL, markdown, and its templating language to provide a simple, but powerful development environment for their data products.

It also means they can do real-time syntax validation, version control, and testing. The cloud version adds AI tools to help users generate and edit this code based on natural language.

FAQ

6. Redash

• License: BSD-2
• GitHub stars: 28.3k as of March 2026
• Redash on GitHub

Redash is a tool that enables you to easily connect to your data sources, query them with SQL, and build dashboards for your team.

It supports a range of:

1. SQL, NoSQL, big data, and API data sources from Redshift to MySQL to Postgres.
2. Charts and visualizations from boxplots to maps to funnels.

Beyond this, Redash also provides alerts, an API to extend its functionality, SSO, and access controls.

What makes Redash special?

Redash is a narrower product than the other alternatives on this list. It focuses more on:

1. Providing a great query writing experience with autocomplete, keyboard shortcuts, snippets, and results caching.
2. Creating useful dashboards with their collection of visualizations and sharing tools.

Because of this simpler feature set, Redash can be simpler and lighter to self host.

FAQ

7. Lightdash

• License: MIT
• GitHub stars: 5.6k as of March 2026
• Lightdash on GitHub

Lightdash makes it easy to turn your dbt project into BI dashboards easily.

Once set up leveraging your existing dbt models, it makes creating visualizations easy. These don't require SQL and instead rely on their data catalog, pre-built metrics, and point and click explorer. It also includes an AI analyst co-pilot to help users self-serve their data needs.

Although reliant on dbt, Lightdash also provides integrations for warehouses, version control tools (like GitHub), alerting (Slack), and sharing. This helps it easily connect to your existing stack.

What makes Lightdash special?

Lightdash has connected itself deeply to dbt. It literally markets itself as “The BI platform built for dbt.” Like Evidence, this lets teams manage everything (like dashboards) as code.

Lightdash's developer focus doesn't stop there. They also provide version controls, staging, automated testing, and CI/CD toolings.

FAQ

Which open source BI tool should you choose?

• Need one BI platform for your whole product stack – analytics, session replay, feature flags, error tracking, and more included? PostHog.
• Need fast, accessible dashboards for non-technical users with a new semantic layer? Metabase.
• Building an enterprise data platform with a semantic layer and 40+ visualization types? Superset.
• Running infrastructure and need observability dashboards, alerting, and real-time monitoring? Grafana.
• Want to build polished data products with code-first workflows and version control? Evidence.
• Need a simple, lightweight self-hosted SQL + dashboard tool with minimal overhead? Redash.
• Already using dbt and want a BI layer that treats dashboards as code? Lightdash.

Is PostHog right for you?

Here's the (short) sales pitch.

We're biased, obviously, but we think you'll love PostHog if:

• You value transparency (we're open source and open core)
• You want an all in one platform, keeping your data in one place
• You want to try before you buy (we're self-serve with a generous free tier)

It's completely free to get started – no credit card required. Our setup wizard handles configuration in minutes, or you can check out our docs to do it yourself.

Frequently asked questions

LLM observability tools help them do this by capturing LLM provider requests and generations, then visualizing and aggregating them. This helps developers monitor, debug, and improve their apps.

To help you pick the best of these tools, we put together this list. All of the following products:

1. Integrate with popular LLM providers like OpenAI, Anthropic, and Vercel AI SDK to capture generations.
2. Let you view individual generations and traces from your app.
3. Calculate and display an aggregated metrics dashboard with cost, latency, and more.
4. Are open source and self-hostable.
5. Have a free hosted version (minus one of them…)

1. PostHog

• License: MIT
• GitHub stars: 32.1k as of March 2026
• PostHog on GitHub

PostHog is an all-in-one developer platform that combines LLM observability with several other developer-focused tools, such as product and web analytics, session replay, feature flags, experiments, error tracking, and surveys.

Its LLM observability product (known as LLM analytics) integrates with popular LLM providers, captures details of generations, provides an aggregated metrics dashboard, and more.

What makes PostHog special?

PostHog’s LLM analytics app works with the rest of our dev tool suite. This means you can visualize LLM-related data along product and business data, create custom queries using SQL, view session replays of AI interactions, A/B test prompts, and more.

Two features worth highlighting for teams iterating on LLM apps:

• Prompt management (beta): Create and version prompts directly in PostHog. Prompts are fetched at runtime via the SDK with caching and fallback support, so you can update them without code deploys. Non-engineers can iterate on prompts from the UI, and every change creates an immutable version you can compare, restore, or link to traces to see which prompt versions drive which outputs.

• Evaluations (beta): Score LLM outputs automatically or with human review to track quality over time – not just whether API calls succeed, but whether they're actually good.

PostHog’s hosted Cloud version and all of its LLM analytics features are free to use. It comes with 100k LLM observability events for free every month with 30 day retention. Beyond this, pricing is usage-based and totally transparent.

Use the setup wizard to get started in minutes – no sales call or elaborate configuration needed.

2. Langfuse

• License: MIT
• GitHub stars: 23.3k as of March 2026
• Langfuse on GitHub

Langfuse (recently acquired by ClickHouse) is an open source LLM engineering platform. It provides LLM call tracking and tracing, prompt management, evaluation, datasets, and more. These give LLM app developers tools they need for their entire workflow.

Langfuse can be self-hosted for free. If you prefer a managed service, Langfuse Cloud is free to use up to 50k events per month and 2 users, but this only includes 30 day data access. Pricing beyond this starts at $29/m for 100k events with additional events at $8/m more.

What makes Langfuse special?

Langfuse is one of the original tools in the LLM observability space. This means it has a wide range of tools for LLM app developers to use and have been instrumental in defining what they look like.

It also claims to be the most used open LLMOps platform. Beyond its early entry, this is thanks to its integrations with most LLM providers and agent frameworks, native SDKs for Python and JavaScript, and its ability to act as an OpenTelemetry backend

Langfuse is also the most fully-featured LLM observability tool. Its pricing page lists a huge 78 features from session tracking to batch exports to SOC2 compliance.

3. Opik

• License: Apache 2.0
• GitHub stars: 18.3k as of March 2026
• Opik on GitHub

Opik is an open source platform for evaluating, testing, and monitoring LLM apps. It provides tracing, annotations, a prompt and model playground, evaluation, and more. It’s built by Comet, an end-to-end model evaluation platform for developers.

Opik’s free hosted plan provides 25k spans per month with unlimited team members and a 60-day data retention. Beyond this, its Pro plan is $19 per month for 100k spans per month with every extra 100k spans costing $5.

What makes Opik special?

Thanks to Opik’s integration with Comet, it’s the only tool on this list that appeals to LLM developers, not just LLM app developers. This means it is ideal for teams training and hosting models of their own, not just using the LLM providers.

4. OpenLLMetry

• License: Apache 2.0
• GitHub stars: 6.9k as of March 2026
• OpenLLMetry on GitHub

OpenLLMetry is an open-source observability product for LLM applications based on OpenTelemetry. It was built by Traceloop and recommends using its SDK to capture data.

Traceloop is free up to 50k spans per month and 5 seats, but this only provides 24-hour data retention. Beyond this, you’ll need to talk to sales.

OpenLLMetry can capture data from a range of LLM providers, vector DBs, and LLM frameworks. It can then send this data to a range of supported destinations from Traceloop to Datadog to Honeycomb.

What makes OpenLLMetry special?

With its range of extensions and destinations, OpenLLMetry is very likely to integrate with the observability tools you already use.

It integrates with the broader OpenTelemetry ecosystem, meaning it can instrument things like your database, API calls, and more. Their semantic conventions for LLM were also adopted by the OpenTelemetry project.

5. Phoenix

• License: Elastic License 2.0
• GitHub stars: 8.9k as of March 2026
• Phoenix on GitHub

Phoenix is an open source AI observability platform. It provides tracing, evaluation, experiments, prompt management, and more. It works out-of-the-box with frameworks like LlamaIndex and LangChain as well as LLM providers like OpenAI, Bedrock, and more. It’s built by Arize AI, a unified AI observability and evaluation platform.

Arize doesn’t provide a free hosted version of Phoenix. Their product, AX Pro, starts at $50 per month for 10k spans and up to 3 users.

What makes Phoenix special?

Similar to OpenLLMetry, Phoenix works well with OpenTelemetry thanks to a set of conventions and plugins that are complimentary to OpenTelemetry. This means Phoenix can more easily integrate into your existing Telemetry stack.

Like Opik, Phoenix is connected to a broader AI development platform. Unique to Arize’s platform is their observability tools for ML and computer vision helping developers debug and improve these systems.

6. Helicone

• License: Apache 2.0
• GitHub stars: 5.3k as of March 2026
• Helicone on GitHub

Helicone is an open source platform for monitoring, debugging, and improving LLM applications. Beyond integrations with popular LLM providers, tracing, and an aggregate analytics dashboard, Helicone provides more tools like prompt management and evals.

Recently acquired by Mintlify, it will continue operating in maintenance mode.

Its hosted version is free up to 10,000 requests with some features limited to the $79/m pro and $799/m team plans. The costs for requests beyond the first 10,000 is unknown, though.

What makes Helicone special?

Helicone provides purpose-built tools for improving LLMs, like its prompt playground, prompt management, evaluation scoring, and feedback. This helps developers improve their LLM applications.

For developers focused on performance and reliability concerns, Helicone also contains both proxy and async interfaces for integrating with LLM providers. This ensures Helicone is only on your critical path if you want it to be.

Which LLM observability tool should you choose?

• Want LLM observability running alongside product analytics, session replay, A/B testing, feature flags, and more in one platform for full visibility? PostHog
• Need the most fully-featured LLM observability platform? Langfuse
• Building or fine-tuning models as well as LLM apps? Opik (via Comet)
• Already using OpenTelemetry and want LLM instrumentation to fit into your existing stack? OpenLLMetry
• Need AI observability beyond LLMs – including ML models and computer vision? Phoenix
• Want purpose-built tools for improving LLM outputs through prompt iteration and evals? Helicone

Is PostHog right for you?

Here's the (short) sales pitch.

We're biased, obviously, but we think you'll love PostHog if:

• You want LLM observability connected to the rest of your product data – session replays, feature flags, A/B testing, and analytics all in one place
• You're already using PostHog, so adding LLM analytics requires no extra setup or contract
• You want to try before you buy (we're self-serve with a generous free tier)

It's completely free to get started – no credit card required. Our setup wizard handles configuration in minutes, or you can check out our docs to do it yourself.

Frequently asked questions

Related reading

• PostHog LLM analytics documentation
• How to A/B test LLM prompts with PostHog
• The best error tracking tools – if you're also instrumenting the rest of your app alongside the LLM parts

1. LogRocket is a frontend monitoring tool that helps developers detect and solve issues.

2. PostHog goes beyond just frontend monitoring by integrating powerful analytics, session replay, error tracking, feature flags, experiments, and more into one platform.

How is PostHog different?

1. PostHog replaces multiple tools

LogRocket is a narrow tool. It does one thing, frontend monitoring, and it does it well. PostHog includes many of the same tools, such as session replay, error tracking, and console logs, but augments them with powerful dev tools, such as product analytics, feature flags, A/B testing, surveys, and more. It's everything you need to both squash bugs and understand every aspect of user behavior.

2. PostHog is for engineers, technical users, builders

PostHog is designed from the ground up to meet the needs of developers, and product-focused engineers. Session replay includes advanced tools for debugging errors and performance issues, while feature flags make it easy to test, and roll out, new features at scale. You get SQL access, an MCP server, a fully documented API, and SDKs for every major platform.

3. Transparent pricing, generous free tiers

Our pricing is 100% transparent. There are no hidden fees or surprise overages – what you see is exactly what you'll pay.

We also default to charging as little as possible while still making a sensible margin, and every product comes with a generous free tier. In fact, more than 90% of companies use PostHog for free!

Getting started takes minutes. Our AI setup wizard handles framework detection, SDK configuration, and initial event setup automatically – so you're capturing data without any manual instrumentation work.

Comparing PostHog and LogRocket

PostHog and LogRocket overlap on session replay and frontend monitoring, but diverge significantly on breadth. PostHog includes a wider range of tools that LogRocket doesn't offer, while LogRocket focuses on doing frontend debugging exceptionally well.

Product analytics

While LogRocket offers some product analytics features, it isn't primarily a product analytics tool and lacks many things product managers and engineers require. PostHog is a more capable product analytics app, offering advanced features such as correlation analysis, custom formulas, and a flexible SQL query editor.

• Correlation analysis: Enables you to automatically find correlated events or properties which affect the conversion rate of users within a funnel. LogRocket doesn't offer any such automated correlation discovery, meaning users must search for correlating factors without assistance.

• SQL access: PostHog gives you full access to your data by writing your own SQL queries. LogRocket doesn't offer SQL access.

Session replay

Both PostHog and LogRocket offer developer-focused session replay tools that goes beyond what dedicated UX research tools like Hotjar provide.

• Redux/state capture: LogRocket captures Redux and Vuex state at the moment of each action – a powerful feature for debugging React and Vue apps. PostHog doesn't have native state management capture.

• AI session summaries: PostHog includes AI-powered session summaries as part of PostHog AI. LogRocket has Galileo AI, which proactively surfaces user struggle patterns and UX recommendations across sessions.

Error tracking

LogRocket focuses on frontend JavaScript error tracking paired with session replay – you see the error alongside the full replay context of what the user was doing.

PostHog's error tracking connects exceptions to session replays, user behavior, and feature flag changes, but is less mature than dedicated tools for deep observability workflows.

SDKs and tracking

Both PostHog and LogRocket support broad tracking options, manual event instrumentation, and autocapture. PostHog supports a wide range of web, mobile, and server libraries, although not all features are available across all of them. LogRocket lacks server-side library support.

Data pipelines

PostHog offers more than 50 integrations, while LogRocket offers slightly fewer. Both tools integrate with popular third-party services.

Popular integrations

See our library for a full list.

Compliance

Both PostHog and LogRocket are SOC 2 certified and GDPR-ready. PostHog adds HIPAA compliance, cookieless tracking, and an open-source codebase you can audit – useful for teams in regulated industries or with strict data residency requirements.

When to choose PostHog vs LogRocket

• Want session replay connected to feature flags, A/B testing, product analytics, error tracking, and more – all in one platform with transparent pricing? Go with PostHog.

• Need debugging with a focus on front-end engineering workflows? LogRocket is built for that.

Recommendations by team type

For engineering-led product teams

• PostHog – SQL access, open-source code, an MCP server for AI coding tools, feature flags, error tracking, and session replay connected to your full analytics. Everything lives in one platform with a shared data model.

For frontend engineers debugging production issues

• LogRocket – Galileo AI proactive issue surfacing and session replay paired with console logs and network monitoring are purpose-built for front-end debugging workflows. PostHog's replay is strong but doesn't match LogRocket here.

For teams who need feature flags and A/B testing

• PostHog – LogRocket has no feature flags or experiments natively. PostHog includes both, tightly integrated with analytics and session replay so you can measure impact and filter replays by flag variant.

For teams building AI products

• PostHog – LLM analytics tracks model performance, token costs, latency, and traces. The MCP server lets AI coding tools like Cursor and Claude Code query your product data without leaving the editor. LogRocket doesn't offer AI observability tooling.

For privacy-conscious and regulated organizations

• Both are SOC 2 certified and GDPR-ready. PostHog adds open-source code, cookieless tracking, EU data residency, and HIPAA compliance. LogRocket supports GDPR and CCPA but is closed source and doesn't publish HIPAA compliance.

For early-stage startups

• PostHog – A generous free tier across every product (1M events, 5k replays, 1M flag requests/mo), transparent pricing you can model in advance, and startups can qualify for $50k in free credits. LogRocket's free tier is limited to 1,000 sessions/month with 1 month of data retention.

Frequently asked questions

As an all-in-one platform, it replaces a lot of legacy tools. PostHog is great if you:

1. Need quantitative and qualitative insights into user behavior
2. Want easy control over new features roll outs, kill switches, and feature gating
3. Desire one integrated tool over a complex stack of discrete solutions

This guide covers the most popular PostHog alternatives honestly – what they're good at, where they fall short, and who they're actually for.

Amplitude

• Founded: 2012
• Similar to: Mixpanel, Heap
• Best known for: Product analytics
• Useful for: Product managers, growth and marketing teams

What is Amplitude?

Amplitude is a product analytics platform with a strong focus on behavioral analytics, retention, and growth. It's well-established in the product analytics space and has built a broader platform that includes a CDP and a separate A/B testing product (Amplitude Experiment).

Amplitude is particularly strong for marketing and growth teams who need multi-touch attribution and predictive analytics.

Key features

• Behavioral analytics: Charts, funnels, user paths, and retention with a polished interface.
• Predictive analytics: AI-powered forecasting and propensity scoring.
• Multi-touch attribution: Track the full marketing funnel and channel impact on conversion.
• Guides and Surveys: Behavior-based in-app tooltips, walkthroughs, NPS surveys, and popups triggered by cohorts and behavioral data.
• Amplitude CDP: Built-in customer data platform for managing user data and identity resolution.
• Amplitude Experiment: Separate A/B testing product with feature flagging.
• Amplitude AI: A suite of AI capabilities including AI Agents, AI Feedback, and an MCP server for querying Amplitude data from Claude, Cursor, and other AI tools.

How does Amplitude compare to PostHog?

Key differences

1. Marketing vs. engineering focus: Amplitude offers multi-touch attribution, predictive forecasting, a CDP, and AI Visibility (which tracks how your brand appears in LLMs), which PostHog doesn't have. PostHog is designed for engineers and product teams: both have MCP servers, but PostHog adds SQL access, open-source code, error tracking, and logs. It also ships relentlessly – new features land every week, which tends to matter to engineering teams who want their tools to keep up.

2. AI capabilities: Both have AI assistants and MCP servers for querying data from tools like Claude and Cursor. Amplitude's AI Feedback stands out for marketing teams – it aggregates and synthesizes user signals from Zendesk, G2, Reddit, app store reviews, and more into prioritized product insights. PostHog has first-class LLM analytics for AI product teams – tracking token usage, latency, costs, and user feedback across AI pipelines – something Amplitude doesn't have.

3. Pricing:

• Amplitude's free Starter plan includes 10K MTUs (up to 10M events), 1,000 session recordings, and unlimited feature flags – enough for early exploration, but teams that need behavioral cohorts, advanced segmentation, or feature experimentation will need to upgrade. Growth and Enterprise pricing requires contacting sales.

• PostHog's free tier covers 1 million analytics events, 5,000 web session replays, 100k flag requests and more with no MTU cap. Pricing is transparent and entirely usage-based, with discounts for bulk purchases.

Bottom line

Amplitude is the better choice for growth and marketing teams who need multi-touch attribution and predictive analytics. PostHog is the better choice for engineering and product teams who want session replay, feature flags, error tracking, LLM analytics, and more alongside their core analytics.

Mixpanel

• Founded: 2009
• Similar to: Amplitude, Heap
• Best known for: Product analytics
• Useful for: Product managers and growth teams

What is Mixpanel?

Mixpanel is a platform that has expanded significantly beyond its analytics-only roots. It launched a full Experiments product with feature flagging and statistical analysis, and added session replay and heatmaps to their lineup in 2024.

Mixpanel is strong on event-based analysis, funnel visualization, and its usage-based pricing model, which makes it popular with startups.

Key features

• Event analytics: Trends, funnels, user paths, and retention analysis.
• Experiments: End-to-end A/B testing with feature flags, cohort targeting, sticky bucketing, and statistical significance across primary, secondary, and guardrail metrics.
• Session replay: Available on all plans (10k replays/month free), integrated directly with funnel and cohort analysis.
• Heatmaps: Click and scroll heatmaps built into the same workflow as analytics and replay.
• Metric trees: Interactive, living frameworks that map relationships between metrics, track goals, and connect experiment results to business outcomes.
• Warehouse connectors: Sync data bidirectionally with BigQuery, Snowflake, Redshift, and Databricks.
• Spark AI: Natural language querying across your analytics data.

How does Mixpanel compare to PostHog?

Key differences

1. Scope: Mixpanel and PostHog now overlap more than they used to – both have analytics, session replay, A/B testing, and feature flags. Where PostHog pulls ahead is error tracking, surveys, logs, CDP, LLM analytics, and data warehouse. Where Mixpanel stands out: metric trees (interactive frameworks that map relationships between metrics, connect experiment results to business outcomes, and track goals in a living dashboard), and a funnel and path analysis UI that non-technical users might find more approachable than PostHog's.

2. Developer experience: PostHog is open source and has an MCP server that lets engineers query their product data directly from AI coding tools like Cursor and Claude. Mixpanel is more accessible to non-technical users but offers less for engineering-led teams who want to inspect the codebase or integrate deeply with their dev tooling.

3. Pricing:

• Mixpanel's free plan includes up to 1M events/month and 10K session replays, but features such as cohorts, custom properties, and feature flags are not available; their startup program gives eligible companies (under 5 years old, under $8M raised) their first year completely free – 1B events, 500k replays, all features unlocked.

• PostHog's free tier includes 1M analytics events, 5,000 session replays, 1M feature flag requests, and much more; PostHog for Startups offers $50k in credits for qualifying early-stage companies.

Bottom line

Mixpanel is a strong choice for teams who want polished product analytics with session replay, A/B testing, and metric trees in one focused tool. Once you also need error tracking, surveys, logs, or a data warehouse, the case for PostHog gets significantly stronger.

FullStory

• Founded: 2014
• Similar to: LogRocket, Hotjar
• Best known for: Session replay
• Useful for: Product managers, UX researchers, customer support

What is FullStory?

FullStory is a digital experience intelligence platform best known for session replay. It captures every user interaction automatically and lets teams search, filter, and analyze sessions without manual event setup.

FullStory is particularly popular with enterprise UX and customer experience teams who need to diagnose friction, understand user journeys at scale, and share session evidence across product, support, and design.

Key features

• Session replay: Full DOM capture with autocapture of all user interactions.
• Heatmaps: Click, scroll, and cursor movement heatmaps.
• Frustration signals: Automatic detection of rage clicks, dead clicks, and error clicks.
• DX Data: FullStory's proprietary behavioral data layer for querying sessions retroactively.
• Funnels and journeys: Visualize user flows and drop-off points.
• Guides and Surveys: No-code product tours, onboarding checklists, banners, smart tips, and in-app NPS surveys triggered based on behavioral data.
• StoryAI: Built-in AI agents that automatically summarize sessions, surface friction points prioritized by conversion impact, and answer behavioral questions in natural language.

How does FullStory compare to PostHog?

Key differences

1. Session replay depth: FullStory's replay tooling – frustration signal detection, cursor heatmaps, and DX Data retroactive querying – is more mature than PostHog's for dedicated UX research. PostHog's replay is strong for product and engineering use cases but less focused on pure UX research workflows.

2. Feature breadth: PostHog includes feature flags, A/B testing, error tracking, logs, and more. FullStory is primarily a session replay and analytics tool (which also features Guides & Surveys) – teams using it typically need other tools to cover the rest of the stack.

3. Pricing and access: PostHog offers 5,000 free session replays per month with transparent, self-serve pricing. FullStory's pricing is opaque – getting started requires a conversation with sales.

Bottom line

FullStory is the better choice for dedicated UX research teams who need advanced heatmaps and frustration signal detection and have budget for an enterprise contract. PostHog is the better choice for product and engineering teams who want session replay as part of a broader platform – and who want transparent pricing and a free tier.

Heap

• Founded: 2013
• Similar to: Mixpanel, Amplitude
• Best known for: Product analytics
• Useful for: Product managers, marketing teams

What is Heap?

Heap was acquired by Contentsquare. Contentsquare is in the process of migrating Heap customers onto its own platform. If you're evaluating Heap as a new tool, it's worth knowing that you may be migrated to Contentsquare rather than Heap long-term.

Heap's key innovation was autocapture combined with retroactive event definition – you capture everything, then define what matters later. This remains genuinely useful for teams who don't know what they'll need to analyze in advance.

Key features

• Autocapture: Capture every user interaction automatically, define events retroactively.
• Visual labeling: Tag events through a visual interface without code.
• Session replay: Available through the Heap platform.
• Funnels and paths: Analyze user journeys and drop-off points.
• Sense AI: Chat-based AI analyst that answers plain-language questions about user behavior, builds charts on demand, and suggests follow-up questions.
• Illuminate: A data science layer that automatically surfaces insights from your behavioral data – including top events influencing conversion and group suggestions to identify high-impact segments – without requiring you to know what to look for in advance.
• Data connectors: Sync to data warehouses including BigQuery and Snowflake.

How does Heap compare to PostHog?

Key differences

1. Retroactive event definition: Heap's visual labeling is genuinely powerful – you can define and retroactively apply event definitions without code or engineering support. PostHog's autocapture captures everything too, but labeling events requires more technical involvement.
2. Feature breadth: Heap covers session replay, web analytics, and heatmaps alongside its analytics core – but PostHog goes further with feature flags, experiments, error tracking, surveys, logs, and more. The Contentsquare merger may also eventually give Heap users access to an even broader feature set as the platforms converge.
3. Pricing:

• Heap's free plan covers up to 10k monthly sessions with core analytics and 6 months of data history. Growth and Pro plans are usage-based with custom pricing – you need to install the snippet to get an estimate, and session replay is an add-on rather than included.

• PostHog's free tier covers 1M analytics events and 5,000 session replays with transparent, self-serve pricing and no sales call required.

Bottom line

Heap's retroactive event labeling remains genuinely useful for non-technical teams, but, if you're evaluating it today, it's worth understanding the broader Contentsquare platform and how it fits into your needs.

LogRocket

• Founded: 2016
• Similar to: FullStory
• Best known for: Session replay and front-end monitoring
• Useful for: Customer support, engineering teams

What is LogRocket?

LogRocket positions itself between FullStory and Sentry – it combines session replay with front-end error monitoring and some product analytics.

It's popular with engineering teams who want a developer-focused session replay tool with performance monitoring built in. LogRocket has also added Galileo, an AI layer that surfaces issues and recommendations proactively.

Key features

• Session replay: Full DOM replay with console logs, network requests, and Redux state.
• Front-end monitoring: JavaScript error tracking and performance profiling.
• Heatmaps: Click, rage click, and scroll maps.
• Product analytics: Basic funnels, user paths, and conversion tracking.
• Galileo AI: Proactive issue detection and UX recommendations.

How does LogRocket compare to PostHog?

Key differences

1. Session replay focus: LogRocket's replay tooling includes Redux/state management capture and Galileo AI proactive issue detection – features aimed squarely at front-end engineers. PostHog's replay is broader, connecting sessions directly to feature flags, experiments, and funnel analysis.
2. Product analytics: PostHog's product analytics are significantly deeper – cohorts, retention, paths, SQL access, and group analytics. LogRocket's analytics are basic and supplementary to its replay core.
3. Pricing:

• LogRocket's free tier covers 1,000 sessions/month with 1 month of data retention. Paid plans start at $69/month for 10k sessions on a monthly commitment (annual plans are cheaper). Session replay is the core product – analytics, heatmaps, and error tracking are included, but you're primarily paying for session volume.

• PostHog's free tier covers 5,000 session replays plus 1M analytics events, feature flags, and more; pricing is transparent and usage-based across the whole platform.

Bottom line

LogRocket is a strong choice for engineering teams who want developer-focused session replay with front-end performance monitoring. PostHog is the better choice if you also need deep product analytics, feature flags, A/B testing, or a single platform for your whole stack.

Which PostHog alternative should you choose?

Choose...

• Amplitude if you're a growth or marketing team that needs multi-touch attribution, predictive analytics, or AI-powered brand visibility in LLMs. Amplitude has built a genuine marketing platform, while PostHog is more geared towards technical users.
• Mixpanel if you want polished product analytics with session replay, A/B testing, and feature flags but don't need the broader PostHog platform. The free tier is generous and the startup program is excellent.
• FullStory if dedicated UX research is your primary use case and you have enterprise budget. Its session replay tooling – frustration signals, StoryAI, Guides & Surveys – is more mature than PostHog's for deep qualitative research.
• LogRocket if you're a front-end engineering team that wants session replay connected to Redux state, console logs, and network monitoring. Strong for debugging workflows, though you'll need other tools alongside it.
• Heap if autocapture and retroactive event definition appeal to you – it's a solid tool. Just make sure you understand what the full Contentsquare platform looks like before committing.

Is PostHog right for you?

Here's the honest version.

PostHog is the best choice if:

• You want one platform with a full suite of tools, keeping all your data and user context in one place.
• You're a startup, an engineer, or a technical product team that values SQL access, an MCP server, and open-source code.
• You want to try before you commit – PostHog is fully self-serve with a generous free tier and no sales call required.

It's free to get started with no credit card required. Our AI setup wizard handles configuration in minutes.

Frequently asked questions

What else do you need to do? Talk to users? Sure. Gather feedback? That's useful, too. But, for us, there's nothing better than watching session replays for understanding a user's pain points.

Replays (aka session recordings) are a great way to understand:

• What causes bugs and user frustration.
• Where users are getting stuck on your site.
• Hesitation at different stages of your funnel.
• How your users are interacting with individual elements of your product.

Hotjar (now part of Contentsquare) and Fullstory are popular session replay products, but they're expensive, inflexible, and primarily designed with marketing teams.

In this guide, we're looking at Hotjar alternatives that are either free or open source, particularly session replay tools.

1. PostHog

PostHog (that's us 👋) is an all-in-one developer platform that includes session replay, product analytics, heatmaps, feature flags, A/B testing, error tracking, surveys, and more.

This means it's not just an open source session replay tool, but a full replacement for tools like Hotjar, FullStory, LaunchDarkly, and Sentry.

PostHog's session replay includes console logs, network activity, a DOM explorer, and performance metrics so you can identify the root cause of bugs without leaving the replay. You can filter recordings by user, event, feature flag, or any combination of properties, save playlists of related sessions, and export recordings offline for safe keeping.

AI session summaries automatically surface key moments and issues, so you don't have to watch every recording in full. All of PostHog's tools work together natively – jump from a funnel drop-off directly into a relevant replay, or see which feature flag a user was on when they hit a bug.

Who is PostHog for?

PostHog is ideal for startups, product-minded developers, and product teams. Unlike Hotjar and Fullstory, it's designed for technical users. Its broad range of features means PostHog can replace several tools.

It also supports replay for mobile apps running that use Android, iOS, React Native, and Flutter SDKs, making it a good option if you're also looking for a tool that offers Hotjar features for mobile apps.

Key features

• Session replay: Console logs, network activity, DOM explorer, and performance metrics. Filter recordings by user, event, or feature flag. Export recordings offline. AI session summaries automatically surface key moments and issues without watching every recording.
• Mobile replay: Native SDKs for iOS, Android, React Native, and Flutter with 2,500 free mobile recordings per month.
• Heatmaps: Visualize clicks (including rage clicks), scroll depth, and mouse movement.
• Error tracking: Monitor exceptions and stack traces connected directly to session replays.
• Product analytics: Funnels, user paths, retention, trends, and SQL access for custom queries.
• Privacy controls: CSS selectors to block sensitive data. Cookieless tracking available.

How much does PostHog cost?

PostHog is free up to 1 million captured events, 5,000 web recordings, and 2,500 mobile recordings per month, and you can set billing limits to avoid surprise bills.

Bottom line

PostHog is the best open source session replay tool if you want more than just replay. Its generous free tier, mobile support, and tight integration with analytics, feature flags, and error tracking make it the most complete option on this list.

2. OpenReplay

OpenReplay is an open-source alternative to Microsoft Clarity that offers more advanced, technical features for engineers on top of basic session replay. Its tooling is particularly effective for application state and customer support. It has some basic analytics features, such as funnel analysis, though it falls some way short of feature-complete product analytics tools.

Who is OpenReplay for?

OpenReplay is a developer-friendly tool. It allows engineers to see how users are interacting with their web apps, respond to issues faster, and troubleshoot problems with users.

Key features

• Session replay: Pixel-perfect recordings with event timelines, network activity, console logs, and JS error capture.
• DevTools: Reproduce bugs as if they happened in your own browser, with application state, GraphQL queries, and 40+ metrics.
• Co-browsing: Live screen sharing with users via WebRTC – no third-party software required.
• Heatmaps: Click and scroll heatmaps added in recent releases.
• Product analytics: Basic funnel analysis and conversion tracking.
• Spot: A Chrome extension for recording bugs directly from the browser with full technical context attached.

How much does OpenReplay cost?

OpenReplay has three cloud plans and a free self-hosted option.

• The Free cloud plan is $0 and includes up to 1,000 sessions/month, 2 users, 1 project, and 30-day data retention.
• The Serverless plan is $5.95 per 1,000 sessions/month with unlimited users and projects, also with 30-day retention.
• The Dedicated plan starts at $0.25/hour (roughly $179/month) and gives you a dedicated VM with configurable data retention, SSO/SAML, conditional recording, and data residency across 35+ regions.

The self-hosted open source edition is completely free and includes all core features.

Bottom line

OpenReplay is the best self-hosted option for developer teams that need advanced debugging tools and full data sovereignty. Its co-browsing feature is unique among open source tools. The tradeoff is that self-hosting requires real DevOps resources to deploy and maintain at scale.

3. Sentry

Sentry is a source-available application monitoring platform primarily known for error tracking and performance monitoring.

Session replay was added in 2023 and is tightly integrated with Sentry's error data: when an exception fires, the session replay for the affected user is automatically linked, giving you the full interaction context without any additional investigation.

Who is Sentry for?

Sentry is for developers and engineering teams, not UX or product teams. Sentry approaches replay from an error-first perspective – it's most valuable when you want to understand what a user was doing right before something broke.

Key features

• Session replay: Video-like reproduction of user interactions including page visits, mouse movements, clicks, taps, and scrolls.
• Error-linked replay: Every error automatically links to the relevant session replay for instant context.
• Performance monitoring: Web vitals, transaction tracing, and frontend performance data alongside replays.
• Error tracking: Real-time exception monitoring with stack traces across dozens of languages and frameworks.
• Self-hosting: Full self-hosted deployment available free.

How much does Sentry cost?

Sentry has four plans.

• The Free plan is $0 and includes 1 user, error monitoring, and 50 session replays/month.
• The Team plan is $26/month with unlimited users and 50 replays included, with additional replays available at usage-based rates.
• The Business plan is $80/month and adds unlimited custom dashboards and anomaly detection.
• Enterprise pricing is custom.

All plans include 50 session recordings. Self-hosting is free via Docker Compose, though it's designed for simpler deployments rather than large-scale production use.

Bottom line

Sentry is the best option if your team already uses it for error monitoring and wants to add session replay without a new vendor. It's not a standalone replay tool – it's most valuable when replay is part of a broader error and performance monitoring workflow.

4. UXWizz

UXWizz is a self-hosted product analytics tool offering visitor insights, session recording, heatmaps, segments, and basic A/B testing.

It offers a lightweight version of session recording that only stores the URL of the page and the user actions. This is designed to optimize your data storage and remove the manual maintenance otherwise required by UXWizz.

Who is UXWizz for?

UXWizz is a good choice for individuals and small businesses who want quick, qualitative insights and session recordings. A Wordpress plugin is also available.

Key features

• Session recording: Lightweight and full DOM recording modes.
• Heatmaps: Click, move, and scroll heatmaps.
• Visitor segmentation: Filter and group visitors by behavior, device, or custom properties.
• A/B testing: Basic split testing built in.
• Error tracking: Monitor and log JavaScript errors.
• AI analysis: Ask AI to generate stats and charts using natural language queries.
• WordPress plugin: Easy installation for WordPress sites.

How much does UXWizz cost?

UXWizz is self-hosted only with a one-time license fee.

• The Personal license is $299 and covers unlimited websites, visitors, recordings, and heatmaps for a single user.
• The Company license is $799 and adds unlimited dashboard users for teams. Both include one year of free support and updates, with extensions available as paid add-ons.
• The Agency license is $2,490 and includes white-labeling, custom branding, custom dashboard pages, domain limits per user, and up to 5 servers – intended for agencies managing analytics on behalf of clients.

All tiers include one year of free support and updates, with extensions available as paid add-ons. There is no cloud version and no free tier, though a free trial is available.

Bottom line

UXWizz is a good fit for individuals and small businesses who want basic self-hosted analytics and session recording without a monthly bill. The single-developer maintenance model is a real risk for long-term reliability, but the one-time pricing makes it a low-stakes option to try.

5. UXlens

UXlens is a developer-first session recording tool that provides insight into user interface and conversion issues. It does not include any additional analytics tools, but does have useful segment filtering features that help businesses locate blocks in their funnel.

Who is UXlens for?

UXlens is a good choice for UX designers and product teams who want to study the impact of their UI on the user journey without spending too much.

Key features

• Session recording: Lightweight recordings focused on user interactions.
• Segment filtering: Customizable filters to track sessions by user segment, device, or page.
• Privacy by default: Input elements masked by default to protect user data.
• Self-hosting: Docker-based self-hosted deployment available.

How much does UXlens cost?

UXlens offers both cloud subscriptions and self-hosted lifetime licenses. Cloud plans start at $6/month (Starter, 6,000 recordings) and go up to $70/month (Business Plus, 80,000 recordings), with Professional at $12/month (12k) and Business at $30/month (32k).

All cloud plans include a 30-day recording history and conversion funnels. Enterprise plans are available on request.

Self-hosted lifetime licenses start at $200 one-time (Startup – up to 3 websites, 10 admin seats) and $500 one-time (Agency – unlimited websites, 10 admin seats). An Agency Plus tier with REST API access is available on a yearly basis (contact for pricing), and a free license is available for non-commercial and personal use.

Bottom line

UXlens is a minimal, affordable session recording tool for small teams who just need the basics. It lacks the depth of analytics and developer tools found in PostHog, OpenReplay, or Sentry, but its low price point and self-hosting option make it worth considering for simple use cases.

6. Matomo

Matomo is an open web analytics platform. Touted as an alternative to Google Analytics, Matomo is largely focused on understanding marketing website analytics. Session recording is available as an add-on via the on-premise marketplace.

Who is Matomo for?

Matomo is mainly built with marketing and content teams in mind, offering insights into website content engagement for optimization of user journeys. Features like session recording are part of a wider set of tools that are useful for product teams as well, but they aren't included in Matomo's open source release.

To learn more, read our PostHog vs Matomo comparison.

Key features

• Web analytics: Traffic, referrers, goals, funnels, and acquisition analysis.
• Session recording: Available as a paid add-on for self-hosted; included in cloud Business plan.
• Heatmaps: Click and scroll heatmaps, also a paid add-on for self-hosted.
• GDPR compliance: Built-in consent management and EU data hosting options.
• Self-hosting: Full on-premise deployment with no data sent to third parties.

How much does Matomo cost?

Matomo On-Premise is free and open source – the core analytics platform costs nothing to self-host, with optional paid plugins available separately.

Matomo Cloud is priced by monthly hit volume: starting at $26/month for 50,000 hits, $42/month for 100,000 hits, $85/month for 300,000 hits, and scaling up to $399/month for 2 million hits and beyond.

All cloud plans include 150 session recordings and 1,500 heatmap pageviews per month, but it's unclear how this scales. Enterprise plans for very high volumes are available on request.

Bottom line

Matomo is the best option on this list if you primarily want website analytics and want session recording as a secondary add-on. The session recording feature is not included in the free open source release, which limits its appeal compared to tools where replay is a core feature.

Which open source session replay tool should you choose?

• Want an all-in-one platform with session replay, product analytics, feature flags, error tracking, and more? Go with PostHog.
• Need a powerful self-hosted option with advanced DevTools, co-browsing, and strict data residency requirements? Choose OpenReplay.
• Already using Sentry for error monitoring and want replay without adding another vendor? Stick with Sentry.
• Want basic self-hosted recording and heatmaps with no monthly fees? UXWizz or UXlens are your best bets.
• Need open source web analytics with session recording as part of the package? Try Matomo.

Is PostHog right for you?

Here's the (short) sales pitch.

We're biased, obviously, but we think PostHog is the best open source session replay tool if:

• You want session replay tightly connected to product analytics, feature flags, and error tracking.
• You need mobile replay (iOS, Android, React Native, Flutter) on all plans including free.
• You want transparent, usage-based pricing with a generous free tier and no sales calls.
• You want open source code you can audit, self-host, or contribute to.

It's completely free to get started – no credit card required. Our AI setup wizard handles configuration in minutes, or check out our docs to do it yourself.

Frequently asked questions

The transition from Universal Analytics forced millions of users onto a platform that thinks and behaves very differently.

GA4's event-based model is more powerful than its predecessor in theory, but in practice, many teams find it harder to use, harder to query, and harder to trust. Add in data sampling on large exploration queries, a 14-month retention limit, privacy concerns under GDPR, opaque pricing, and a UI that frustrates marketers and developers alike – and it's easy to understand why so many teams are looking for alternatives.

Whether you're leaving GA4 for privacy reasons, because you've outgrown its free tier limits, or because you simply need more than web analytics, this guide covers the best GA4 alternatives available today.

1. PostHog

• Best for: Startups, engineers, and product teams
• Tracking method: Event-based
• GDPR compliance: ✔ (via self-hosting or EU Cloud)
• GA data import: ✖

PostHog (that's us 👋) is an all-in-one developer platform that includes web analytics, product analytics, session replay, error tracking, experiments, user surveys, and more – pretty much everything you need to track user behavior in an app or website.

This means it's not just a GA4 alternative, but also a replacement for tools like Mixpanel, LaunchDarkly, and FullStory.

Typical PostHog users are engineers and product managers at startups and mid-size companies, such as ElevenLabs, Supabase, and Lovable.

Like GA4, PostHog is an event-based platform. It's priced per event captured, though it offers a generous 1 million events for free each month, so many users can use it for free.

How does PostHog compare to GA4?

PostHog gives you all the web analytics of GA4 and far more on top. Where GA4 is a marketing analytics tool, PostHog is a full product development platform. This means that, in addition to tracking user behavior, you can also use PostHog to run A/B tests, set up feature flags, record user sessions, and even survey users.

GA4 doesn't have any of these features built-in, so you'd need to integrate with other tools to get the same functionality.

Read our PostHog and Google Analytics comparison for an in-depth look at the differences

Bottom line

PostHog is the best GA4 alternative for engineering and product teams who need more than just web analytics. It covers everything GA4 does and adds a full stack of product tools, all with transparent pricing and a generous free tier.

2. Matomo

• Best for: Content, marketing and e-commerce websites
• Tracking method: Session-based
• GDPR compliance: ✔
• GA data import: ✔

Matomo is one of the most popular Google Analytics alternatives around. Like UA, it's based on a session-based tracking model, and it even allows new users to import data from an existing Google Analytics account.

It also shares some weaknesses inherent in Universal Analytics. While Matomo has added more advanced analytics features, it's better suits traditional website analytics than tracking desktop and mobile apps. Like PostHog, it's open source, so can be self-hosted if you prefer.

How does Matomo compare to GA4?

Matomo is session-based like Universal Analytics, making it a more natural migration path for teams who preferred the old Google Analytics model. It gives you full data ownership and GDPR compliance without the complexity of GA4.

Bottom line

Matomo is closest you'll get to a Universal Analytics-style experience. It's session-based and was explicitly conceived as European alternative to Google Analytics. It has plenty of features, too, though some may find the interface a little dated. It's especially well-suited to publishers, privacy-conscious organizations, and teams in regulated industries.

3. Kissmetrics

• Best for: Marketing and e-commerce websites
• Tracking method: Event-based
• GDPR compliance: ✔
• GA data import: ✖

Kissmetrics is all about tracking marketing ROI. As a result, it doesn't track what Kissmetrics deems vanity metrics like bounce rate, time on page, exits, etc. It does, however, track most essential website metrics, and makes it easy to understand the impact of organic and paid marketing activity.

One of its key features is the Populations report, which groups users into key cohorts, such as those who have recently activated trials, or are at risk of churning. You can also create custom funnel reports, and track user paths through your website.

How does Kissmetrics compare to GA4?

Kissmetrics and GA4 serve different masters. GA4 is built for traffic and marketing attribution. Kissmetrics is built for understanding customers and driving revenue – it intentionally excludes "vanity metrics" like bounce rate and time on page, focusing instead on what actually drives growth.

Bottom line

Kissmetrics is a great platform for sophisticated marketing teams who want to track the impact of their work on revenue. It's overkill for basic website analytics use cases, but a good option for mature businesses looking for a robust alternative to Google Analytics.

4. TelemetryDeck

• Best for: Mobile apps
• Tracking method: Event-based
• GDPR compliance: ✔
• GA data import: ✖

While it can be used on websites, TelemetryDeck is primarily a privacy-minded analytics tool for mobile apps. As such, it uses an event-based tracking model – TelemetryDeck calls them signals. It has first-party SDKs for Swift (iOS, macOS etc. apps), Kotlin (Android and Java apps), and Javascript (Node and web apps).

TelemetryDeck makes it easy for app developers to track things like active users, OS version, app version, and basic user metadata like user location. It also supports basic retention and conversion funnel insights. TelemetryDeck doesn't collect any personally identifiable metadata, so you don't need tracking consent banners.

How does TelemetryDeck compare to GA4?

TelemetryDeck does far less than GA4 by design. It's intentionally lean – no user profiles, no revenue tracking, no ad attribution. What it offers is privacy-safe app analytics with zero compliance overhead, which is a compelling tradeoff for many indie developers and privacy-focused teams.

Bottom line

TelemetryDeck is a good option for those who want basic app analytics, but it falls short of feature parity with GA4. It's not a full GA4 replacement for teams that need revenue tracking, ad attribution, or user profiles – but it's a great fit for indie developers and privacy-first teams.

5. Plausible

• Best for: Content and marketing websites
• Tracking method: Session-based
• GDPR compliance: ✔
• GA data import: ✔

Plausible is a leader in the trend of lightweight, privacy-orientated analytics tools. It's easy to use and doesn't collect any personally identifiable information. This makes it ideal for complying with GDPR, but this comes at the cost of functionality.

Plausible can only track very basic website metrics like pageviews, session duration, and referrer information. This makes it useless for apps, and significantly less powerful than Google Analytics and other alternatives in this list.

But, if you just want basic website analytics, it gets the job done, and won't adversely impact the performance of your website thanks to its lightweight tracking script.

How does Plausible compare to GA4?

Plausible covers the basics of web analytics without the complexity. It's not a like-for-like GA4 replacement – it lacks user profiles, advanced segmentation, and the depth of reporting GA4 offers. But for teams who just want simple, trustworthy traffic data without GDPR headaches, it's hard to beat.

Bottom line

While it lacks many of the advanced features of GA4, Plausible is a good option for content and marketing teams who just want easy to use, basic website analytics.

6. Vercel Web Analytics

• Best for: Content and marketing websites
• Tracking method: Event-based
• GDPR compliance: ✔
• GA data import: ✖

Vercel Web Analytics is a privacy-friendly analytics tool included in all Vercel plans. Like Plausible, it tracks basic website metrics like pageviews, unique users, time on page, and referrers. It doesn't collect personally identifiable information, so can be used without cookie banners.

Other useful features include a Speed Insights tool for keeping track of your website's Core Web Vitals. You can also set up custom events to track conversions.

How does Vercel Web Analytics compare to GA4?

Vercel Web Analytics is a convenience product, not a GA4 replacement. If you're already on Vercel, it's worth enabling for basic traffic data. If you need more depth – funnels, user paths, retention, ad attribution – you'll need a dedicated analytics tool.

Bottom line

Vercel Web Analytics is a nice value-add for any front-end dev using Vercel's frontend-as-a-service. There's certainly no need to deploy another privacy-first analytics tool (e.g. Plausible, Fathom) if you're already using Vercel, though it falls a long way short of a genuine Google Analytics alternative. If you need more than what it offers, consider deploying PostHog or Matomo.

7. Piwik PRO

• Best for: Content, marketing and e-commerce websites
• Tracking method: Session-based
• GDPR compliance: ✔
• GA data import: ✖

Piwik PRO is a commercial spinoff of Matomo – Matomo used be called Piwik. As such, there are some similarities between the two, such as session-based tracking and superficial UX similarities.

Piwik PRO's main differentiators are enterprise level support and the integration of a customer data platform (CDP). Like Matomo, it also puts an emphasis on privacy compliance by integrating a consent manager.

How does Piwik PRO compare to GA4?

Piwik PRO is particularly strong for regulated industries and enterprise teams that need GDPR compliance, consent management, and EU data residency in a single package. It's less self-serve than GA4 but offers more control.

Bottom line

Unsurprisingly, Piwik PRO's roots in Matomo make it a popular choice for users who are familiar with Universal Analytics. It's less feature-rich than Matomo in some respects, but may be a better choice for larger organizations who require more support and scale.

8. Fathom

• Best for: Content and marketing websites
• Tracking method: Session-based
• GDPR compliance: ✔
• GA data import: ✔

Fathom is another leading privacy-focused analytics tool that's similar in scope to Plausible. It's ideal for small and medium-size marketing websites, but it lacks the deeper features typical Google Analytics users will demand.

For a deep dive, check out PostHog vs Fathom Analytics too.

How does Fathom compare to GA4?

Like Plausible, Fathom is intentionally simple. It provides clean, unsampled web analytics data without the privacy concerns or interface complexity of GA4. It's not for teams that need advanced segmentation, user-level data, or ad attribution – but it's excellent for what it does.

Bottom line

There isn't much to choose between Fathom and Plausible. Again, if you want lightweight website analytics without the feature bloat of Google, you can't go wrong with Fathom.

9. Counter

• Best for: Content and personal websites
• Tracking method: Session-based
• GDPR compliance: ✔
• GA data import: ✖

Counter is a free and open source analytics tool that operates a "pay what you want" model. It's by no means a feature-complete alternative to GA4, but it's ideal if you want to track basic website activity like visits, referral data, and user properties (device, platform, browser etc.).

Counter's creators can offer it for free because it only collects aggregated data, reducing the complexity and load on the server, while also improving data privacy. To count unique users, Counter uses a combination of techniques including the browser's cache, sessionStorage, and referrer inspection.

How does Counter compare to GA4?

Counter is about as minimal as analytics gets. It does just enough to answer "how many people visited my site and where did they come from?" For personal projects, blogs, and indie developers who don't need anything more, it's a compelling free alternative.

Bottom line

Counter is a great choice if you have basic needs. It does most of the things the popular privacy-first analytics tools offer, but does so for free. The simple interface is easy to understand, though it doesn't allow you to drill down deeply into individual page performance like Fathom or Plausible.

Which GA4 alternative should you choose?

Here's a quick guide based on what you need:

For engineering and product teams who need more than web analytics:

• PostHog – Web analytics, product analytics, session replay, feature flags, A/B testing, error tracking, surveys, and more in one developer platform. Best if you want to replace GA4 and several other tools with transparent pricing.

For a familiar Universal Analytics-style replacement:

• Matomo – Session-based analytics with GA data import and self-hosting. Best for teams migrating from Universal Analytics who want full data ownership.
• Piwik PRO – Best for enterprise teams in regulated industries (healthcare, finance, government) who need a CDP and consent manager alongside analytics.

For revenue and marketing analytics:

• Kissmetrics – Person-based analytics tied to revenue and LTV. Best for e-commerce and SaaS teams who want to understand which channels and features drive revenue.

For privacy-first lightweight web analytics:

• Plausible – Clean, GDPR-compliant web analytics with no cookies or consent banners. Best for content sites and marketing websites that want simple, trustworthy data.
• Fathom – Very similar to Plausible, with EU-owned infrastructure and uptime monitoring. Best for privacy-conscious teams willing to pay slightly more.
• Counter – Free, minimal web analytics. Best for personal projects and indie developers who just need basic traffic data.

For app developers:

• TelemetryDeck – Privacy-safe analytics for iOS, Android, Flutter, and React Native apps with no consent banners required.
• PostHog – Full-featured analytics for iOS, Android, React Native, and Flutter with session replay, feature flags, and error tracking. Best if you need more than privacy-safe event counts.

For teams already on Vercel:

• Vercel Web Analytics – Easiest to implement with zero configuration. Not a full GA4 replacement but good enough for basic traffic data.

For early-stage startups:

• PostHog – A generous free tier (1M events, 5,000 replays, 1M feature flag requests) plus up to $50k in credits for eligible startups. One platform that scales from first users to product-market fit.

Is PostHog right for you?

Here's the (short) sales pitch.

We're biased, obviously, but we think PostHog is the best GA4 replacement if:

• You need more than web analytics – session replay, feature flags, A/B testing, and error tracking all in one place.
• You want transparent, usage-based pricing without a sales call.
• You're an engineering or product team who wants SQL access, open-source code, and APIs.
• You want to try before you buy (we're self-serve with a generous free tier).

It's completely free to get started – no credit card required. Our AI setup wizard handles configuration in minutes, or check out our docs to do it yourself.

Frequently asked questions

GA4 is designed to track traffic, campaigns, and user behavior across websites and apps, with tight integration into Google's advertising ecosystem. It's one of the most widely deployed analytics tools in the world, and the default choice for teams who want marketing and attribution data in a familiar interface.

PostHog is an all-in-one developer platform that combines all the tools developers need in one place, with a single login and a single contract.

If you're looking for a GA4 alternative – whether for privacy reasons, because you need more than web analytics, or because you've outgrown GA4's free tier limits – this comparison will help you decide if PostHog is the right fit.

How is PostHog different?

1. We're an all-in-one platform

PostHog combines product analytics and web analytics with session replay, feature flags, error tracking, experiments, surveys, a baked-in data warehouse, and more into one tightly integrated platform. Everything you need from a single app with a single contract. A genuine single source of truth for your product and customer data.

GA4 is a powerful web analytics tool, but it doesn't include session replay, feature flags, A/B testing, error tracking, or surveys. For those features, you'd need separate tools like FullStory, LaunchDarkly, and Optimizely, and a way to stitch the data together.

2. It's built for developers

This means you get support from the engineers who actually build the product, extensively documented APIs, an MCP server for querying your data directly from AI coding tool, and a SQL query builder so you can analyze data how you want.

We're open source, so you can inspect our source code. And we ship fast – check out the weekly changelog to see what's new.

GA4 is primarily designed for marketers and growth teams. It integrates tightly with Google Ads and the broader Google Marketing Platform, but offers limited developer-facing tooling.

3. Transparent and cheap pricing (forever)

We default to charging as little as possible while still making a profit – we also have a generous free tier on all our products. We can do this because we're efficient. We don't splurge on D-list comedians to host an annual convention you'll never attend. Want to know how much we'll charge? See our pricing calculator.

GA4's core product is free, but once you hit the limits of the free tier you'll need to evaluate GA360, which starts at approximately $50,000/year according to third party sources (Google doesn't publish pricing).

Comparing PostHog and Google Analytics

Analytics

GA4 is primarily built for web analytics, so this is where the comparison matters most. PostHog's web analytics dashboard covers all the core metrics – traffic sources, pageviews, sessions, UTM tracking, and more – while also adding capabilities GA4 lacks, like cookieless tracking and web vitals monitoring.

PostHog goes significantly deeper on product analytics – including features like group analytics for analyzing behavior at the account or company level (ideal for B2B SaaS), SQL queries, correlation analysis, and more.

You can also go deeper on user behavior by utilizing heatmaps, scrollmaps, formulas, and the custom SQL insights.

Does PostHog have reports, dimensions, and other GA4 features?

Yes, PostHog has much of the same functionality as Google Analytics, but we use different terminology. Here’s a quick comparison of the two:

See our guide to PostHog for Google Analytics users for more help on making the switch.

Platform

When you choose PostHog, you get more than analytics.

Integrations

It's hard to import data into Google Analytics because:

1. The data type and format you're allowed to import is restrictive.
2. You either need to constantly upload CSV files manually, or set up an SFTP server to automatically do this for you.

In contrast, PostHog is built to be your single source of truth, so it's simple to import data from other sources using our built-in data warehouse, or send PostHog data to other tools using our realtime destinations.

Below is a comparison of some of the most popular apps – see our data pipeline and warehouse docs for a complete list of integrations.

Security and compliance

PostHog makes GDPR compliance easy by letting you choose where your data is hosted: EU or US.

Google also offers various privacy controls, but you can't choose where your data is stored – a meaningful concern for teams in regulated industries or those serving EU users.

When to choose PostHog vs Google Analytics 4

• Want an all-in-one developer platform that goes beyond web analytics with product analytics, session replay, feature flags, A/B testing, error tracking, and more – all in one place? Go with PostHog.
• Need deep integration with Google Ads, Search Console, and the broader Google Marketing Platform for marketing attribution and campaign reporting? GA4 is built for that.

Recommendations by team type

For engineering-led product teams

• PostHog – SQL access, MCP server, open-source codebase, error tracking, and LLM analytics. Everything a developer needs to understand users, ship features, and debug problems without leaving the platform.

For marketing and growth teams

• GA4 – Best-in-class integration with Google Ads, Search Console, and Google's attribution ecosystem. If your team lives in Google's marketing stack, GA4 is the natural fit.

For product managers and UX teams

• PostHog – Funnels, retention, session replay, and surveys in one place means PMs can answer behavioral questions and gather user feedback without stitching together multiple tools.

For B2B SaaS companies

• PostHog – Group analytics enables account-level analysis, so you can understand behavior at the company level, not just the individual user level.

For privacy-conscious and regulated organizations

• PostHog – EU hosting with data stored exclusively in the EU, HIPAA-readiness, cookieless tracking, and raw data access via the built-in data warehouse. GA4 doesn't offer HIPAA compliance or EU-only data residency.

For content and media sites

• GA4 – If your primary need is understanding content performance, audience demographics, and ad revenue attribution across a high-traffic publishing site, GA4's native integrations with Google AdSense, Ad Manager, and Search Console are hard to beat.

For early-stage startups

• PostHog – A single platform that covers analytics, replays, feature flags, and more from day one. The generous free tier includes 1 million events, 5,000 session replays, and 1 million feature flag requests per month. Startups can also qualify for $50k in free credits.

For enterprise teams

• Tied – GA360 offers unsampled reporting, 50-month data retention, and deep Google Marketing Platform integration for large marketing organizations. PostHog offers SSO enforcement, a BAA for HIPAA compliance, priority support, and a complete product development stack with transparent pricing.

Frequently asked questions

In the age of AI, though, we have a different kind of data that mashes aspects of both together. I'm talking about the LLM traces and generations your AI agent or agentic workflows produce as they get stuff done.

This data is crucial for seeing whether your agent is actually doing its job. It's also rich with insights about how your users are using AI features, what they're trying to do, and even how they feel about the whole interaction.

Think about all your interactions with an AI in the last week and all the subtle (or not so subtle) emotional signals embedded in them. By analyzing these signals in aggregate, AI product teams can discover usage patterns, outlier behaviors, and problem areas.

Usually, analysis of these signals would take a set of complex queries and calculations, but since we have all this data in PostHog and we deal with this sort of complexity all the time, we built it for you.

It's aptly named "Clustering." We probably should have called it "Agentic AI-driven magic AI unicorn insights agentic" but engineers aren't the best at naming AI things.

Here's how the whole pipeline works under the hood, with links to the code if you want to dig deeper. If you just want to see it in action, skip to the demo.

The complete clustering pipeline

Here's the overall flow from raw traces to clustered insights:

1. Ingest - Traces and generations land as PostHog events
2. Text representation - Convert each trace to a readable text format
3. Sample - Hourly sampling of N traces/generations
4. Summarize - LLM-powered structured summarization
5. Embed - Generate embedding vectors from summaries
6. Cluster - UMAP dimensionality reduction + HDBSCAN clustering
7. Label - An AI agent names and describes each cluster
8. Display - Clusters tab with scatter plot and distribution chart

Design considerations

Before diving into the steps, here are the main considerations we kept coming back to and the choices we made for each:

• Huge traces - Some traces are enormous and can't just be thrown at an LLM. Our answer: the uniform downsampling described below in step 1. We iteratively drop lines while preserving the overall structure, so even massive traces fit within context limits, although this is at the cost of some accuracy/quality.
• Keeping costs sane - Running LLM summarization on every single trace would cost a lot. So we sample a small random subset each hour, use GPT-4.1 nano for summarization (fast and cheap), and are planning to move to the OpenAI Batch API to optimize further, as well as some BYOK approaches for "on demand" steering and triggering of the whole pipeline.
• One-size-fits-all vs. custom summaries - It's hard to have a single general summary schema that works perfectly for every type of trace we might see. Ideally, users could steer or bring their own prompts and structured outputs. For now, we started with a general-purpose prompt and summary schema that works well across most use cases, but enabling user-defined summarization is a potential future improvement.
• No existing embeddings to lean on - PostHog doesn't already have a RAG system or embeddings index over all LLM analytics events. Rather than building that infrastructure first, we took a shortcut: convert traces to text, summarize them with an LLM, then embed the summaries. This gives us high-quality, semantically rich vectors without needing a full RAG pipeline - and the summaries are useful on their own as a feature.
• Zero-config by default, customizable when you need it - The Temporal workflows just run in the background. As you send traces to LLM Analytics, clustering will just work once there's enough data to sample from. No setup needed — but if you want more control, you can now create clustering jobs to steer what gets clustered (more on this below).
• User steering - We recently shipped clustering jobs, which let you define up to five independent clustering configurations per project. Each job can target a specific analysis level (traces or generations) and apply event filters to scope which data gets included — for example, you could create a job that only clusters traces from a specific model or a particular user segment. Jobs run automatically during the next scheduled cycle.

Step 1: From JSON blobs to readable text

Traces are ingested into LLM Analytics as normal PostHog events. They have their own loose schema around special $ai_* properties – covering everything from generations and spans to sessions – but also the flexibility of general PostHog events, which means they generally work with any other PostHog feature out of the box.

This gives us a blob of JSON with some expected $ai_ properties, and even within each property, the structure and format can vary wildly depending on the LLM provider, framework, or how users have instrumented their own agents.

So we need to figure out how to get from this bag of JSON to something a clustering algorithm can work with – we need numbers. And whenever you need numbers with LLMs, it often means you need embeddings. Our task is to figure out what embeddings to generate that will be useful downstream.

We can't just throw some massive JSON blob at an LLM and expect it to produce a great summary. It might be okay, but we can do better by putting ourselves in the shoes of our LLM: if I can create a general text representation where I myself can easily "read" a trace, then that'll also be a great input for summarization. We be context engineering like the AI hipsters we aspire to be.

So we built a process that renders each trace (or any event in it) as a clean, simple text representation – essentially an ASCII tree with line numbering. The idea here is just some simple text so you can "read" the trace, you know, as a human.

Here's what it looks like in the PostHog UI:

You can see the full text representation for this trace in this gist — it's a real example from one of my side projects (a daily factoids chatbot that got into a surprisingly deep conversation about mantis shrimp vision and digital signal processing). Point being, if its easier for me as a human to grok what's going on, the LLM should be able to do a decent job of summarizing it with less risk of getting side tracked by all the other gnarly metadata and structure in the raw JSON.

The line numbering (L001:, L002:) is important – it gives the downstream summarization LLM a way to reference specific parts of the trace, which makes the structured output much more useful.

You can see how this is generated in trace_formatter.py.

Handling huge traces

Some traces are just too big. We use uniform downsampling to shrink them – picking every Nth line from the body while preserving the header. The sampler notes what percentage of lines were kept, and the gaps in line numbers tell the LLM that content was omitted. Using the mantis shrimp trace from the gist, here's a snippet of what a downsampled version might look like:

L001: ----------------------------------------------------------
L002:
[SAMPLED VIEW: ~40% of 136 lines shown]
L003: AVAILABLE TOOLS: 1
L005:   web_search(max_results?: integer, query?: any)
L012: [1] SYSTEM
L018: Factoid text: The mantis shrimp can perceive 12-16 types
       of color receptors compared to humans' 3...
L035: [2] USER
L037: huh - explain this more to me
L041: [3] ASSISTANT
L045:   - web_search(query="mantis shrimp vision 12 vs 16...")
L049: [4] TOOL
L051: {"query": "mantis shrimp vision...", "results": [...]}
L059: [5] ASSISTANT
L061: It seems counterintuitive, right?...
L077: [6] USER
L079: any relation at all to something like DSP or FFT?
L101: [9] ASSISTANT
L103: That is a brilliant connection!...
L122: [10] USER
L124: oh interesting - k bye i love you
L130: [1] ASSISTANT
L132: Haha, well that escalated quickly! I love you too...
L136: **K bye!**

Notice the [SAMPLED VIEW: ~40% of 136 lines shown] header and the jumps in line numbers (e.g., L051 → L059, L079 → L101). The LLM can still follow the conversation flow – the structure and key turns are preserved, just with less detail in between. This works well in practice because the same context often gets passed back and forth within each step of a trace.

See how downsampling works in message_formatter.py.

Step 2: Summarization

Now that we have a readable text representation, we can summarize it. We sample N traces per hour (cost management – we're not summarizing everything) and send each text representation to an LLM for structured summarization.

The key word there is structured. Rather than asking for a free-text summary, we ask for a specific schema:

# Simplified summarization schema
# See: schema.py

class SummarizationResponse(BaseModel):
    title: str              # Short descriptive title
    flow_diagram: str       # ASCII flow of the trace steps
    summary_bullets: list[SummaryBullet]  # Key points with line refs
    interesting_notes: list[str]          # Anything unusual or notable

class SummaryBullet(BaseModel):
    bullet: str             # The summary point
    line_refs: list[str]    # e.g., ["L003", "L015"] - back-references

See the full summarization schema in schema.py

We use GPT-4.1 nano for this – it's fast, cheap, and the structured output mode means we get reliable, parseable results every time. The line references back to the text representation are particularly useful: they let the summary stay grounded in the actual trace data rather than hallucinating. You can read the actual prompts we use in system_detailed.djt and user.djt. If you have sensitive data in your traces, we made sure clustering works with privacy mode which controls what gets sent.

These summaries appear as $ai_trace_summary and $ai_generation_summary events in your project every hour. They also power the on-demand trace summarization feature you can use to quickly understand any individual trace or generation without reading through the full conversation.

Ideally we would enable users the ability to steer and customize this part of the flow – maybe you want a different summary schema, or you want to add specific questions for the LLM to answer in the summary. We're considering this for the future, but for now we started with a general-purpose schema that works decently across most use cases.

Why structured output matters

Structured output is better than a raw summary for one critical reason: downstream embedding quality. When we embed a title + flow diagram + specific bullets with line references, we get a much higher signal representation than embedding a wall of free text. The LLM has already done the work of extracting what matters.

Why not RAG? We considered building a full RAG system – chunking traces, building indices, retrieving at query time. But the complexity of doing that well at PostHog's scale (billions of events across thousands of teams) made us reach for a simpler approach first. Summarization-first means each trace becomes a small, self-contained artifact that's easy to embed and cluster. We may still build RAG for features like natural language search, but for clustering, this works well.

Step 3: Embedding

Once we have summaries, we format them back into plain text – title, flow diagram, bullets, and notes concatenated together, with line numbers stripped to reduce noise – and embed them using OpenAI's text-embedding-3-large model, giving us a 3,072-dimensional vector for each summary.

Why embed the enriched summary instead of the raw trace? Because the summary lives in a higher-level semantic space. Raw traces contain a lot of noise – token counts, model versions, repeated system prompts. The summary captures the intent and flow, which is exactly what we want our clusters to be organized around.

So now, for each trace or generation, we have 3,072 numbers. Time for the fun part.

Step 4: Clustering

This is where we lean on more "old school" traditional ML techniques. There are still several areas where LLMs haven't eaten the world: recommender engines, clustering, time series modeling, tabular model building, and getting well-calibrated numbers for regression problems. For all of these, you're still better off equipping your agent with the tools to formulate and run traditional algorithms on the data, rather than asking it to do the calculations directly.

Dimensionality reduction

We can't just cluster the raw 3,072-dimensional vectors – the curse of dimensionality would make distances meaningless. So we first need to reduce dimensions while preserving the important structure.

We run UMAP, an algorithm for reducing high-dimensional data while preserving local structure, twice with different goals:

3072-D embeddings
     ↓
┌────┴────┐
↓         ↓
100-D     2-D
(UMAP)   (UMAP)
↓         ↓
HDBSCAN   Scatter plot

• 3072 → 100 dimensions for clustering: min_dist=0.0 packs similar points tightly together, which is what HDBSCAN wants.
• 3072 → 2 dimensions for visualization: min_dist=0.1 keeps some visual separation so the scatter plot is readable.

HDBSCAN

UMAP's job was dimensionality reduction — compressing 3,072 dimensions into something workable while preserving structure. HDBSCAN is the algorithm that actually finds the clusters by scanning the reduced space for dense regions of similar points and grouping them together. For the actual clustering, we use HDBSCAN on the 100-D reduced embeddings:

# Simplified clustering pipeline
# See: clustering.py

import umap
import hdbscan
import numpy as np

def cluster_embeddings(embeddings: np.ndarray):
    """Run the full clustering pipeline."""

    # Normalize embeddings
    norms = np.linalg.norm(embeddings, axis=1, keepdims=True)
    normalized = embeddings / norms

    # UMAP for clustering (100-D)
    reducer_cluster = umap.UMAP(
        n_components=100,
        min_dist=0.0,
        metric="cosine",
    )
    embeddings_100d = reducer_cluster.fit_transform(normalized)

    # UMAP for visualization (2-D)
    reducer_viz = umap.UMAP(
        n_components=2,
        min_dist=0.1,
        metric="cosine",
    )
    embeddings_2d = reducer_viz.fit_transform(normalized)

    # HDBSCAN clustering on 100-D embeddings
    clusterer = hdbscan.HDBSCAN(
        cluster_selection_method="eom",
        min_cluster_size=5,
    )
    labels = clusterer.fit_predict(embeddings_100d)

    return labels, embeddings_2d

See the full UMAP + HDBSCAN pipeline in clustering.py

HDBSCAN has some nice properties for our use case:

• No need to pick k (number of clusters) in advance - it figures this out automatically based on density.
• Noise cluster - items that don't fit any cluster get assigned to cluster -1. These "outliers" can be interesting edge cases you might want to explore.
• cluster_selection_method="eom" (Excess of Mass) tends to produce more granular, interpretable clusters compared to the "leaf" method.

After this step, every trace or generation has an integer cluster ID. But an integer doesn't tell you much – we need to make these meaningful.

Step 5: The labeling agent

Now we have clusters with integer IDs. Cluster 0 has 47 traces, cluster 1 has 23, cluster -1 (noise) has 8. Not exactly actionable.

This is where we bring AI back in – specifically, a LangGraph ReAct agent powered by GPT-5.2. Its job is to explore the clusters and come up with meaningful labels and descriptions.

You can read the agent's system prompt in prompts.py.

The agent has access to 8 tools:

The agent starts with a bulk-labeling phase: it calls get_all_clusters_with_sample_titles for an overview, then uses bulk_set_labels to assign initial labels to every cluster. This guarantees coverage — if the agent hits a token limit or error later, no cluster is left unlabeled.

Then it moves to refinement. For clusters that seem ambiguous, it drills deeper with get_cluster_trace_titles and get_trace_details, then calls set_cluster_label to update individual labels. Finally, it calls finalize_labels to signal completion.

See the agent tools and prompts in labeling_agent/

Step 6: Orchestration with Temporal

All of this needs to run reliably across thousands of teams. We use Temporal workflows to orchestrate the pipeline.

A daily coordinator workflow discovers eligible teams (those with enough recent trace data), then spawns child workflows in batches – up to 4 concurrent workflows at a time to manage load. These workflows:

1. Fetch recent embeddings
2. Run clustering pipeline (UMAP + HDBSCAN)
3. Run labeling agent
4. Emit cluster events

See the orchestration workflow in coordinator.py

The output of each child workflow is a set of $ai_trace_clusters and $ai_generation_clusters events. These are standard PostHog events, which means the clusters tab in the UI is just querying events like everything else in PostHog.

A concrete example

Here's what you're seeing in the demo:

• Scatter plot - Each dot is a trace, positioned using the 2-D UMAP coordinates. Colors represent cluster assignments. You can immediately see which groups of traces are similar and how they relate spatially.
• Cluster distribution - A bar chart showing how many traces landed in each cluster, with the agent-generated labels. This gives you a quick sense of what your users are actually doing.
• Drill-down - Click into any cluster to see the individual traces, their summaries, and the full details. This is where you find the patterns - maybe 40% of your traces are "user asking for refund status" and you didn't even know.

The noise cluster (labeled "Outliers") often contains the most interesting one-off traces – edge cases, unusual workflows, or bugs that don't fit any pattern. Pair this with evaluations (our LLM-as-a-judge feature) to automatically score the quality of generations within each cluster.

Steering with clustering jobs

Since launching this pipeline, we've added clustering jobs — a way to create independent clustering configurations so you can steer what gets analyzed. Instead of one big clustering run across all your data, you can define up to five jobs per project, each with its own analysis level (traces or generations) and event filters.

For example, you might create separate jobs for:

• Traces from your production GPT-4o agent
• Generations from your RAG pipeline
• Only traces where a specific custom property matches (e.g., $ai_model = "claude-sonnet-4-20250514")

Each job runs automatically during the next scheduled cycle, and the run selector on the Clusters page shows which job produced each run. See the clustering jobs docs for the full setup guide.

Try it now

If you're already using LLM Analytics in PostHog, clustering runs automatically – no configuration needed. Want more control? Set up clustering jobs to define exactly what gets clustered. If you're not using LLM Analytics yet, you can get started in minutes with SDKs for OpenAI, Anthropic, LangChain, Vercel AI, and many more.

Further reading

• Clusters documentation - Setup guide and feature overview
• Custom properties - Attach your own metadata to traces for richer clustering
• Privacy mode - Control what trace data gets sent to PostHog
• Feedback - Leave feedback directly on the clusters docs page, email us at team-llm-analytics@posthog.com, or start a thread in the community

The good news: there are solid options. This guide covers the best HIPAA-compliant analytics tools available right now, whether you need a Business Associate Agreement (BAA), a self-hosted deployment, or both.

What is HIPAA?

Passed in 1996, HIPAA (Health Insurance Portability and Accountability Act) defines the legal requirements for securing and handling health information, and the severe penalties for failing to do so.

Data protected under HIPAA is called Protected Health Information (PHI), or ePHI if it is digitized. It includes any data that can be used to identify the past, current or future health status of an individual.

This includes test results and diagnoses, but also birthdays, ethnicity, gender and other information. Even an IP address can be considered ePHI under HIPAA.

While similar in some respects to the EU's General Data Protection Regulation (GDPR), HIPAA applies specifically to companies handling the PHI of US-based customers. Companies that also need to comply with the GDPR should see our guide to GDPR-compliant analytics.

There are two ways to be HIPAA-compliant while using analytics tools:

1. Self-host your analytics, so data remains totally within your control.
2. Sign a Business Associate Agreement (BAA) with a third-party analytics tool.

What is a Business Associate Agreement (BAA)?

Some services enable HIPAA compliance through the creation of a Business Associate Agreement (US Department of Health). This is a contract with a service provider to ensure that they are jointly compliant and liable for services they provide.

It's worth noting that, because BAAs expose third-parties to increased risk and scrutiny, they are often an expensive option and/or require users to purchase a higher tier of license.

Some analytics tools, such as Google Analytics, don't offer BAAs and are therefore not HIPAA-compliant.

The best HIPAA-compliant analytics tools

1. PostHog

PostHog is a developer platform that combines product analytics, web analytics, session replay, feature flags, experiments, error tracking, user surveys, and a lot more.

It gives you every tool you need to understand user behavior and, unlike typical analytics tools that rely on third-party integrations, all these tools work together seamlessly.

Being an all-in-one platform has two further benefits:

1. PostHog can replace multiple products – e.g. Mixpanel for product analytics, LaunchDarkly for feature management, Sentry for error tracking, etc.
2. You only need to sign one BAA to get all these tools, reducing legal complexity and risk.

PostHog and HIPAA compliance

• Self-hostable: ✔
• BAA available: ✔
• BAA plan: Platform packages – $250/mo

A BAA is available on PostHog's platform packages, which also includes priority support and generous free usage limits for all tools – e.g. 1 million free analytics events every month. You can also self-host the open-source edition of PostHog, but this isn't recommended as it's provided without guarantee or support.

2. Mixpanel

Mixpanel is a product analytics tool with genuinely strong funnel and behavioral analysis – useful for healthcare teams tracking complex user journeys. It added session replay and feature flags in late 2025, which means more functionality under a single BAA than before.

That said, surveys and error tracking still aren't included natively, so you'd likely need additional tools – and separate BAAs – for those.

Mixpanel and HIPAA compliance

• Self-hostable: ✖
• BAA available: ✔
• BAA plan: Contact sales for pricing

A BAA is available on Mixpanel's Enterprise plan.

See also: The most popular Mixpanel alternatives

3. Countly

Countly is an analytics platform for mobile, web, and desktop applications that also offers add-ons for remote configuration, A/B testing, and user surveys. Support for app crash and error reports, and push notifications, makes it particularly well-suited to mobile app analytics.

Countly and HIPAA compliance

• Self-hostable: ✔
• BAA available: ✖

Countly doesn't offer a BAA for HIPAA compliance on its hosted cloud, but it does offer the option to either:

1. Self-host the product on your own.
2. Deploy it to a private cloud managed by Countly.

This makes Countly a good option if you'd prefer to self-host your analytics.

4. Freshpaint

Freshpaint isn't an analytics tool per se, it's more of an analytics event tracker and customer data platform (CDP) that's specifically designed for healthcare companies.

Freshpaint sits between data sources (e.g. data warehouses) and third-party data destinations and ensures no PHI is passed between them. This means you can continue to use non-HIPAA compliant tools, such as Google Analytics, safe in the knowledge you're not accidentally passing PHI into them.

Freshpaint and HIPAA compliance

• Self-hostable: ✖
• BAA available: ✔
• BAA plan: Contact sales for pricing

Freshpaint is a cloud-only product specifically designed for healthcare companies, so it offers a BAA (available on the Compliance plan, which requires a custom quote).

5. Piwik PRO

Piwik PRO is a commercial analytics and customer data platform spun out of the open-source analytics tool, Matomo. As such, it's more a web analytics tool than other options in this list, though you can use it on mobile and web apps. Because it's based on Europe, Piwik PRO is popular among companies also seeking GDPR compliance – it has a built-in compliance manager to assist with this, too.

PiwikPRO and HIPAA compliance

• Self-hostable: ✔
• BAA available: ✔
• BAA plan: Contact sales for pricing

PiwikPRO offers HIPAA compliance as part of its PRO Enterprise plan, either by signing a BAA or by self-hosting, giving you maximum flexibility.

6. Amplitude

Amplitude sits somewhere between PostHog and Mixpanel. It's a product analytics tool at its core, but also has extra features such as session replay, feature flags, A/B testing, and Guides & Surveys. It also has anomaly detection, which will automatically flag when certain metrics fall outside expected trends, and creating insights based on natural language questions like "signups in the last 30 days."

Amplitude and HIPAA compliance

• Self-hostable: ✖
• BAA available: ✔
• BAA plan: Contact sales for pricing

Amplitude offers a BAA on its Enterprise plan, which includes advanced security features and custom data governance controls. You can also use its product analytics tool on top of a Snowflake data warehouse, which may be an option for HIPAA compliance if you're already storing analytics data in Snowflake.

See also: The most popular Amplitude alternatives

Which HIPAA-compliant analytics tool should you choose?

• Want one platform for product analytics, session replay, feature flags, and more – with a single BAA covering everything? PostHog
• Need a focused product analytics tool with a BAA and strong funnel analysis? Mixpanel
• Want to self-host your analytics without signing a BAA? Countly
• Already using non-HIPAA-compliant tools and need a PHI firewall in front of them? Freshpaint
• Need web analytics and a CDP with both BAA and self-hosting options? Piwik PRO
• Want retention-focused product analytics with a BAA and warehouse-native options? Amplitude

FAQ

This guide only includes open-source analytics tools that:

1. Have built-in analysis views. This includes end-to-end analytics, and dashboard tools that connect to existing data sources, but not data pipeline platforms.

2. Are actively maintained. This is subjectively judged based on recently merged pull requests, commits to their repos, and the responsiveness of the maintainer to review requests.

3. Are free to self-host. Tools that are "open core" but don't offer a free, self-hostable deployment option are not included.

The best open source analytics tools

You want visibility into what users are doing – but you also want control over where that data lives. Open source analytics tools give you both: insights without handing your data to a third party.

This guide covers the best open source analytics tools you can self-host, from full product analytics platforms to lightweight web analytics and BI tools.

Looking for cloud product analytics tools instead? See our guide to the best product analytics tools for startups.

1. PostHog

What is PostHog?

• Created: 2020
• License: MIT
• GitHub Stars: 31.9k

PostHog is an all-in-one platform that combines product analytics with several other developer-focused tools, such as web analytics, session replay, feature flags, experiments, error tracking, surveys, logs, and more.

Beyond the core product analytics features, it also supports creating custom insights using SQL, and offers a built-in data warehouse so you can import data from external sources like Stripe, Hubspot, and Zendesk.

Who should use PostHog?

Developers and startups who need an in-depth understanding of what users are doing in their app, or want to track the complete journey from their marketing website through to their app. There's a generous free tier, which means ~90% of users use PostHog for free.

Pros and cons

FAQ

2. Matomo

What is Matomo?

• Created: 2007
• License: AGPLv3
• GitHub Stars: 21.3k

Matomo is an open-source platform for website analytics. The user experience is closely modeled on the previous version of Google Analytics, known as Universal Analytics, which many people prefer to Google's current offering.

It looks little dated compared to some tools in this list, but it's a mature and reliable platform used by many large government and non-profit organizations.

Who should use Matomo?

Companies that want comprehensive website and marketing analytics without the privacy implications of using Google Analytics.

Pros and cons

FAQ

3. Metabase

What is Metabase?

• Created: 2015
• GitHub Stars: 46.3k
• License: AGPLv3

Metabase is one of the most popular open-source BI tools. Metabase's visual query builder enables you to create shareable dashboards in just a few minutes with a drag-and-drop interface, and there's a native SQL editor for advanced users.

Metabase is a great tool for business and revenue reporting. It requires more technical knowledge than either PostHog or Matomo, but it's powerful in the right hands.

Who should use Metabase?

Companies that want to analyze product and company data that's already stored in data warehouses and have a reasonable degree of internal knowledge of SQL.

Pros and cons

FAQ

4. Plausible Analytics