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A faster alternative to PostHog: Statsig
PostHog's all-in-one approach to product analytics sounds great until you hit scale. Teams start noticing lag in their dashboards, experiments take forever to reach significance, and the bills grow unpredictably across five different pricing meters. The promise of open-source flexibility turns into infrastructure headaches and engineering bottlenecks.
Statsig emerged from a different philosophy: build the fastest experimentation platform possible, then expand from there. The founders left Facebook's experimentation team to solve a specific problem - existing tools couldn't handle internet-scale traffic without compromising on statistical rigor or speed. Today, they process over 1 trillion events daily with sub-millisecond latency, making them a compelling alternative for teams outgrowing PostHog.
Company backgrounds and platform overview
Both companies launched in 2020 but took fundamentally different paths. Statsig's founders left Facebook to build what they called the fastest experimentation platform available - a bold claim they've backed up by processing over 1 trillion events daily for OpenAI, Notion, and Figma. PostHog started as an open-source analytics alternative, attracting developers who wanted to own their data and escape vendor lock-in.
The architectural choices reveal each company's priorities. Statsig unified experimentation, feature flags, analytics, and session replay through a single data pipeline that maintains 99.99% uptime while handling 2.5 billion unique monthly experiment subjects. This integrated approach means faster query times and consistent metrics across all features.
PostHog assembled separate products - analytics, replay, feature flags, experiments - each with independent infrastructure and pricing. They built on ClickHouse for analytics performance but this modularity creates challenges. Reddit discussions highlight how their "unconventional business model" of competing across multiple categories while maintaining the lowest prices creates confusion about actual costs.
The open-source strategy initially attracted engineering teams who wanted control. PostHog offers both a community edition on GitHub and a paid cloud version. But as teams scale, the trade-offs become apparent: self-hosting requires significant DevOps resources, while their cloud offering means trusting a third party with your event data.
Statsig focused on statistical rigor and performance from the start. They built warehouse-native capabilities that let data stay in your Snowflake, BigQuery, or Databricks instance - a crucial requirement for enterprise teams. The platform supports both Bayesian and Frequentist methodologies, CUPED variance reduction, and automated heterogeneous effect detection. These aren't just checkboxes; they're the features that convinced sophisticated experimentation teams at Atlassian and Microsoft to switch.
Feature and capability deep dive
Experimentation and A/B testing capabilities
The experimentation gap between these platforms shows up immediately in practice. Statsig provides sequential testing, CUPED variance reduction, and stratified sampling - techniques that reduce experiment runtime by 30-50%. These aren't academic features; they translate directly to faster decision-making.
PostHog offers basic A/B testing with standard statistical methods. That works fine for simple tests, but sophisticated analysis requires more. Statsig automatically detects interaction effects between experiments and applies Bonferroni correction for multiple comparisons. When you're running hundreds of experiments simultaneously like OpenAI does, these safeguards prevent false positives from derailing your product strategy.
Both platforms connect feature flags to experiments, but the implementation philosophy differs dramatically. Statsig includes unlimited free feature flags with sub-millisecond evaluation and automatic rollbacks when metrics breach thresholds. You set guardrail metrics once, and the system protects you across all experiments.
PostHog charges for flag requests after 1 million monthly uses - a limit many teams hit within weeks. As Paul Ellwood from OpenAI notes: "Statsig's experimentation capabilities stand apart from other platforms we've evaluated. Statsig's infrastructure and experimentation workflows have been crucial in helping us scale to hundreds of experiments across hundreds of millions of users."
Analytics and data infrastructure
Data architecture determines everything else. Statsig's warehouse-native deployment runs directly in your existing data warehouse - Snowflake, BigQuery, or Databricks. Your data never moves, which eliminates security concerns and data silos. You get full analytics power without the typical ETL complexity.
PostHog's ClickHouse infrastructure delivers solid performance but requires data replication. Some users find this complex for large-scale implementations. The replication lag can cause metric discrepancies between your source of truth and PostHog's analytics.
The platforms handle metrics calculation differently too. Statsig provides one-click SQL visibility for every calculation, metric definition, and statistical test. You can verify exactly how conversion rates are computed or why an experiment showed certain results. This transparency builds trust with data teams who've been burned by black-box platforms.
PostHog offers SQL access but requires manual query construction for advanced analysis. That flexibility appeals to technical users but creates bottlenecks when product managers need quick answers.
Performance at scale reveals the real difference. Statsig processes those 1 trillion daily events with consistent sub-second query times. This isn't theoretical capacity - it's what they deliver for Microsoft and OpenAI in production. PostHog works well for smaller volumes but requires careful optimization beyond 100 million monthly events. Several Reddit threads document teams hitting performance walls and needing to redesign their implementation.
Pricing models and cost analysis
Free tier comparison
The free tier reveals each company's business model. Statsig bundles everything: unlimited feature flags, 50,000 session replays, and full experimentation capabilities. You get the complete platform to test before paying.
PostHog's free tier seems generous at first glance. But the limitations hit quickly:
5,000 session replays (10x less than Statsig)
Feature flags count against your usage
Each product module has separate limits
Experiments require paid analytics events
One Reddit user noted PostHog "seems too good to be true" - and they're right. The initial costs look low because you're only seeing one piece of the puzzle.
Enterprise scaling costs
Real costs emerge at scale. At 10 million monthly events, Statsig costs 50-70% less than PostHog when you factor in all features. The gap widens because PostHog charges for:
Analytics events
Feature flag requests
Session replays
Experiment participants
Data exports
Statsig's unified pricing means you pay for analytics events and replays. Period. Feature flags remain free at any volume - crucial when you're evaluating millions of flags daily.
Volume discounts amplify the difference. Statsig offers transparent enterprise pricing starting at 200K MAU with discounts reaching 50% at higher volumes. Their pricing calculator shows exact costs. PostHog's pricing stays opaque, with multiple Reddit discussions highlighting confusion about actual enterprise costs.
Don Browning from SoundCloud explained their decision: "We evaluated Optimizely, LaunchDarkly, Split, and Eppo, but ultimately selected Statsig due to its comprehensive end-to-end integration. We wanted a complete solution rather than a partial one, including everything from the stats engine to data ingestion."
Decision factors and implementation considerations
Developer experience and time-to-value
Speed to first experiment matters more than feature count. Statsig's 30+ SDKs ship with edge computing support and <1ms evaluation latency. Teams typically launch their first experiment within days, not weeks.
The SDK design philosophy differs too. Statsig's SDKs handle:
Automatic retries and fallbacks
Local evaluation for zero latency
Graceful degradation during outages
Built-in exposure logging
PostHog's open-source approach offers customization flexibility but demands engineering resources. You control everything, which means you're responsible for everything. Users praise the flexibility but acknowledge the engineering overhead for production deployments.
Support and documentation quality
Support quality directly impacts velocity. Statsig provides dedicated customer data scientists who help optimize experiment design and statistical analysis. This isn't just technical support - it's strategic guidance that helped Brex reduce experimentation setup time by 50%.
Sumeet Marwaha from Brex captured the impact: "Our engineers are significantly happier using Statsig. They no longer deal with uncertainty and debugging frustrations."
PostHog relies on community support and self-service documentation. The open-source community provides helpful answers, but response times vary. Product managers report complexity when implementing features without engineering support.
Data governance and security requirements
Your compliance requirements might make this decision for you. Statsig's warehouse-native deployment keeps sensitive data within your existing Snowflake, BigQuery, or Databricks instance. You maintain complete control while getting full analytics capabilities. This approach satisfied security reviews at Microsoft and Atlassian.
PostHog offers two paths:
Self-hosted: Complete data control but you manage infrastructure, updates, and scaling
Cloud: Simplified operations but your data lives in PostHog's infrastructure
The self-hosted option sounds appealing until you calculate the true cost of maintaining production infrastructure. Cloud simplifies operations but may not pass enterprise security reviews.
Team composition and technical expertise
Platform choice depends on who actually uses these tools. Statsig enables non-technical users to create experiments and analyze results independently. One-third of Statsig dashboards are built by PMs and designers - not engineers. The interface abstracts complexity without hiding important details.
PostHog assumes technical proficiency across teams. Engineers love the flexibility and control. But that same flexibility creates bottlenecks when PMs need developer help for basic tasks. Reddit discussions consistently highlight this trade-off between power and accessibility.
Bottom line: why is Statsig a viable alternative to PostHog?
Statsig handles over 1 trillion events daily while maintaining 99.99% uptime - scale that matches or exceeds any analytics platform. But raw capacity only matters if it translates to actual performance. OpenAI, Notion, and Brex trust Statsig's infrastructure because it delivers consistent sub-second query times regardless of load.
The unified platform approach solves a real problem. Instead of managing separate tools for analytics, experiments, and feature flags, everything works together. Notion scaled from single-digit to over 300 experiments per quarter because the platform removed operational friction. Sumeet Marwaha from Brex put it simply: "Having experimentation, feature flags, and analytics in one unified platform removes complexity and accelerates decision-making."
Cost advantages compound at scale. Statsig remains consistently 50% lower than PostHog across all product categories. The unlimited free feature flags eliminate a major hidden cost - PostHog's per-request pricing can explode your bill overnight.
Warehouse-native deployment provides unique advantages for security-conscious teams. Running directly in Snowflake, BigQuery, or Databricks means your data never leaves your control. PostHog's self-hosted option requires managing infrastructure; Statsig leverages your existing data stack.
Statistical sophistication comes standard. CUPED variance reduction, sequential testing, and automated interaction detection aren't enterprise add-ons - every customer gets the same advanced capabilities powering OpenAI's experiments. These features reduce experiment runtime by 30-50%, accelerating your entire product development cycle.
PostHog users note the platform can feel "overwhelming initially" due to its extensive feature set. Statsig's focused approach - start with experimentation, expand from there - creates a clearer path to value.
Closing thoughts
Choosing between Statsig and PostHog comes down to your priorities. If you need maximum flexibility and have engineering resources to spare, PostHog's open-source approach offers compelling customization options. But if you're looking for speed - both in terms of platform performance and time to value - Statsig delivers a more focused solution.
The best experimentation platforms disappear into your workflow. They process massive scale without lag, provide trustworthy results without complexity, and help teams ship better products faster. For teams that have outgrown PostHog's performance limits or pricing model, Statsig offers a proven alternative that scales with your ambitions.
Want to dig deeper? Check out Statsig's migration guide or explore their public pricing calculator to see exact costs for your use case. You can also read detailed comparisons of experimentation platforms and feature flag costs to make an informed decision.
Hope you find this useful!