A more scalable alternative to PostHog: Statsig

Teams running hundreds of experiments hit a wall with PostHog's pricing model. You're paying separately for analytics events, feature flags, session replays, and A/B tests - costs that multiply quickly as you scale. The platform works great for startups, but enterprise teams need something built for high-volume experimentation.

Statsig takes a different approach. The platform bundles all experimentation tools under unified pricing while delivering the statistical rigor that companies like OpenAI and Notion require. Let's dig into what makes these platforms different and why the choice matters for your team.

Company backgrounds and platform overview

Statsig launched in 2020 when ex-Facebook engineers built an experimentation platform without legacy tool bloat. The team prioritized speed and developer experience over enterprise sales processes. Today, Statsig processes over 1 trillion events daily for companies like OpenAI, Notion, and Figma.

PostHog emerged the same year as an open-source analytics platform for engineers who wanted self-hosted options. The platform attracted developers on Reddit by offering competitive pricing and data ownership. Their Product OS bundles analytics, feature flags, and session replay into one system.

The philosophical differences shape each platform's approach. Statsig optimizes for statistical rigor and experiment velocity - every feature supports faster, more reliable testing. PostHog emphasizes flexibility and self-service deployment, letting teams customize their analytics stack. These aren't just marketing differences; they fundamentally change how you work with data.

Statsig serves enterprise teams that need sophisticated experimentation at scale. Think companies running hundreds of concurrent tests with complex interaction effects. PostHog appeals to developers and startups that prioritize data control and transparent pricing over advanced statistical methods.

"Statsig's infrastructure and experimentation workflows have been crucial in helping us scale to hundreds of experiments across hundreds of millions of users."

Paul Ellwood, Data Engineering, OpenAI

Feature and capability deep dive

Experimentation and feature management

Here's where the platforms diverge dramatically. Statsig's experimentation engine handles complex testing scenarios that PostHog's basic A/B testing can't match:

• Sequential testing stops experiments early when results are clear

• CUPED variance reduction delivers 30-50% faster results

• Stratified sampling ensures balanced user distribution

• Automated rollback kills features when metrics tank

PostHog bundles experimentation with feature flags but lacks these advanced statistical methods. You get basic A/B testing and percentage rollouts - fine for simple tests, insufficient for sophisticated product teams.

The pricing structure reveals the core difference. PostHog charges for feature flag requests after 1 million monthly uses. Statsig includes unlimited flags at no extra cost. This isn't just about money; it's about experimentation velocity. When flag checks cost money, teams hesitate to instrument deeply. When they're free, you instrument everything and learn faster.

Statsig automatically detects metric regressions and rolls back features - a capability PostHog doesn't offer. This matters when you're running dozens of experiments simultaneously. One bad feature can tank your core metrics while you're asleep. Statsig's guardrails prevent disasters; PostHog requires manual monitoring.

Analytics and data infrastructure

Both platforms process billions of events, but their architectures tell different stories. Statsig handles over 1 trillion events daily with 99.99% uptime across all services. The infrastructure scales horizontally without performance degradation. PostHog's ClickHouse-based system focuses on real-time analysis but processes fewer events at enterprise scale.

PostHog's open-source model attracts engineers who want self-hosted analytics. You control your data completely but manage infrastructure yourself. The tradeoff: operational overhead grows with data volume. Statsig offers both cloud and warehouse-native deployment options. You can run analytics in Snowflake, BigQuery, or Databricks while Statsig handles the processing.

The warehouse-native approach transforms data workflows. Instead of shipping events to another vendor's cloud, your data stays in your warehouse. Statsig runs computations there, maintaining security while delivering enterprise-grade analytics. PostHog requires choosing between self-hosting complexity or sending data to their cloud - no middle ground exists.

Pricing models and cost analysis

Free tier comparison

Let's start with what you get for free. Statsig bundles unlimited feature flags, 50K session replays, and full experimentation tools in its free tier. No seat limits. No project restrictions. Just pure product development.

PostHog's free tier looks generous at first: 1M analytics events, 5K session replays, and 1M feature flag requests. But each product counts separately against your limits. Hit any cap and you're pushed to paid pricing. Teams on Reddit's r/SaaS consistently mention this surprise when scaling past hobby projects.

The practical impact: Statsig lets you run experiments, deploy flags, and analyze results without watching meters. PostHog requires careful monitoring across products. You're constantly calculating whether that new feature flag will push you over the limit.

Enterprise cost scaling

At scale, the pricing models diverge dramatically. Consider a typical SaaS with 500K MAU generating 20M events monthly:

Statsig pricing: ~$500/month for everything

• Unlimited feature flags

• Full experimentation suite

• Session replay included

• All analytics features

PostHog pricing: ~$1,500/month split across:

• Analytics events: $450

• Feature flags: $400

• Session replays: $350

• Experiments: $300

Statsig's pricing analysis shows PostHog costs 2-3x more at enterprise volumes. The gap widens with heavy feature flag usage since PostHog charges per request while Statsig includes unlimited flags. Teams migrating from PostHog report 50-70% cost savings after switching to Statsig's unified model.

Decision factors and implementation considerations

Time-to-value and onboarding

Getting started quickly matters when choosing analytics platforms. Statsig ships with 30+ SDKs across every major programming language. Basic feature flag implementation takes under 10 minutes. The platform's one-click SQL transparency lets you verify calculations instantly - no black box algorithms.

PostHog's autocapture functionality tracks frontend events automatically without manual instrumentation. Sounds great until you realize autocapture creates noise. You'll spend weeks filtering out irrelevant events. Self-hosted deployments add another layer: configuring data pipelines, managing infrastructure, debugging ClickHouse queries.

The onboarding difference becomes clear in practice. Statsig customers report productive experiments within days. PostHog users spend weeks tuning autocapture rules and building dashboards before running their first meaningful test.

Support and enterprise readiness

Support quality determines implementation success. Statsig provides dedicated customer data scientists who help design experiments and validate statistical approaches. Engineering teams get direct Slack access to Statsig engineers. Sometimes the CEO responds directly to technical questions.

As Sumeet Marwaha, Head of Data at Brex, noted: "Our engineers are significantly happier using Statsig. They no longer deal with uncertainty and debugging frustrations."

PostHog structures support differently:

• Free tier users rely on community forums

• Enterprise customers access paid support plans

• The product team focuses on user interviews over hands-on guidance

This isn't just about response times. Statsig's support team includes data scientists who understand experimentation theory. They'll spot issues with your test design before you waste weeks on invalid results. PostHog's support handles technical issues but won't review your experimental methodology.

Data governance and deployment flexibility

Both platforms offer warehouse-native deployment for teams with strict data requirements. But the implementation differs significantly.

Statsig supports:

• Snowflake

• BigQuery

• Redshift

• Databricks

• Athena

PostHog's Product OS builds exclusively on ClickHouse for real-time analysis. This lock-in becomes problematic if your organization standardizes on different infrastructure.

The key advantage: Statsig maintains dual deployment models. Start with hosted infrastructure for quick wins. Migrate to warehouse-native when compliance requires it. The transition happens seamlessly - same features, same workflows, different data location. PostHog forces an upfront choice between cloud-hosted or self-hosted, making future migrations complex and risky.

Bottom line: why is Statsig a viable alternative to PostHog?

Statsig delivers enterprise-grade experimentation at roughly half PostHog's cost. The unified pricing model eliminates surprise bills from separate product charges. Companies processing over 100K MAU typically save thousands monthly by switching from PostHog's per-product pricing to Statsig's bundled approach.

The technical advantages compound at scale. Unlimited feature flags mean you instrument everything without budget anxiety. Advanced statistical methods like CUPED and sequential testing deliver results 30-50% faster than basic A/B tests. Automated metric monitoring prevents feature disasters before they impact users.

OpenAI chose Statsig's warehouse-native architecture to scale experimentation across hundreds of millions of users. The approach maintains data sovereignty while leveraging Statsig's statistical engine. You get enterprise experimentation without sacrificing control.

PostHog works well for teams prioritizing self-hosted analytics and basic feature management. But once you need sophisticated experimentation - interaction detection, variance reduction, automated rollbacks - the platform shows its limits. You'll either accept basic testing or hire data scientists to build custom solutions.

"The biggest benefit is having experimentation, feature flags, and analytics in one unified platform. It removes complexity and accelerates decision-making," said Sumeet Marwaha, Head of Data at Brex.

The choice ultimately depends on your experimentation maturity. Teams running occasional A/B tests might find PostHog sufficient. But if you're building a culture of continuous experimentation - where every feature gets tested, every metric gets monitored, and decisions follow data - Statsig provides the infrastructure to scale without breaking budgets.

Closing thoughts

Choosing between Statsig and PostHog comes down to your experimentation ambitions. PostHog offers a solid foundation for teams getting started with analytics and basic testing. But as your testing velocity increases and statistical rigor becomes critical, Statsig's purpose-built platform delivers the capabilities enterprise teams need.

The cost savings are real - most teams cut their experimentation spend by 50-70% after switching. But the bigger win is velocity. When feature flags are free and experiments run faster, product teams ship better features more frequently.

Want to dig deeper? Check out Statsig's migration guide or explore their detailed pricing calculator to model your specific use case.

Hope you find this useful!