A more affordable alternative to PostHog: Statsig

Feature flags and experimentation platforms have become critical infrastructure for modern software teams. Yet many companies find themselves paying tens of thousands monthly for tools that bundle features they don't need or charge for basic functionality like flag checks.

The choice between platforms often comes down to a simple trade-off: do you want sophisticated experimentation capabilities or do you need a jack-of-all-trades product suite? This comparison between Statsig and PostHog examines how that trade-off plays out in practice - from pricing models to statistical methods to warehouse deployment options.

Company backgrounds and platform overview

Statsig emerged in 2020 when a team of engineers decided to build the fastest experimentation platform on the market. They rejected the bloated interfaces and gatekeeping that plagued legacy tools. Their approach was simple: create a developer-first platform that could handle trillions of events without breaking a sweat.

PostHog launched that same year but took a different path. They started with open-source product analytics, then gradually expanded into feature flags and experimentation. Their Product OS now bundles multiple tools under one roof - analytics, session replay, feature flags, and more.

Both platforms serve technical teams but attract different segments. Statsig powers experimentation at companies like OpenAI and Notion - organizations that need warehouse-native deployment and sophisticated statistical methods. PostHog appeals to startups and mid-market teams who want self-serve simplicity and the option to peek under the hood.

Platform philosophies and target markets

Statsig built for technical depth from the start. Teams can deploy directly in their data warehouse or use Statsig's hosted infrastructure. This flexibility becomes crucial when you're processing 2.3 million events per second and can't afford latency spikes.

The platform's architecture reflects lessons learned at Facebook and Uber - companies where experimentation failures have real consequences. Every design decision prioritizes speed and reliability over feature breadth. As Paul Ellwood from OpenAI notes: "Statsig's infrastructure and experimentation workflows have been crucial in helping us scale to hundreds of experiments across hundreds of millions of users."

PostHog embraces radical transparency through open source. Developers can inspect code, self-host when needed, and customize freely. Their product suite includes analytics, session replay, and feature flags accessible through a generous free tier. This approach works well for teams that value control and transparency over raw performance.

Feature and capability deep dive

Experimentation and testing capabilities

Statistical rigor separates professional experimentation platforms from basic A/B testing tools. Here's where the platforms diverge dramatically.

Statsig implements several advanced techniques:

• CUPED variance reduction for detecting smaller effects

• Sequential testing with always-valid p-values

• Both Bayesian and Frequentist methodologies

• Stratified sampling for marketplace experiments

• Switchback tests for time-sensitive features

PostHog offers basic A/B tests integrated with their analytics. These work fine for simple split tests - changing button colors or testing headlines. But you won't find advanced variance reduction or sequential testing in their toolkit.

The difference matters when you're running hundreds of experiments. Statsig's CUPED can reduce sample sizes by 30-50%, letting teams ship features weeks faster. The platform also provides automatic rollbacks when metrics breach thresholds, protecting against harmful changes slipping through.

Analytics and developer experience

Scale reveals architectural differences between platforms. Statsig processes 1+ trillion events daily while maintaining sub-millisecond latency. Their event ingestion pipeline handles bursts without dropping data or degrading performance.

PostHog's autocapture functionality automatically tracks user interactions - convenient for getting started but potentially overwhelming at scale. Every click, scroll, and hover generates an event. Teams often spend weeks filtering noise from signal.

Both platforms provide SDKs across languages, but implementation philosophy differs:

Statsig's approach:

• 30+ lightweight SDKs optimized for minimal overhead

• Client SDKs under 15KB gzipped

• Server SDKs with local evaluation for zero-latency decisions

• Direct warehouse integration for existing data pipelines

PostHog's approach:

• Heavy focus on JavaScript autocapture

• Larger SDK footprint due to bundled functionality

• API-based flag evaluation adding network latency

• Self-hosting option for data control

The biggest architectural difference remains warehouse-native deployment. Statsig runs directly inside Snowflake, BigQuery, or Databricks - your data never leaves your infrastructure. PostHog primarily operates as a hosted solution, though self-hosting remains an option. This distinction becomes critical for enterprises with strict data governance requirements or substantial warehouse investments.

Pricing models and cost analysis

Free tier comparison

The free tiers reveal different business philosophies. Statsig includes:

• Unlimited feature flags (no request limits)

• 2 million events monthly

• 50,000 session replays

• All platform features unlocked

PostHog's free tier provides:

• 1 million events

• 5,000 session recordings

• 1 million feature flag requests

• Separate limits per product

That 10x difference in session replays makes Statsig's free tier practical for real pilot projects. More importantly, Statsig never charges for feature flag checks - a fundamental difference that compounds at scale.

Enterprise pricing structures

Cost differences become dramatic as usage grows. Statsig's analysis shows that at 10 million monthly events, PostHog costs 2-3x more than Statsig. The gap widens because PostHog charges for each product module separately.

Let's run the numbers for a typical enterprise:

• 500,000 monthly active users

• 20 sessions per user monthly

• 10 feature flag checks per session

This generates 100 million monthly gate checks. PostHog charges for every single one. Statsig provides them free - forever. A G2 review from an enterprise customer confirms: "Customers could use a generous allowance of non-analytic gate checks for free, forever."

The impact hits teams using flags for operational controls hardest. Infrastructure flags, kill switches, circuit breakers, and environment toggles all count toward PostHog's limits. With Statsig, these operational flags cost nothing.

Bundled pricing also simplifies budgeting. One usage-based bill covers all products - experimentation, flags, and analytics. Volume discounts apply automatically as you scale. No negotiating separate contracts for each tool or worrying about overage charges on individual products.

Decision factors and implementation considerations

Time-to-value and onboarding

Getting your first experiment live matters more than feature lists. Statsig users report launching experiments within days, not weeks. The platform includes experiment templates, automated power calculations, and pre-built summaries that eliminate manual setup.

PostHog requires more configuration for experimentation beyond basic analytics. You'll manually set statistical parameters, define success metrics, and build experiment workflows. Their documentation treats each product as a separate entity rather than an integrated system.

The integration story highlights philosophical differences:

With Statsig:

• One SDK handles all functionality

• Shared data model across products

• Single interface for flags, experiments, and analytics

• Automatic metric computation from existing events

With PostHog:

• Separate configuration per product

• Independent data models

• Multiple interfaces to learn

• Manual metric definition required

Support and scalability

Support quality directly impacts implementation speed. Statsig provides dedicated customer data scientists even for self-serve accounts. Teams get direct Slack access where product experts - sometimes even the CEO - answer questions directly.

PostHog relies on community forums and self-service documentation. Their product team focuses on building features rather than hands-on support. This works for technical teams comfortable debugging independently.

Infrastructure capabilities become critical at scale:

Statsig's scale proof points:

• Handles trillions of events daily

• Maintains 99.99% uptime

• Powers experimentation at OpenAI and Notion

• Supports 2.5 billion monthly experiment subjects

PostHog's sweet spot:

• Works well for smaller deployments

• Self-hosting provides data control

• Community support for common issues

• Pricing structure reflects focus on mid-market

The architectural differences show in customer outcomes. Notion scaled from single-digit to 300+ experiments quarterly using Statsig's infrastructure. Brex reduced costs by 20% while improving cross-team data trust. These transformations require more than features - they need rock-solid infrastructure and expert guidance.

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

Statsig delivers enterprise-grade experimentation capabilities at 50-70% lower cost than PostHog's modular pricing. The savings come from a fundamental pricing philosophy: charge for value (insights from experiments) not usage (flag checks and API calls).

PostHog charges separately for feature flags, analytics, and experimentation. A growing startup might pay $500/month for flags, $800 for analytics, and $700 for experimentation. Statsig bundles these capabilities with unlimited free feature flags and generous event allowances - often cutting bills by thousands monthly.

Key differentiators for decision makers

The most significant advantage lies in statistical sophistication. Statsig offers:

• CUPED variance reduction (30-50% faster experiments)

• Sequential testing (peek at results without p-hacking)

• Stratified sampling (accurate marketplace experiments)

• Network effect detection (social and viral features)

• Switchback tests (time-based interventions)

These aren't academic features - they translate to shipping faster with more confidence. Teams detect smaller improvements, avoid false positives, and make better product decisions. PostHog's basic A/B testing can't match this depth.

Statsig's warehouse-native deployment solves enterprise security and compliance requirements PostHog struggles with. Companies maintain complete data control while leveraging Statsig's computational engine. No data leaves your warehouse. No security reviews for data transfers. No compliance headaches.

The platform processes 1+ trillion events daily without breaking a sweat. This scale enables transformational outcomes - as Paul Ellwood from OpenAI confirms: "Statsig's infrastructure and experimentation workflows have been crucial in helping us scale to hundreds of experiments across hundreds of millions of users."

Closing thoughts

Choosing between Statsig and PostHog often comes down to your team's experimentation maturity. If you need basic analytics with some A/B testing sprinkled in, PostHog's all-in-one suite might work. But if you're serious about experimentation - running dozens of concurrent tests, needing advanced statistics, or managing enterprise-scale traffic - Statsig provides the depth and cost structure to support your growth.

The unlimited feature flags alone can save teams thousands monthly. Add in sophisticated experimentation capabilities, warehouse-native deployment, and responsive support, and the choice becomes clearer. Statsig built a platform for teams that treat experimentation as a core competency, not an afterthought.

Want to dive deeper into the technical differences? Check out Statsig's detailed platform comparison or explore their customer case studies to see how companies like OpenAI and Notion transformed their experimentation programs.

Hope you find this useful!