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An alternative to PostHog for experimentation: Statsig
Choosing between experimentation platforms feels like navigating a minefield of pricing tiers, feature limitations, and hidden costs. PostHog promises everything under one roof - analytics, replays, feature flags, experiments - but teams quickly discover the catch: each tool has its own meter, and costs multiply fast.
Statsig takes a different approach. Built by ex-Facebook engineers who understood experimentation at scale, the platform processes over 1 trillion events daily while keeping costs predictable. This analysis digs into why companies like OpenAI and Notion chose Statsig over PostHog for their experimentation needs.
Company backgrounds and platform overview
Statsig emerged in 2020 when a small engineering team decided to build experimentation tools differently. They skipped the legacy baggage that weighs down platforms like Optimizely and focused on three things: speed, scalability, and developer experience. The result powers experimentation at OpenAI, Notion, and Brex - companies running hundreds of tests monthly on millions of users.
PostHog took the open-source analytics platform route. Their approach attracted engineers who wanted control over their data and deployment options. The generous free tier - 1 million events, 5,000 session replays, unlimited feature flags - made it especially appealing to startups. But there's a pattern here: teams love PostHog until they scale, then costs spiral unexpectedly.
The fundamental difference lies in their DNA. Statsig built for experimentation first; everything else supports that core mission. PostHog's Product OS bundles nine tools: analytics, session replay, feature flags, experiments, surveys, data warehouse, CDP, web analytics, and error monitoring. Jack of all trades, master of... well, you know how that goes.
Don Browning, SVP Data & Platform Engineering at SoundCloud, explained their choice: "We evaluated Optimizely, LaunchDarkly, Split, and Eppo, but ultimately selected Statsig due to its comprehensive end-to-end integration." SoundCloud reached profitability for the first time in 16 years after implementing Statsig's experimentation framework.
Feature and capability deep dive
Experimentation capabilities
Here's where the rubber meets the road. Statsig includes CUPED variance reduction, sequential testing, and Bayesian methods as standard features. These aren't buzzwords - they're essential tools that help teams detect 20-30% smaller effects with the same sample size. PostHog offers basic A/B testing but lacks these advanced statistical methods entirely.
Paul Ellwood from OpenAI's data engineering team put it bluntly: "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."
The warehouse-native deployment changes the game for privacy-conscious enterprises. Teams run experiments directly in Snowflake, BigQuery, or Databricks - no data leaves their infrastructure. PostHog requires either sending data to their servers or managing a complex self-hosted deployment. One approach keeps your security team happy; the other keeps them up at night.
Statistical rigor matters when making million-dollar decisions. Statsig automatically runs power analysis, checks for sample ratio mismatch, and alerts on novelty effects. PostHog treats experiments as an add-on to analytics - fine for basic tests, inadequate for serious experimentation programs.
Platform integration and developer experience
Both platforms offer 30+ SDKs, but implementation tells a different story. Statsig emphasizes zero-latency performance with edge computing support. When you're serving billions of requests daily, every millisecond counts. PostHog's SDKs focus on autocapture - great for getting started, problematic for performance at scale.
The infrastructure gap becomes obvious under load. Statsig processes trillions of events daily maintaining 99.99% uptime. This isn't theoretical - it's proven at OpenAI and Microsoft scale. PostHog users on Reddit report performance issues as they grow, particularly with real-time features.
Feature flag implementation reveals another crucial difference:
Statsig: Unlimited free feature flags at any scale, sub-millisecond evaluation
PostHog: Charges for flag requests beyond 1 million, performance varies by deployment
One Reddit user questioned PostHog's value after their feature flag costs exceeded their entire infrastructure budget. That's not sustainable for teams shipping features aggressively.
Pricing models and cost analysis
Free tier comparison
Let's cut through the marketing speak and look at actual limits:
Statsig's free tier:
Unlimited feature flags (no request limits)
50,000 session replays monthly
Full experimentation features
No user limits
PostHog's free tier:
1 million events total
5,000 session replays
1 million feature flag requests
All features, but each has separate limits
That 10x difference in replay allowance matters. Teams debugging production issues burn through 5,000 replays fast. Statsig gives you room to actually use the tools without constantly watching meters.
Enterprise cost scenarios
The team at Statsig published a detailed pricing analysis comparing major platforms. The results? Statsig costs 50-70% less than PostHog at 100K+ monthly active users. But raw numbers don't tell the whole story.
PostHog's multi-product billing creates a cost multiplication effect. You're not just paying for one service - you're paying for analytics AND replays AND feature flags AND experiments. Each product hitting its limit adds another line item. A typical SaaS at 500K MAU faces these monthly charges with PostHog:
Analytics events: $500-800
Feature flags: $300-500
Session replays: $200-400
Experiments: $300-500
Total damage: $1,300-2,200 monthly. Statsig for the same usage? Under $800, with unlimited feature flags included.
The philosophy difference is stark. PostHog argues you should "pay for what you use" - sounds reasonable until you realize Reddit users are questioning the model. Teams report surprise bills when launching new features triggers flag spikes or when debugging sessions consume replay quotas.
Decision factors and implementation considerations
Technical architecture and scalability
Numbers don't lie: Statsig processes 1+ trillion daily events with consistent sub-millisecond latency. The platform handles 2.5 billion unique monthly experiment subjects without breaking a sweat. This isn't hypothetical capacity - it's daily reality for customers like OpenAI and Microsoft.
PostHog's open-source model offers flexibility but demands engineering resources. Self-hosting means your team owns:
Infrastructure provisioning and scaling
Database optimization as data grows
Security patches and updates
Performance tuning under load
Statsig's managed infrastructure eliminates this overhead. Your engineers focus on building features, not maintaining experimentation infrastructure. Brex reported 50% time savings for their data science team after switching.
Support and documentation quality
When your CEO asks why conversion dropped 10% overnight, you need answers fast. Statsig provides hands-on enterprise support with dedicated customer data science teams. Their CEO might even jump into your Slack channel to help debug issues - try getting that from PostHog.
Documentation quality separates tools from platforms. Statsig's docs include:
SQL queries for custom metrics
Statistical methodology explanations
Implementation patterns by use case
Performance optimization guides
PostHog's documentation covers basics well but lacks depth on advanced topics. Community support works until you hit edge cases at 2 AM with a launch deadline looming.
A G2 reviewer noted: "The documentation Statsig provides also is super valuable." That's not exciting feedback - it's essential feedback.
Integration complexity and time to value
Real talk from Reddit's product management community: engineers question PostHog's implementation complexity. One PM described their current tool as "set it and forget it" - great for simplicity, terrible for actually improving your product.
Statsig's approach balances power with pragmatism:
SDKs work across 30+ languages
Edge computing reduces latency globally
Metrics layer connects to existing data pipelines
Pre-built integrations with major data warehouses
Notion went from single-digit to 300+ experiments quarterly after adopting Statsig. That's not just tool adoption - it's cultural transformation enabled by accessible experimentation.
Cost considerations at scale
The pricing analysis reveals an uncomfortable truth: PostHog consistently ranks as the most expensive option beyond 100K MAU. LaunchDarkly becomes prohibitive around the same threshold, but at least their pricing is predictable. PostHog's multi-meter approach creates budgeting nightmares.
Your actual costs depend on behavior patterns:
B2B SaaS with power users generates more events per user
Consumer apps might have higher user counts but lower engagement
Feature flag usage spikes during releases and rollbacks
Session replay consumption varies by debugging needs
Statsig's pricing calculator helps estimate real costs based on these patterns. PostHog's calculator... well, you'll need several calculators for each product.
Bottom line: why Statsig works as a PostHog alternative
Statsig delivers enterprise-grade experimentation at half the cost of PostHog. The unlimited feature flags alone save teams thousands monthly - especially those shipping aggressively. But cost is just the entry point.
The platform excels where PostHog struggles: statistical rigor. CUPED variance reduction, sequential testing, and Bayesian analysis come standard. These methods help teams detect smaller effects faster - critical when every day of indecision costs revenue. Companies like Notion scaled from single-digit to 300+ experiments quarterly by leveraging these capabilities.
Paul Ellwood from OpenAI summarized their experience: "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."
Warehouse-native deployment solves the data privacy puzzle without compromising performance. You maintain full control within Snowflake, BigQuery, or Databricks while accessing Statsig's statistical engine. PostHog's self-hosted option requires managing complex infrastructure; Statsig runs on your existing data stack.
The business impact speaks loudest:
Brex consolidated three tools into Statsig, cutting costs by 20%
SoundCloud achieved profitability for the first time in 16 years
Notion increased experiment velocity by 30x
These aren't outliers - they represent consistent patterns across Statsig's customer base. When experimentation becomes this accessible and affordable, teams naturally run more tests and make better decisions.
Closing thoughts
Choosing an experimentation platform shapes how your team builds products for years. PostHog offers a Swiss Army knife approach - lots of tools, each with limitations. Statsig provides a precision instrument for teams serious about experimentation.
The math is straightforward: lower costs, better statistics, proven scale. But the real value lies in transformation - teams that couldn't justify experimentation suddenly can. Products improve faster. Revenue grows more predictably.
If you're evaluating platforms, start with these resources:
Statsig's pricing calculator for real cost estimates
Statistical methods documentation to understand the technical advantages
Hope you find this useful!