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An enterprise alternative to PostHog: Statsig
Your product team needs experimentation infrastructure that actually scales. PostHog promises an all-in-one platform, but teams quickly discover the hidden costs: feature flags that charge per request, analytics priced per event, and experiments treated as a separate add-on. The modular pricing looks attractive until you're juggling multiple invoices and explaining budget overruns.
Statsig takes a different approach. Built by engineers who ran experiments at Facebook scale, the platform bundles everything into a single system. No surprise bills when your team creates new dashboards or your feature flags hit production traffic.
Company backgrounds and platform overview
Statsig launched in 2020, founded by ex-Facebook engineers who built Meta's experimentation platform. They brought hard lessons from managing billions of daily experiments: variance reduction matters more than fancy dashboards, and unified data pipelines beat modular tools every time. PostHog started a year earlier, targeting developers with open-source analytics tools and a self-hosting philosophy.
These origins shaped fundamental architecture decisions. Statsig unified four products—experimentation, flags, analytics, and replay—through a single data pipeline. Install one SDK, get everything. PostHog built modular products that operate independently. Each tool has separate pricing, separate infrastructure, and separate learning curves. This choice impacts your entire implementation: from how engineers instrument code to how analysts pull reports.
The difference shows in customer profiles. Statsig powers OpenAI's ChatGPT experiments and Notion's feature rollouts. These teams need warehouse-native deployments, sophisticated statistics, and proven scale. PostHog attracts budget-conscious startups and self-hosting enthusiasts—teams willing to manage infrastructure for perceived cost savings. One Reddit user questioned the sustainability of PostHog's generous free tiers against enterprise pricing.
PostHog's Product OS lets you adopt individual tools. Maybe you only need session replay today. But this flexibility creates problems: your replay data lives separately from experiment results. Want to analyze how a new feature affects user behavior? You'll manually correlate across systems. Statsig's integrated approach means metrics flow seamlessly—experiment results appear directly in analytics dashboards.
Feature and capability deep dive
Experimentation and A/B testing capabilities
Professional experimentation requires more than t-tests and p-values. Statsig implements CUPED for variance reduction, cutting required sample sizes by 30-50%. The platform supports sequential testing for early stopping and stratified sampling for heterogeneous populations. These aren't academic exercises: they're the difference between waiting months for results versus shipping improvements weekly.
PostHog provides basic frequentist testing. No variance reduction. No sequential analysis. You'll wait longer for conclusive results and risk false positives from peeking at experiments. The platform works for simple A/B tests but struggles with complex scenarios like:
Multi-armed bandits for recommendation systems
Factorial designs testing interaction effects
Network effect experiments requiring cluster randomization
Warehouse-native deployment changes everything for enterprise teams. Statsig runs directly on Snowflake, BigQuery, Databricks, and Redshift. Your data never leaves your infrastructure. PostHog primarily offers hosted deployments through their ClickHouse backend—fine for startups, problematic for regulated industries.
"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." — Paul Ellwood, Data Engineering, OpenAI
Feature flag and release management
Both platforms handle the basics: percentage rollouts, user targeting, and environment management. The critical difference emerges in production safety. Statsig automatically rolls back features when metrics breach thresholds. Set guardrails for error rates, latency, or business metrics—the system protects your users without manual intervention.
PostHog requires constant monitoring. Your team watches dashboards and manually disables problematic features. Fine for office hours, dangerous for global products. Statsig's automated safety mechanisms caught issues at Notion before customers noticed, preventing potential outages.
The pricing gap reveals different philosophies about infrastructure costs:
Statsig: Unlimited feature flags at every tier. No per-request charges ever.
PostHog: $0.0001 per request after one million monthly. A single popular feature can blow your budget.
Teams using PostHog report unexpected bills when features go viral. That viral TikTok integration? It just cost you thousands in flag requests. Statsig absorbs these spikes—you pay for value delivered, not infrastructure consumed.
Analytics and data infrastructure
Scale separates toys from tools. Statsig processes over 1 trillion events daily with 99.99% uptime. The platform handles OpenAI's ChatGPT traffic, Notion's collaborative editing, and Atlassian's enterprise deployments. PostHog's ClickHouse foundation works well for web analytics but hasn't demonstrated comparable scale publicly.
The real advantage? Unified workflows. Statsig embeds experiment results within analytics dashboards. Analyze feature impact without context switching. See how your new onboarding flow affects retention, conversion, and engagement—all in one view. PostHog treats analytics and experiments as separate products. You'll export data and manually join results.
Infrastructure choices cascade through your organization:
Data scientists waste time reconciling metrics across tools
Engineers maintain multiple integrations and SDKs
Product managers struggle to connect feature changes with business impact
"At OpenAI, we want to iterate as fast as possible. Statsig enables us to grow, scale, and learn efficiently. Integrating experimentation with product analytics and feature flagging has been crucial for quickly understanding and addressing our users' top priorities." — Dave Cummings, Engineering Manager, ChatGPT
Pricing models and cost analysis
Free tier comparison
PostHog splits its free tier across products. You get:
1 million analytics events
5,000 session replays
1 million feature flag requests
Limited experiment runs
Each product counts separately. Hit the limit on any one? Time to pay. Statsig bundles everything: unlimited feature flags, 2 million events, and 50,000 session replays. That's 10x more replays than PostHog, plus flags that never expire or charge overages.
The bundled approach eliminates budget anxiety. Your team experiments freely without watching meters. Launch that new feature to 100% of users—no flag charges. Track every user interaction—plenty of event budget. Record sessions for debugging—50,000 replays handle most use cases.
Enterprise pricing at scale
Let's talk real numbers. At 10 million monthly events, PostHog costs approximately $500 for analytics alone. Add experiments, feature flags, and session replays:
Analytics: $500/month
Feature flags: $300/month (assuming moderate usage)
Experiments: $200/month
Session replay: $250/month
Total: $1,250+ monthly for basic product analytics needs.
Statsig bundles everything for under $200 at the same scale. The savings compound as you grow. Companies report 50-70% cost reductions after switching from PostHog's modular pricing.
Hidden charges make PostHog's true cost unpredictable:
Group analytics: $0.00031 per group per month
Identified vs anonymous events: Different pricing tiers
Cohort exports: Additional charges
Data retention: Premium feature
One Reddit user questioned PostHog's pricing sustainability: "They're competing across multiple categories with established billion-dollar incumbents, and offering low pricing." The concern proves valid—PostHog's enterprise costs shock teams expecting startup-friendly pricing.
Decision factors and implementation considerations
Technical implementation and developer experience
SDK coverage matters less than implementation quality. Both platforms offer 30+ SDKs across web, mobile, and server environments. The difference? Statsig provides edge computing support and transparent SQL queries. You see exactly how metrics calculate. PostHog abstracts calculations, making debugging difficult.
Implementation timelines vary dramatically. Statsig customers report running experiments within weeks. The unified platform means one SDK installation covers everything. PostHog users spend months configuring separate products, each with unique requirements.
Developer experience impacts adoption across your organization:
Single SDK reduces integration complexity
Unified APIs simplify automation
Consistent concepts accelerate onboarding
Transparent queries enable custom analysis
PostHog's self-hosted option attracts privacy-conscious teams. But it demands substantial DevOps investment: managing ClickHouse clusters, scaling infrastructure, and maintaining security patches. Statsig's warehouse-native deployment eliminates infrastructure overhead while keeping data in your control.
Support and enterprise readiness
Enterprise support reveals fundamental philosophy differences. Statsig assigns dedicated customer data scientists to enterprise accounts. These aren't generic support agents—they're statisticians who help design experiments and interpret results. PostHog follows a community-first approach, requiring paid plans for email assistance.
Security and compliance separate startup tools from enterprise platforms:
SOC 2 Type II: Statsig maintains continuous certification
GDPR compliance: Built into the platform, not bolted on
Warehouse-native options: Complete data sovereignty
99.99% uptime SLAs: Backed by financial guarantees
PostHog's open-source nature provides code transparency—valuable for some teams. But it lacks automated safeguards critical for production environments. Statsig includes proactive health checks and automatic rollbacks. The platform prevents incidents before they impact users.
"Our engineers are significantly happier using Statsig. They no longer deal with uncertainty and debugging frustrations," notes Sumeet Marwaha, Head of Data at Brex.
Scale considerations matter even if you're small today. Statsig handles trillions of events daily across customers like OpenAI and Notion. The same infrastructure that powers ChatGPT experiments works for your startup. PostHog performs well at smaller scales but becomes expensive as usage grows.
Bottom line: why is Statsig a viable alternative to PostHog?
Statsig delivers enterprise-grade experimentation at pricing 50-70% below PostHog's costs. The unified platform eliminates the nickel-and-dime pricing that makes PostHog expensive at scale. You get feature flags, experiments, analytics, and session replay without calculating separate budgets.
The technical advantages compound over time. CUPED variance reduction means faster experiment results. Automated rollbacks prevent incidents. Warehouse-native deployment maintains data control. These aren't nice-to-have features—they're the difference between shipping weekly and spending months in analysis paralysis.
"The biggest benefit is having experimentation, feature flags, and analytics in one unified platform. It removes complexity and accelerates decision-making." — Sumeet Marwaha, Head of Data, Brex
Real companies see real results. Brex cut analysis time by 50% and reduced costs by 20%. OpenAI scales to hundreds of experiments across billions of users. Notion rolls out features confidently with automated safety checks. These teams chose Statsig because it handles enterprise scale without enterprise complexity.
PostHog works for simple use cases. But as your product grows, the modular pricing and separate tools create friction. Teams hesitate to run experiments because each test costs money. Feature flags become a budget line item instead of a development tool. Analytics and experiments live in silos, making insights harder to find.
The platform scales to billions of users without forcing migrations. Start with the cloud deployment and move to warehouse-native when you need data control. The same SDKs, same APIs, and same workflows—just running on your infrastructure.
Closing thoughts
Choosing between Statsig and PostHog isn't really about features. Both platforms offer experimentation, flags, and analytics. The decision comes down to philosophy: do you want modular tools with separate pricing, or an integrated platform that scales with your ambitions?
For teams serious about experimentation—teams who measure success in metrics moved, not tests run—Statsig provides the statistical rigor and unified workflow to ship with confidence. The platform grew from real needs at Facebook scale. Now it brings those same capabilities to companies ready to graduate from basic A/B testing.
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Hope you find this useful! Building great products requires great infrastructure—choose tools that grow with your ambitions, not limit them.