An alternative to PostHog for analytics: Statsig

PostHog started as the open-source analytics darling, promising transparency and fair pricing for growing teams. But as companies scale, they're discovering a harsh reality: PostHog's modular pricing can turn a $200 monthly bill into thousands overnight, while features that seemed comprehensive at first prove limiting for serious experimentation work.

This reality has pushed teams like OpenAI, Notion, and Figma toward Statsig - a platform that takes a fundamentally different approach. Instead of bundling nine separate products with individual price tags, Statsig offers unified analytics and experimentation with unlimited feature flags at any scale. Let's dig into what actually separates these platforms beyond the marketing speak.

Company backgrounds and platform overview

Statsig launched in 2020 when a group of engineers decided to strip experimentation platforms down to their core. No legacy bloat. No rigid workflows. Just fast, reliable tools that data teams actually want to use. The focus paid off - they now process over 1 trillion events daily with 99.99% uptime.

PostHog took a different path. They built an open-source alternative to closed analytics platforms, then expanded into nine separate products: analytics, experiments, feature flags, session replay, and more. Their open-core model lets you self-host or use their cloud - appealing to teams who want control over infrastructure.

The philosophical differences run deep. PostHog targets developers with transparent pricing and open-source roots. Their generous free tier covers 90% of users without payment. Statsig attracts enterprise teams who need warehouse-native deployment, sequential testing, and variance reduction methods that actually work at scale.

Target markets and positioning

PostHog serves startups and mid-market companies who prioritize self-hosted options and predictable usage-based pricing. Teams love the control - you can inspect the code, run it on your servers, and avoid vendor lock-in entirely.

Statsig focuses on high-growth companies with sophisticated experimentation needs. These teams run hundreds of concurrent tests across millions of users. They need:

• CUPED variance reduction to detect smaller effects

• Sequential testing to prevent peeking bias

• Stratified sampling for complex user segments

• Automated metric guardrails

The difference shows in their customer lists. OpenAI, Notion, and Figma chose Statsig specifically for these advanced capabilities. As Paul Ellwood from OpenAI's data engineering team explained: "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."

Feature and capability deep dive

Experimentation and A/B testing

Here's where the platforms diverge dramatically. Statsig built experimentation as the core - everything else supports that mission. Warehouse-native deployment means you run experiments directly in Snowflake, BigQuery, or Databricks. Your data never leaves your infrastructure. Compliance teams love this.

The statistical toolkit goes beyond basic t-tests. CUPED variance reduction detects 30% smaller effects with the same traffic. Sequential testing prevents the classic mistake of checking results too early and declaring false winners. Statsig even exposes the underlying SQL with one click - data scientists can validate exactly how metrics calculate.

PostHog bundles A/B testing with feature flags for simpler use cases. You can run split tests and track conversions. The basics work fine. But teams needing switchback tests for marketplace effects, or automated bias detection for non-random assignment, hit walls quickly. One product manager noted the implementation complexity versus actual value delivered.

Both platforms support Bayesian and frequentist approaches. The difference lies in depth. Statsig provides confidence intervals, power calculations, and sample size recommendations out of the box. PostHog requires manual calculations for anything beyond simple conversion tracking.

Analytics and reporting

Traditional analytics tools treat analysis as the end goal. Statsig treats it as the starting point for experimentation. Every feature release becomes a natural experiment - ship it, measure impact, iterate based on data.

This integration changes workflows fundamentally. Product analytics, experimentation, and feature flags share the same metrics catalog. No duplicate definitions. No conflicting numbers between tools. When a PM spots an interesting user behavior pattern, they can launch an experiment to test their hypothesis in minutes, not days.

PostHog separates these workflows across products. You analyze behavior in one tool, design experiments in another, then reconcile metrics between them. This separation creates friction at exactly the wrong moment - when teams have momentum around a hypothesis.

The self-service aspect matters too. Statsig customers report that non-technical stakeholders build one-third of all dashboards. Product managers create conversion funnels. Marketers track campaign performance. Executives monitor business KPIs. All without writing SQL or filing engineering tickets.

Sriram Thiagarajan, CTO at Ancestry, captured this perfectly: "Having a culture of experimentation and good tools that can be used by cross-functional teams is business-critical now. Statsig was the only offering that we felt could meet our needs across both feature management and experimentation."

Pricing models and cost analysis

PostHog's pricing looks simple at first. Free tier includes:

• 1 million analytics events

• 5,000 session replays

• 1 million feature flag requests

Exceed any limit and costs accumulate across multiple products. Reddit users have flagged this concern repeatedly - what starts cheap gets expensive fast.

Statsig takes the opposite approach. Feature flags remain completely free at any scale. You only pay for analytics events and session replays. No surprise bills when you roll out a new feature to all users.

Standardized pricing analysis shows the real impact. At 100K monthly active users, PostHog typically costs 2-3x more than Statsig. The gap widens with scale - PostHog's costs spike dramatically beyond 10 million events monthly.

Real-world cost scenarios

Let's get specific with actual numbers:

Small startup (10K MAU):

• Statsig: Free forever with unlimited flags

• PostHog: $200+ monthly once you exceed flag limits

Growth company (500K MAU):

• Statsig: ~$500/month with volume discounts

• PostHog: $1,500+ for comparable features

Enterprise (10M MAU):

• Statsig: 50%+ volume discounts apply

• PostHog: Costs escalate sharply

The pricing model differences compound over time. One PM shared their frustration after viral growth caused their PostHog bill to spike unexpectedly. With Statsig's unlimited flags, that scenario can't happen.

Decision factors and implementation considerations

Developer experience and integration

Both platforms offer comprehensive SDKs, but implementation philosophy differs. Statsig provides 30+ SDKs with edge computing support for sub-millisecond flag evaluation globally. One integration covers flags, experiments, and analytics together.

PostHog requires separate implementations for each product module. Analytics needs one SDK configuration. Feature flags need another. Experiments need coordination between both. The modular approach increases both initial setup time and ongoing maintenance burden.

The unified approach pays dividends during incidents. When something breaks at 3am, debugging one integration beats troubleshooting three separate systems.

Support and documentation

Statsig customers consistently praise hands-on support. Direct CEO access via Slack means technical questions get answered by people who built the system. This matters when you're debugging complex experiment interactions or optimizing for scale.

PostHog relies primarily on community support for free-tier users. Email support requires a paid plan. The community provides value, but response times and expertise vary. Their documentation covers basics well but lacks depth on advanced experimentation techniques.

Both platforms invest heavily in documentation. The difference shows in focus areas. Statsig's guides cover statistical best practices, experiment design, and scaling strategies. PostHog spreads coverage across nine products, limiting depth in any single area.

Data ownership and deployment flexibility

Strict data governance kills many tool evaluations before they start. Statsig's warehouse-native deployment solves this elegantly. Your data stays in your warehouse. Statsig's statistical engine connects directly to compute results. Complete control with zero data movement.

PostHog offers self-hosting for data control. You run their software on your infrastructure. This works for analytics but creates challenges for experimentation. Running statistically valid experiments requires specific computational approaches that self-hosted deployments struggle to maintain.

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

The core difference comes down to focus. PostHog built nine products that work adequately for basic use cases. Statsig built one platform that excels at connecting analytics to experimentation at scale.

For teams serious about experimentation, the choice becomes clear:

• Warehouse-native architecture keeps data secure and compliant

• Advanced statistics like CUPED and sequential testing prevent costly mistakes

• Unified platform eliminates tool sprawl and data inconsistencies

• Free unlimited flags remove pricing anxiety during growth

Don Browning, SVP at SoundCloud, summarized their evaluation: "We evaluated Optimizely, LaunchDarkly, Split, and Eppo, but ultimately selected Statsig due to its comprehensive end-to-end integration."

The pricing reality seals the deal. PostHog costs 2-3x more than Statsig for analytics at scale. Their free tier caps at 5,000 session replays versus Statsig's 50,000. Reddit discussions consistently question whether PostHog "seems too good to be true" while noting implementation complexity.

Leading companies didn't choose Statsig by accident. They chose it because unified analytics and experimentation, backed by rigorous statistics and transparent pricing, delivers results PostHog's modular approach can't match.

Closing thoughts

Choosing between Statsig and PostHog ultimately depends on your team's experimentation maturity. PostHog works well for teams wanting basic analytics with occasional A/B tests. But once you need statistical rigor, warehouse integration, or predictable pricing at scale, Statsig becomes the clear choice.

The best part? You can try Statsig free with generous limits that actually let you validate the platform. Check out their pricing calculator to see real numbers for your use case, or dive into their experimentation guides to understand the statistical methods that set them apart.

Hope you find this useful!