An alternative to PostHog for A/B testing: Statsig

Choosing between experimentation platforms often comes down to a fundamental question: do you want a tool built specifically for A/B testing, or one that treats experiments as an afterthought? PostHog started as an analytics platform and later bolted on experimentation features. Statsig took the opposite approach.

This difference shapes everything from pricing to statistical rigor. Companies running serious A/B tests need more than basic split testing - they need variance reduction, sequential analysis, and the ability to detect subtle effects across millions of users.

Company backgrounds and platform overview

Both companies launched in 2020 but took radically different paths. Statsig's founding team - ex-Facebook engineers who built Meta's experimentation platform - knew exactly what enterprise A/B testing required. They built those capabilities from day one. PostHog targeted developers with open-source analytics tools, adding experimentation features much later.

The scale difference shows immediately. Statsig processes over 1 trillion daily events for companies like OpenAI, Notion, and Atlassian. PostHog primarily serves smaller startups through its open-source model, though they're pushing upmarket with mixed results.

Here's where architecture matters: Statsig built a unified data pipeline where experiments, feature flags, analytics, and session replay share the same infrastructure. PostHog sells modular products - each requiring separate implementation, configuration, and billing. This isn't just a technical detail; it fundamentally changes how teams work.

With Statsig, you launch an experiment and immediately see analytics dashboards populated with relevant metrics. No extra setup, no configuration headaches. PostHog users configure each tool separately: first analytics, then feature flags, then experiments, then replay. Each step adds complexity and cost.

The pricing models tell the same story. Statsig charges only for analytics events and session replays - feature flags remain free regardless of volume. PostHog charges for every component: each flag request, each analytics event, each experiment exposure, and each replay session. For teams running hundreds of experiments, these costs compound quickly.

Feature and capability deep dive

A/B testing and experimentation capabilities

Statistical rigor separates professional experimentation platforms from basic split-testing tools. Statsig provides the full arsenal: sequential testing to prevent peeking problems, CUPED variance reduction to detect smaller effects, and automated rollback triggers when metrics tank. PostHog offers basic A/B testing - fine for simple tests, inadequate for serious experimentation programs.

The differences compound at scale. Statsig's warehouse native deployment runs experiments directly in Snowflake, BigQuery, or Databricks. Your data never leaves your infrastructure. PostHog requires exporting data for warehouse analysis, adding latency and security concerns. When you're iterating on experiments daily, those extra steps slow everything down.

Both platforms claim Bayesian statistics support, but implementation matters. Statsig adds:

• Frequentist engines with proper false positive control

• Stratified sampling for balanced assignment

• Interaction detection between concurrent experiments

• Power analysis tools to right-size tests

PostHog handles basic two-variant tests adequately. Multi-armed bandits? Factorial designs? Interaction effects? You'll need a different platform.

Developer experience and technical architecture

SDK availability looks similar on paper - both platforms support 30+ languages and frameworks. But Statsig's edge computing support changes the game entirely. Feature flags evaluate in under a millisecond at the CDN edge, eliminating the network latency that plagues traditional client-server architectures.

Transparency matters when debugging experiments. Every Statsig metric shows its underlying SQL query with one click. PostHog abstracts queries behind their interface, making investigation painful when numbers don't match expectations. As one Statsig user noted: "Implementing on our CDN edge and in our nextjs app was straight-forward and seamless."

Data control reveals another philosophical difference:

• Statsig's approach: Warehouse native option keeps all data in your infrastructure

• PostHog's approach: Self-hosted version requires managing ClickHouse clusters yourself

Most enterprise teams find Statsig's model simpler for compliance. You get the benefits of a managed service while maintaining data sovereignty.

Pricing models and cost analysis

Usage-based pricing comparison

The pricing philosophy gap between these platforms keeps widening. Statsig provides unlimited feature flags for free, charging only for analytics events and session replays. PostHog bills separately for each product module - a model that punishes growth.

Let's run the numbers. A typical B2B SaaS with 100K monthly active users generates roughly 2M events monthly (20 events per user). Here's what you'd pay:

Statsig pricing:

• 2M analytics events: ~$200/month

• Unlimited feature flags: $0

• Total: $200/month

PostHog pricing:

• 2M analytics events: ~$250/month

• 2M feature flag requests: ~$200/month

• Experimentation add-on: ~$150/month

• Total: $600/month

The free tier comparison drives home the difference. PostHog caps at 1M events and 1M flag requests - limits many startups exceed within weeks. Statsig offers 10M free events monthly with unlimited flags forever. Teams can actually experiment without watching usage meters.

Enterprise cost implications

Scale transforms these pricing differences into budget-breaking disparities. Statsig's volume discounts reach 50% or higher beyond 20M monthly events. PostHog maintains near-linear pricing that becomes unsustainable as companies grow.

Hidden costs multiply the damage. PostHog charges $450 monthly just for group analytics - table stakes for any B2B company tracking account-level metrics. Want data pipelines? Extra charge. Need advanced segmentation? Another fee. Statsig bundles everything in base pricing.

Don Browning, SVP at SoundCloud, explained their decision: "We evaluated Optimizely, LaunchDarkly, Split, and Eppo, but ultimately selected Statsig due to its comprehensive end-to-end integration. We wanted a complete solution rather than a partial one."

The warehouse-native option provides another cost advantage. Companies leverage existing data infrastructure while avoiding vendor lock-in - invaluable for enterprises with strict governance requirements or existing data teams who want query access.

Decision factors and implementation considerations

Time-to-value and onboarding complexity

Getting your first experiment live shouldn't require a multi-week implementation project. Statsig's unified platform enables teams to launch experiments within hours using pre-built templates and automated metric creation. The platform does the heavy lifting.

PostHog's modular approach creates friction at every step:

1. Configure analytics SDK and verify events

2. Implement feature flag SDK separately

3. Connect analytics events to feature flags

4. Set up experimentation framework

5. Configure each experiment manually

Real customer experiences highlight the difference. Runna launched over 100 experiments in their first year using Statsig's templates and guardrail metrics. Meanwhile, Reddit users report PostHog's initial setup feels "overwhelming" due to extensive configuration requirements.

Scalability and enterprise readiness

Your experimentation platform should accelerate growth, not constrain it. Statsig processes over 1 trillion events daily with 99.99% uptime - battle-tested by companies like OpenAI running hundreds of concurrent experiments. PostHog shows performance strain beyond 10 million monthly events according to pricing comparisons.

Enterprise readiness extends beyond raw throughput. Consider these requirements:

• Data residency: Statsig's warehouse native keeps data in your infrastructure

• Compliance: SOC 2, GDPR, HIPAA compatibility built-in

• Support: Dedicated customer success teams for complex implementations

• Integration: Native connections to data warehouses and analytics tools

Paul Ellwood from OpenAI's data engineering team put it simply: "Statsig's infrastructure and experimentation workflows have been crucial in helping us scale to hundreds of experiments across hundreds of millions of users."

PostHog relies on community forums and documentation for support. That works for simple use cases but falls short when production experiments affect millions of users and revenue.

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

PostHog's multi-product pricing model becomes a liability at scale. Each component - analytics, flags, experiments, replay - carries separate charges that compound exponentially. Statsig's analysis shows PostHog consistently ranks as the most expensive option across all usage tiers.

Statsig bundles everything with unlimited feature flags included free forever. While PostHog meters every flag request beyond 1M, Statsig removes that constraint entirely. Companies like Brex reduced costs by 20% after switching platforms - savings that scale with growth.

The technical gap proves even more decisive for serious experimentation programs. Statsig provides:

• CUPED variance reduction to detect 50% smaller effects

• Sequential testing to prevent false positives from peeking

• Stratified sampling for balanced user assignment

• Automated monitoring to catch metric regressions

These aren't nice-to-have features - they're essential for trustworthy experiments. PostHog lacks these statistical methods entirely.

Scale crystallizes the platform differences. Statsig handles over 1 trillion events per day with sub-millisecond latency. PostHog users report performance degradation as they grow, forcing expensive infrastructure upgrades or platform migrations. Paul Ellwood from OpenAI emphasized this advantage: "Statsig's experimentation capabilities stand apart from other platforms we've evaluated."

Closing thoughts

The choice between Statsig and PostHog ultimately reflects your experimentation ambitions. Teams running occasional split tests might find PostHog's basic capabilities sufficient. But companies serious about data-driven decision making need robust statistical methods, transparent pricing, and infrastructure that scales with their growth.

Statsig built an experimentation platform from first principles, incorporating lessons from Meta's massive testing program. That DNA shows in every architectural decision - from free feature flags to warehouse-native deployment to advanced statistical engines. PostHog retrofitted experiments onto an analytics platform, and the seams show.

For teams ready to move beyond basic A/B testing, explore Statsig's experimentation platform or dive into their technical documentation. The platform offers a generous free tier that lets you validate the capabilities before committing.

Hope you find this useful!