An alternative to PostHog for feature flags: Statsig

Feature flags have become table stakes for modern engineering teams, but choosing the right platform can make or break your velocity. PostHog's all-in-one approach sounds appealing until you realize you're paying for nine products when you need two.

Statsig takes a different path. Built by ex-Facebook engineers who knew the pain of bloated experimentation platforms, they focused on doing a few things exceptionally well. Here's what that means for teams deciding between these two options.

Company backgrounds and platform overview

Statsig launched in 2020 when engineers from Facebook's experimentation team got tired of watching companies struggle with legacy tools. They built what they wished existed: powerful experimentation without the enterprise cruft. Their scrappy approach resonated - OpenAI, Notion, and Figma signed on early.

PostHog started the same year with a different vision. They began as open-source analytics, then expanded through their Product OS architecture into feature flags, experiments, and beyond. Reddit users describe them as "the weirdest successful SaaS startup" - a badge they wear proudly.

The philosophical split shows immediately. Statsig builds for engineering-led teams who want sophisticated experiments without bureaucracy. PostHog appeals to companies seeking self-hosted analytics with a Swiss Army knife of product tools. One goes deep, the other goes wide.

PostHog's open-source roots let teams run everything on their own servers. They now charge for nine different products: analytics, session replays, feature flags, A/B tests, surveys, and more. Statsig bundles core capabilities but obsesses over statistical rigor and developer workflows.

These choices cascade through everything. PostHog keeps adding products - error tracking, data pipelines, heat maps. Statsig doubles down on experimentation excellence with CUPED variance reduction, sequential testing, and warehouse-native deployment. Different philosophies, different outcomes.

Feature and capability deep dive

Core experimentation capabilities

Here's where the rubber meets the road. Statsig ships with CUPED variance reduction that cuts experiment runtime in half - crucial when you're testing with millions of users. Their sequential testing lets you check results early without inflating false positive rates. PostHog offers basic A/B testing that works fine until you need more.

The sophistication gap becomes obvious fast:

• Stratified sampling for balanced user groups? Statsig only

• Heterogeneous effect detection to spot winner/loser segments? Not in PostHog

• Switchback tests for marketplace experiments? You'll need Statsig

• Multi-armed bandits for dynamic optimization? Missing from PostHog's toolkit

Deployment architecture matters too. Statsig's warehouse-native option runs experiments directly in Snowflake, BigQuery, or Databricks. Your data never leaves your infrastructure. PostHog requires shipping everything to their ClickHouse cluster - a non-starter for enterprises with strict data governance requirements.

Analytics and developer experience

SDK coverage looks similar on paper. Statsig provides 30+ SDKs including edge computing support via Cloudflare Workers. PostHog offers about 15 focused on web and mobile. But performance tells the real story.

Statsig's SDKs evaluate feature flags in under 1 millisecond after initialization. They handle billions of evaluations daily without breaking a sweat. PostHog's open-source approach trades some performance for self-hosting flexibility - a reasonable choice, but one with consequences at scale.

Developer workflows diverge significantly. Statsig emphasizes real-time diagnostics: you see exposure events instantly, health checks flag issues immediately, and debugging happens in context. As one G2 reviewer noted, "Implementing on our CDN edge and in our nextjs app was straight-forward and seamless."

PostHog integrates everything through their Product OS. Analytics connect with session replays and feature flags in one interface. It's cohesive if you buy into their entire ecosystem. But teams with existing tools face integration headaches.

Pricing models and cost analysis

Free tier comparison

The free tier difference hits you immediately. Statsig offers unlimited feature flags forever - no catches, no limits. PostHog caps their free tier at 1 million flag requests monthly. Once you exceed that, every flag check costs money.

Product bundling amplifies the gap:

• Statsig free tier: Unlimited flags, basic analytics, 50,000 session replays, experimentation

• PostHog free tier: 1M flag requests, limited analytics events, basic features across products

This isn't academic. A mobile app with 50,000 daily active users easily generates millions of flag checks. With Statsig, that usage stays free forever. With PostHog, you're writing checks by month two.

Enterprise cost implications

Cost analysis shows PostHog runs 2-3x more expensive than Statsig beyond 100,000 MAU. The culprit? PostHog's per-request pricing for flags creates unpredictable costs that balloon with traffic spikes.

Statsig's model scales with analytics events and session replays only. Feature flag usage never impacts your bill. This creates predictable costs essential for budget planning. You know exactly what you'll pay as you grow.

Real numbers make this concrete. Take a B2C app with 500,000 MAU generating 20 sessions each:

• PostHog: ~$1,000 for 10 million flag requests alone

• Statsig: $0 for unlimited flags, pay only for analytics you track

The difference compounds monthly. Teams report saving hundreds of thousands annually after switching.

Decision factors and implementation considerations

Onboarding and time-to-value

Speed matters when you're shipping daily. Statsig customers typically implement their first experiments within one month. Runna launched over 100 experiments in their first year - that pace requires minimal setup friction.

PostHog's broader scope demands more configuration. Self-hosting adds complexity. Their hosted tiers require navigating pricing decisions across nine products. Statsig provides turnkey cloud deployment that scales automatically from day one.

Bluesky's CTO Paul Frazee captured it well: "We thought we didn't have the resources for an A/B testing framework, but Statsig made it achievable for a small team." That accessibility matters when engineering resources are precious.

Support and scalability

Infrastructure reliability becomes non-negotiable at scale. Statsig processes 1+ trillion events daily with 99.99% uptime. OpenAI, Notion, and Figma trust their experiments to this infrastructure. PostHog's community support works for smaller teams, but enterprises need more.

The support difference shows in outcomes. Secret Sales launched 30 features in six months with Statsig's customer data scientists guiding their experimentation strategy. PostHog users on Reddit report implementation complexity requiring significant engineering investment.

Technical architecture differences

Architecture choices reveal platform priorities. PostHog bundles everything into their Product OS using ClickHouse for analysis. This monolithic approach creates dependencies - updating one product affects others.

Statsig's modular architecture lets you adopt features incrementally. Start with feature flags, add experimentation when ready, layer in session replays later. Each component works independently without breaking existing workflows.

Warehouse-native deployment represents the starkest difference. Statsig supports these platforms natively:

• Snowflake

• BigQuery

• Redshift

• Databricks

• Athena

PostHog requires data export to achieve similar functionality. That adds latency, complexity, and potential security concerns to your pipeline.

Cost implications at scale

Let's talk real money. PostHog charges $0.0001 per feature flag request after the free tier. Sounds tiny until you do the math. Statsig includes unlimited flags in all plans, charging only for analytics and replays.

A typical scenario shows the impact:

• B2C app: 1 million MAU, 20 sessions monthly

• PostHog cost: ~$2,000/month just for flags

• Statsig cost: $0 for unlimited flags

Brex reported 20% overall cost savings after switching. The savings accelerate with growth - critical for companies watching burn rates closely.

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

Statsig delivers 50-70% cost savings compared to PostHog while providing deeper experimentation capabilities. The unlimited feature flags alone justify the switch for high-traffic applications. But cost is just the beginning.

Engineering teams choose Statsig for focused excellence. PostHog spreads across nine products; Statsig perfects four core tools. This focus translates to faster implementation and better reliability. Brex engineers report being "significantly happier" after making the switch.

Ancestry's CTO Sriram Thiagarajan put it clearly: "Statsig was the only offering that we felt could meet our needs across both feature management and experimentation." That combination - powerful flags plus sophisticated testing - defines Statsig's sweet spot.

The warehouse-native option seals the deal for enterprises. Keep your data in Snowflake or BigQuery while leveraging Statsig's statistics engine. PostHog can't match this flexibility without forcing data through their infrastructure.

Performance metrics tell the final story. Statsig handles 1+ trillion daily events with 99.99% uptime. Flags evaluate in under 1ms. Session replays cost 10x less than PostHog. The platform scales from startup to IPO without architecture changes or pricing surprises.

Closing thoughts

Choosing between Statsig and PostHog comes down to your priorities. If you want an all-in-one platform and don't mind the complexity, PostHog works. But if you need best-in-class experimentation with predictable costs, Statsig delivers.

The unlimited feature flags change the economics completely. Combined with superior experimentation capabilities and warehouse-native deployment, Statsig offers a compelling alternative for teams serious about testing.

Want to dive deeper? Check out Statsig's migration guide or compare detailed pricing scenarios. The data speaks for itself.

Hope you find this useful!