An alternative to GrowthBook SDK: Statsig

Choosing between experimentation platforms shouldn't require a PhD in statistics or a DevOps team on standby. Yet that's exactly where many teams find themselves when evaluating GrowthBook's open-source framework against modern alternatives.

The reality is simpler. Teams need experimentation tools that scale with their business, not their headcount. They need platforms that unify their data story instead of fragmenting it across multiple tools. Most importantly, they need pricing that makes sense whether they're testing with 100 users or 100 million.

Company backgrounds and platform overview

Statsig emerged in 2020 from a straightforward observation: experimentation platforms had become bloated and slow. The founding engineers shipped four production tools in under four years. Today they power experiments for OpenAI, Notion, and Figma while processing over 1 trillion events daily.

GrowthBook took a different path. Built as an open-source alternative to Optimizely and Google Optimize, the platform addresses two pain points: escalating costs and data privacy concerns. Their warehouse-native architecture lets teams run experiments on existing infrastructure - no data duplication required.

These origins shaped fundamentally different products. Statsig optimized for speed and scale, building integrated tools that share a single data pipeline. GrowthBook optimized for flexibility and control, relying on community contributions to expand functionality.

The business models reflect these philosophies. Statsig provides dedicated support teams for enterprise customers - the kind who need 99.99% uptime guarantees. GrowthBook offers community forums for free users and ticketed support for paying customers. Both work, but they serve different needs.

Feature and capability deep dive

Core experimentation capabilities

Here's where the technical gaps become clear. Statsig ships with sequential testing, CUPED variance reduction, and stratified sampling built in. These aren't nice-to-haves - they're essential for teams running sophisticated experiments. CUPED alone can increase statistical power by 30-50%, letting you detect smaller effects with the same traffic.

GrowthBook provides Bayesian and Frequentist statistical engines that handle standard A/B tests competently. The platform works well for basic split tests and feature rollouts. But teams needing advanced techniques like:

• Switchback experiments for marketplace effects

• Multi-armed bandits for dynamic optimization

• Stratified sampling for heterogeneous populations

Those teams will hit GrowthBook's ceiling quickly.

"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

Analytics and developer experience

The real differentiator isn't individual features - it's architecture. Statsig built experimentation, feature flags, analytics, and session replay on one unified pipeline. Track an event once; use it everywhere. No duplicate SDKs. No conflicting metric definitions. No data reconciliation nightmares.

GrowthBook takes the modular approach. You get solid experimentation and feature flags, but product analytics requires separate tools like Mixpanel or Amplitude. This creates ongoing maintenance burden:

• Multiple SDK implementations to manage

• User properties to sync across platforms

• Metric definitions to keep aligned

• Data pipelines to monitor and debug

For small teams, this modularity offers flexibility. For growing companies, it becomes technical debt that compounds monthly.

"The biggest benefit is having experimentation, feature flags, and analytics in one unified platform. It removes complexity and accelerates decision-making by enabling teams to quickly and deeply gather and act on insights without switching tools."

Sumeet Marwaha, Head of Data, Brex

Pricing models and cost analysis

Forget feature comparisons for a moment. The pricing model determines who can actually use your experimentation platform. Statsig charges for analytics events and session replays - actual usage. GrowthBook charges $20 per user per month for Pro features - regardless of usage.

This creates wildly different economics at scale.

Real-world cost scenarios

Consider a typical B2C startup: 100,000 monthly active users generating standard analytics events and feature flag checks. With Statsig, you're still in the free tier. With GrowthBook's 20-person product team? That's $400 monthly minimum - before you've run a single experiment.

The enterprise gap is more dramatic. A company with 10 million MAU might pay Statsig $2,000-3,000 monthly after volume discounts. That same company with 200 GrowthBook users faces $4,000 in seat costs alone. Usage-based pricing scales with your business. Seat-based pricing scales with your org chart.

The feature flag platform cost analysis shows Statsig typically costs 50% less than traditional solutions while including unlimited seats and MAU support.

Hidden costs of seat-based pricing

Seat restrictions create problems beyond budgets:

• Access gatekeeping: Product managers can't view experiments without burning a seat

• Slowed velocity: Engineers wait for license approvals before debugging flags

• Limited insights: Customer success can't access data to help users

• Budget surprises: New hires mean immediate cost increases

GrowthBook's pricing tiers make this explicit: 3 users on Starter, up to 100 on Pro, custom pricing for more. Meanwhile, Statsig includes unlimited seats at every tier. Your entire organization can access experiments, view results, and make data-driven decisions.

Decision factors and implementation considerations

Technical implementation and onboarding

Getting started matters. Statsig provides 30+ SDKs with sub-millisecond evaluation and automated rollback for production safety. Teams integrate both experimentation and feature flags in hours. The platform handles infrastructure complexity - no DevOps setup, no capacity planning, no midnight pages about server failures.

GrowthBook's self-hosted option gives you complete data control. It also gives you complete responsibility:

• Server provisioning and maintenance

• Security patches and updates

• Scaling for traffic spikes

• Uptime monitoring and alerting

• Backup and disaster recovery

For teams with existing infrastructure expertise, this control is valuable. For teams wanting to focus on experiments rather than operations, it's overhead.

Support and scalability

Statsig powers experiments at massive scale - processing those trillion+ daily events for OpenAI and Microsoft with 99.99% uptime. Enterprise customers get:

• Dedicated support engineers

• Guaranteed SLAs

• Custom integrations

• Direct Slack channels

• Quarterly business reviews

GrowthBook's community support model works differently. The 4,500+ member Slack community provides peer support and best practices. Paid plans add ticketed support starting at $20/user/month. For teams needing guaranteed response times or dedicated assistance, you'll need Enterprise pricing.

Data architecture and privacy

Both platforms support warehouse-native deployments for maximum data control. The implementations differ significantly.

Statsig offers both hosted and warehouse-native options without feature loss. Run on Statsig's infrastructure for simplicity or connect to your warehouse for control. Either way, you get the full platform.

GrowthBook emphasizes self-hosting as the primary model. This provides maximum privacy but requires ongoing maintenance. As Secret Sales discovered when choosing Statsig's warehouse-native option, having a "grown-up solution for experimentation" means more than just data control - it means reliability at scale.

Bottom line: why Statsig is a viable alternative to GrowthBook

GrowthBook offers solid open-source foundations for teams comfortable managing infrastructure. But Statsig removes the common barriers that prevent experimentation from scaling. Usage-based pricing means you pay for analytics events, not seats or flag checks. Your entire organization can adopt experimentation without budget meetings.

The infrastructure difference matters when you're building for growth. Statsig processes 1 trillion events daily with 99.99% uptime - the same infrastructure trusted by OpenAI, Notion, and Bluesky. You get enterprise reliability from day one. No DevOps required.

"Our engineers are significantly happier using Statsig. They no longer deal with uncertainty and debugging frustrations," notes Sumeet Marwaha at Brex.

Unified tooling changes team velocity. Instead of maintaining separate platforms for flags, experiments, and analytics, everything lives in one system. SoundCloud reached profitability for the first time in 16 years after consolidating tools with Statsig. Teams ship faster when data tells one story, not three conflicting versions.

Cost predictability enables long-term planning. While GrowthBook charges per seat, Statsig scales with actual usage. Feature flags stay free at any volume. Session replay includes 50,000 free monthly sessions - 10x competitors. You know costs before the CFO asks.

Closing thoughts

Choosing an experimentation platform isn't about feature checklists. It's about finding tools that grow with your ambitions without growing your operational burden. Statsig delivers that balance: sophisticated capabilities without the complexity tax.

For teams evaluating alternatives to GrowthBook's SDK, the decision often comes down to a simple question. Do you want to manage experimentation infrastructure or run experiments? Both are valid choices. Just make sure you're choosing based on your team's actual needs, not theoretical flexibility.

Ready to explore further? Check out Statsig's migration guides or dive into their SDK documentation to see the implementation differences firsthand. The best experimentation platform is the one your entire team actually uses.

Hope you find this useful!