An alternative to GrowthBook Cloud: Statsig

Choosing between experimentation platforms isn't just about features - it's about finding the right fit for how your team actually works. GrowthBook's open-source approach appeals to teams wanting control over their infrastructure, but the reality of self-hosting often hits harder than expected: maintenance overhead, scaling challenges, and per-seat pricing that adds up fast.

Statsig takes a different approach. Built by former Facebook engineers who understood the pain of managing experimentation at scale, the platform handles the heavy lifting while giving teams the flexibility they need. The difference shows up in the details: where GrowthBook charges per user, Statsig offers unlimited seats; where GrowthBook requires separate analytics tools, Statsig includes them natively.

Company backgrounds and platform overview

Statsig emerged in 2020 when former Facebook engineers decided to rebuild experimentation from scratch. They'd seen firsthand how clunky tools slowed down product development at scale. Instead of copying existing platforms, they focused on speed and developer experience - winning customers through product quality rather than aggressive sales tactics. In under four years, they shipped production-grade experimentation, feature flags, analytics, and session replay as a unified platform.

GrowthBook started as an open-source alternative to expensive commercial platforms. The creators built it to solve their own problems: high costs and privacy concerns with existing solutions. They offer both self-hosted and cloud options, appealing to teams who want full control over their data infrastructure.

The platforms reflect fundamentally different philosophies. Statsig's product-led approach captured enterprise clients like OpenAI, Notion, and Figma through engineering velocity alone. These companies didn't need convincing - they needed a platform that could handle their scale. GrowthBook built a community-driven development model with over 4,500 Slack members contributing features and feedback.

Both serve technical audiences, but deployment tells the real story. Statsig processes over 1 trillion events daily across unified cloud infrastructure - a scale that would crush most self-hosted deployments. GrowthBook's flexibility through self-hosting attracts teams with strict data governance requirements, though the operational overhead often surprises first-time users.

Feature and capability deep dive

Experimentation capabilities

Statsig stands out with warehouse-native deployment alongside cloud hosting. Teams run experiments directly in Snowflake, BigQuery, or Databricks while maintaining complete data control. This isn't just connecting to your warehouse - it's running the entire experimentation engine inside it.

The statistical depth goes beyond basic A/B testing:

• CUPED variance reduction that speeds up experiment conclusions by 30-50%

• Sequential testing with always-valid p-values

• Automated heterogeneous effect detection

• Switchback testing for marketplace experiments

• Stratified sampling for imbalanced populations

GrowthBook provides Bayesian and Frequentist engines with a visual editor for non-technical users. The platform handles standard A/B testing and feature flags competently. Multiple comparison corrections help avoid false positives, but you won't find advanced capabilities like switchback testing or sophisticated variance reduction techniques.

The difference becomes clear in practice. A marketplace company running city-level experiments needs switchback testing to handle interference effects. An e-commerce platform testing checkout flows benefits from CUPED to reach conclusions faster. GrowthBook users often build these capabilities themselves or simply go without.

Analytics and data infrastructure

Processing over 1 trillion events daily isn't just a vanity metric - it reflects architectural choices that matter at any scale. Statsig combines experimentation with native product analytics, letting teams access conversion funnels, user journeys, and session replay without tool-switching. You define a metric once and use it everywhere.

GrowthBook connects to existing analytics tools rather than providing native capabilities. You'll query data from Google Analytics, Mixpanel, or your warehouse through their interface. This sounds flexible until you hit the limitations:

• Can't easily analyze experiments alongside product metrics

• Metric definitions live in multiple places

• Performance depends on your underlying infrastructure

• No unified view of user behavior

The infrastructure gap shows up in daily workflows. Statsig's purpose-built analytics engine handles massive event volumes with sub-millisecond latency. When an experiment shows unexpected results, you can immediately dive into user sessions, analyze conversion funnels, and understand what happened - all in one place.

As one Statsig customer noted on G2: "Using a single metrics catalog for both areas of analysis saves teams time, reduces arguments, and drives more interesting insights." This unified approach eliminates the metric discrepancies that plague teams using separate tools.

Pricing models and cost analysis

Transparent pricing structures

Statsig charges only for analytics events and session replays. Feature flags remain completely free at any scale - whether you're doing 100 or 100 million gate checks. This usage-based model means you pay for actual value, not potential usage.

GrowthBook's per-seat pricing starts at $20/user/month after just 3 users. Every product manager, engineer, analyst, and marketer who needs access counts against your limit. Premium features like visual editors and advanced statistics push costs higher.

Real-world cost implications

Let's get specific about what this means for different team sizes:

Startup (100K MAU, 5-person team):

• Statsig: Free tier covers everything including experimentation and analytics

• GrowthBook: $40/month minimum for basic access

Growth stage (1M MAU, 25-person product org):

• Statsig: ~$500/month based on event volume

• GrowthBook: $500/month just for user seats, before any feature upgrades

Enterprise (10M+ MAU, 100+ product team):

• Statsig: Volume discounts of 50%+ kick in

• GrowthBook: $2,000+/month in seat costs alone

The warehouse-native option changes everything for data-conscious teams. Keep your data in your own infrastructure while accessing enterprise features. No data transfer costs, no vendor lock-in, no compliance headaches.

Ancestry's CTO captured this perfectly: "Statsig was the only offering that we felt could meet our needs across both feature management and experimentation." They needed scale without breaking the budget - exactly what usage-based pricing delivers.

Decision factors and implementation considerations

Developer experience and integration

Technical implementation reveals each platform's priorities. Statsig ships 30+ SDKs covering every major language and framework. React, Python, Go, Rust, Swift - they're all first-class citizens. Edge computing support delivers sub-1ms evaluation latency globally through Cloudflare Workers and Vercel Edge Functions.

GrowthBook's SDKs handle the basics but require manual work for production scenarios:

• Building retry logic for network failures

• Implementing caching strategies

• Setting up failover mechanisms

• Configuring edge deployment

This difference compounds over time. Statsig's SDKs include automatic retries, intelligent caching, and instant failover out of the box. One team reported spending two weeks building reliability features for GrowthBook that Statsig provided by default.

Integration complexity varies by use case. Simple feature flags work fine on both platforms. But when you need warehouse-native deployment, custom event pipelines, or real-time synchronization, the implementation effort diverges significantly. Statsig's opinionated approach means less flexibility but far less work.

Support and documentation quality

Support models reflect company DNA. Statsig assigns dedicated customer data scientists to enterprise accounts - actual experts who help design experiments and interpret results. They'll review your experimental design, suggest variance reduction techniques, and debug unexpected results. Even free-tier users get direct engineer access through Slack.

GrowthBook's 4,500-member Slack community provides peer support, but response quality varies. You might get instant help from a power user or wait days for answers to complex questions. Premium support exists but costs extra and lacks the hands-on guidance Statsig provides.

Documentation directly impacts productivity. Statsig's docs include:

• Runnable code examples for every SDK

• Statistical methodology explanations

• Architecture diagrams for scale

• Common pitfall warnings

GrowthBook documents core functionality well but leaves gaps around advanced statistics and warehouse integration. Teams discover these limitations mid-implementation - after significant time investment.

Brex's Head of Data put it simply: "Our engineers are significantly happier using Statsig. They no longer deal with uncertainty and debugging frustrations." When your team ships experiments daily, documentation and support quality directly impact velocity.

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

The fundamental difference comes down to philosophy: GrowthBook makes you pay per person, Statsig makes you pay for usage. While GrowthBook charges $20 per user after just 3 users, Statsig offers unlimited seats with usage-based pricing. Your entire team - engineers, PMs, analysts, marketers - can access experiments without budget math.

Infrastructure tells the real story. Statsig processes over 1 trillion events daily for companies like OpenAI and Microsoft. When Notion scaled from single-digit to 300+ experiments quarterly, they needed proven infrastructure, not a self-hosted solution requiring constant care and feeding. GrowthBook's open-source roots mean most deployments handle far smaller volumes.

Beyond basic experimentation, Statsig includes product analytics and session replay in one platform. No more switching between tools, reconciling conflicting metrics, or building custom integrations. Teams analyze user behavior, launch experiments, and debug issues in one unified workflow. GrowthBook requires separate tools for these capabilities, creating the exact data silos modern teams try to avoid.

The cost advantage becomes obvious at scale. Statsig's experimentation pricing beats competitors above 100K MAU. Feature flags stay completely free at any volume. This pricing model let Brex cut costs by 20% while scaling to 100+ concurrent experiments. Don Browning, SVP at SoundCloud, evaluated the entire market: "We evaluated Optimizely, LaunchDarkly, Split, and Eppo, but ultimately selected Statsig due to its comprehensive end-to-end integration."

Closing thoughts

Picking an experimentation platform shapes how your team builds products for years to come. GrowthBook works well for small teams comfortable with self-hosting and seat-based limits. But as you scale - in users, experiments, or team size - those constraints become roadblocks.

Statsig built a platform that grows with you: unlimited seats, built-in analytics, and infrastructure that handles real scale. The teams who switched report faster experiment velocity, lower costs, and happier developers. That's not marketing speak - it's what happens when tools match how teams actually work.

Want to dig deeper? Check out Statsig's experimentation platform or read their detailed pricing breakdown to see the math yourself. Hope you find this useful!