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GrowthBook vs Statsig for enterprises
Choosing an experimentation platform feels straightforward until you dig into the details. You need a system that handles millions of events, integrates with your existing stack, and doesn't blow up your budget as you scale.
GrowthBook and Statsig represent two distinct philosophies in the experimentation space. One prioritizes open-source flexibility and warehouse-native architecture; the other focuses on unified infrastructure and scale. Understanding these differences determines whether you'll spend months building custom solutions or start running experiments next week.
Company backgrounds and platform overview
GrowthBook emerged in 2021 when engineers frustrated with expensive A/B testing tools built an open-source alternative. The platform connects directly to your data warehouse - no data copying required. This warehouse-native approach gives teams complete control over their data while running experiments. You write SQL queries, define metrics, and maintain full visibility into every calculation.
Statsig launched a year earlier, founded by former Facebook engineers who built the company's experimentation infrastructure. They created something different: an integrated platform combining feature flags, experiments, analytics, and session replay in one system. The founding team prioritized two things above all else - shipping fast and building tools engineers actually want to use.
The architectural differences tell you everything about each company's priorities. GrowthBook gives you transparency and flexibility through open-source code and self-hosting options. You can see every SQL query, modify the source code, and deploy anywhere. Statsig built for speed and scale instead, processing over 1 trillion events daily for companies like OpenAI and Notion.
These design choices attract different teams:
GrowthBook: Organizations wanting full control, existing data warehouse infrastructure, strong SQL capabilities
Statsig: Teams needing massive scale, integrated workflows, minimal setup time
The choice often comes down to a simple question: do you want to build and maintain your experimentation infrastructure, or do you need something that works at scale from day one?
Feature and capability deep dive
Experimentation capabilities
Both platforms handle the statistical basics well. You get Bayesian and Frequentist approaches, CUPED variance reduction, and sequential testing out of the box. But Statsig goes deeper with specialized methods that matter for complex experiments.
Switchback testing lets you test infrastructure changes where user-level randomization doesn't work. Stratified sampling ensures balanced treatment groups across critical segments. Automated heterogeneous effect detection surfaces when treatments work differently for different user groups - crucial insights that basic A/B tests miss entirely.
These capabilities aren't just nice-to-have features. Netflix uses switchback testing to optimize streaming infrastructure. Uber relies on stratified sampling for marketplace experiments. When your experiments touch core infrastructure or two-sided marketplaces, these advanced methods become essential.
Platform integration and architecture
GrowthBook's warehouse-native design queries your existing data sources directly. Write a SQL query, define your metrics, and start experimenting. This approach works beautifully if you already have robust data infrastructure. But it also means you're responsible for data pipeline reliability, query performance, and metric consistency.
Statsig takes a different path with both warehouse-native and high-performance hosted options. Their unified data pipeline powers four integrated products:
Experimentation platform with advanced statistics
Feature flag system with instant experiment conversion
Product analytics for exploratory analysis
Session replay for qualitative insights
This integration changes daily workflows significantly. Turn any feature flag into an experiment with one click. Analyze results using the same metrics catalog across all products. Watch actual user sessions to understand why experiments succeed or fail. Brex's team reported saving 50% of data scientists' time after consolidating their stack.
Developer experience and SDK performance
Performance at scale separates good platforms from great ones. Statsig processes over 1 trillion events daily with sub-millisecond evaluation latency across 30+ SDKs. Their infrastructure handles OpenAI's massive experimentation needs without breaking a sweat.
GrowthBook emphasizes lightweight SDKs with local evaluation for fast load times. Their approach prioritizes flexibility - customize caching strategies, modify evaluation logic, deploy to any edge computing platform. One GrowthBook user noted: "Implementing on our CDN edge and in our nextjs app was straight-forward and seamless."
The SDK philosophy reflects deeper priorities. Statsig optimizes for massive scale with managed infrastructure. GrowthBook focuses on flexibility and self-hosting simplicity. Choose based on whether you need proven scale or maximum control.
Pricing models and cost analysis
Pricing structure comparison
GrowthBook's pricing couldn't be simpler: $20 per user monthly for Pro features. Pay based on team size, not usage volume. Run unlimited experiments, deploy infinite feature flags, analyze boundless data - your bill stays the same.
Statsig flips this model completely. Pay for analytics events and session replays while getting unlimited seats and feature flags. Their free tier includes 2M events monthly, enough for many startups to run sophisticated experimentation programs at zero cost.
Real-world cost scenarios
Let's get specific. A 50-person product team with 1 million monthly active users faces different economics on each platform:
GrowthBook: $1,000 monthly ($20 × 50 users). Price stays fixed whether you run 10 experiments or 1,000. Budget certainty comes at a premium - especially for smaller teams who might only need 5-10 active experimenters.
Statsig: Potentially free if you stay under 2M events. Once you scale, costs typically range from $200-500 monthly for this team size. Volume discounts kick in above 20M events, often reducing costs by 50% or more.
The math changes dramatically at enterprise scale. SoundCloud evaluated Optimizely, LaunchDarkly, Split, and Eppo before choosing Statsig for cost efficiency. Don Browning, their SVP, cited "comprehensive end-to-end integration" as the deciding factor - but the pricing certainly helped.
Secret Sales reduced costs by 50% after switching platforms. Brex reported 20% savings compared to their previous solution. These aren't theoretical calculations - they're real companies saving real money while gaining more capabilities.
Decision factors and implementation considerations
Technical implementation and time-to-value
GrowthBook requires existing analytics infrastructure to function. No data warehouse? You'll need to build one first. Your team needs SQL expertise to write metric queries, configure data sources, and troubleshoot pipeline issues. This creates a chicken-and-egg problem: you need mature data infrastructure to experiment, but experiments help justify investing in that infrastructure.
Statsig removes these prerequisites entirely. Send events through their SDKs and get automatic metric calculation, statistical analysis, and experiment results. Bluesky launched 30 experiments in 7 months with a lean team - no dedicated data engineers required.
For teams wanting warehouse control, Statsig also provides native deployment across Snowflake, BigQuery, and other major platforms. You get the benefits of warehouse-native architecture without building the entire system yourself.
Support and enterprise readiness
Open-source projects excel at transparency but often struggle with support. GrowthBook provides:
Community forums for free tier users
Email support for paid customers
Self-service documentation
Limited enterprise support options
This model works fine until something breaks during a critical experiment. Then you're debugging SQL queries at 2 AM or waiting days for community responses.
Statsig takes enterprise support seriously:
Dedicated customer success teams
Private Slack channels with engineering access
99.99% uptime SLA
Proactive monitoring and alerts
Brex's engineering team noted: "Our engineers are significantly happier using Statsig. They no longer deal with uncertainty and debugging frustrations." This level of support matters when experiments directly impact revenue.
Cost considerations at scale
Both platforms start free, but the economics diverge as you grow. GrowthBook's seat-based pricing becomes expensive for large organizations - imagine paying $20 monthly for every PM, engineer, and analyst who might glance at experiment results.
You'll also need separate tools for:
Session replay ($1000s monthly)
Advanced analytics ($1000s monthly)
Performance monitoring ($100s monthly)
Custom statistical methods (engineering time)
Statsig's pricing analysis shows it remains cost-effective at any scale. The platform bundles all these capabilities without charging for flag checks - a critical difference from competitors who monetize every feature evaluation.
Open source vs. commercial platform trade-offs
GrowthBook's open-source nature provides genuine benefits. Audit the code, customize functionality, avoid vendor lock-in. For teams with strong DevOps capabilities and specific requirements, this flexibility proves invaluable.
But flexibility comes with hidden costs:
Infrastructure maintenance
Security patches
Feature development
Scaling challenges
Limited support
Commercial platforms trade customization for reliability. Notion scaled from single-digit to 300+ experiments quarterly using Statsig's managed infrastructure. They gained continuous platform improvements, enterprise support, and guaranteed performance - letting their team focus on experiments rather than infrastructure.
Bottom line: why is Statsig a viable alternative to GrowthBook?
Statsig solves the fundamental fragmentation problem in modern product development. Instead of juggling separate tools for experiments, feature flags, analytics, and session replay, you get one unified platform that actually works together. Feature flags instantly become experiments. Experiment results integrate with product analytics. Session replays provide context for quantitative findings.
Brex saved 50% of their data scientists' time through this consolidation. Sumeet Marwaha, their Head of Data, explained: "The biggest benefit is having experimentation, feature flags, and analytics in one unified platform. It removes complexity and accelerates decision-making."
The infrastructure handles extraordinary scale - over 1 trillion daily events with sub-millisecond latency. OpenAI, Notion, and Figma trust this platform for their experimentation programs. This isn't theoretical capacity; it's proven performance under real-world conditions.
Usage-based pricing aligns costs with value received. Small teams experiment for free. Growing companies pay reasonable amounts. Enterprises get volume discounts that make Statsig cheaper than building internally. Statsig's pricing analysis demonstrates they're the only provider offering truly free feature flags at all usage levels.
Perhaps most importantly, Statsig delivers instant value with minimal configuration. While GrowthBook requires technical setup, SQL expertise, and ongoing maintenance, Statsig works immediately. Teams start running experiments in days, not months. The platform includes automated statistical corrections, real-time health checks, and intelligent rollback capabilities - features that typically require months of custom development in open-source solutions.
Closing thoughts
Choosing between GrowthBook and Statsig ultimately depends on your team's priorities and constraints. GrowthBook offers transparency and control for teams with strong technical capabilities and existing data infrastructure. Statsig provides speed, scale, and integration for teams that need to ship experiments quickly without building custom infrastructure.
The best platform is the one your team will actually use. Consider your current capabilities, growth trajectory, and tolerance for infrastructure management. Then pick the tool that lets you focus on what matters: running experiments that improve your product.
For a deeper dive into experimentation platforms, check out:
Hope you find this useful!