An alternative to GrowthBook's Bayesian engine: Statsig

Choosing an experimentation platform feels straightforward until you dig into the statistics. Most teams default to Bayesian methods because they seem intuitive - you get probability statements like "95% chance the variant is better." But this simplicity comes with hidden costs: limited statistical power, fewer advanced techniques, and constraints on how you analyze results.

GrowthBook built their entire platform around Bayesian statistics. Statsig took a different approach - supporting both Bayesian and Frequentist methods while adding advanced techniques like CUPED variance reduction and sequential testing. The choice reflects a fundamental difference in philosophy about what modern experimentation teams actually need.

Company backgrounds and platform overview

Statsig emerged from Facebook's experimentation culture in 2020. The founding engineers had seen firsthand how sophisticated testing infrastructure drives product decisions at scale - processing trillions of events daily wasn't just a technical achievement, it was table stakes. They built for enterprises from day one, knowing that developer experience matters as much as statistical rigor.

GrowthBook chose the open-source path, addressing cost and privacy concerns by letting teams self-host. The platform connects directly to your data warehouse. You keep sensitive data in-house. For engineering teams comfortable with infrastructure management, this control appeals strongly.

The platforms' architectures reveal deeper differences. GrowthBook's warehouse-native approach means you need existing data infrastructure before running experiments. Statsig offers both cloud and warehouse-native deployments - flexibility that attracted companies like OpenAI and Notion. As Don Browning from SoundCloud noted:

"We evaluated Optimizely, LaunchDarkly, Split, and Eppo, but ultimately selected Statsig due to its comprehensive end-to-end integration."

Target audiences crystallized around these technical choices. GrowthBook serves teams who:

• Already have mature data warehouses

• Want complete code transparency

• Prefer self-hosting over managed services

Statsig attracts broader use cases: product teams needing turnkey solutions, enterprises requiring both speed and warehouse compatibility, and data science teams who want advanced statistical methods beyond basic Bayesian testing.

Feature and capability deep dive

Experimentation capabilities

Here's where the Bayesian limitation becomes clear. GrowthBook's exclusive focus on Bayesian statistics works fine for simple A/B tests. You get probability estimates. You avoid the confusion of p-values. But real-world experimentation demands more sophisticated approaches.

Statsig's dual statistical engine - supporting both Frequentist and Bayesian methods - unlocks critical capabilities:

• Sequential testing: Monitor experiments continuously without inflating false positive rates

• CUPED variance reduction: Detect 30-50% smaller effects using pre-experiment data

• Switchback testing: Handle marketplace experiments where user randomization fails

• Power analysis: Calculate required sample sizes before launching tests

Teams at Notion leverage these capabilities to scale their testing program dramatically:

"We transitioned from conducting a single-digit number of experiments per quarter using our in-house tool to orchestrating hundreds of experiments, surpassing 300, with the help of Statsig."

The technical advantages compound at scale. CUPED alone can reduce experiment runtime by weeks. Sequential testing lets you peek at results safely - something Bayesian-only platforms struggle with. For data science teams trained in Frequentist methods, having both options prevents methodology battles that derail experimentation programs.

Platform integration and developer experience

SDK coverage reveals platform maturity. Statsig maintains 30+ SDKs spanning every major language, framework, and edge computing platform. GrowthBook covers core languages - JavaScript, Python, Ruby - but gaps emerge in specialized environments. Need React Native support? Edge computing compatibility? Advanced server-side rendering? The missing SDKs force workarounds.

Architecture differences run deeper than SDK count. GrowthBook provides experimentation and feature flags. Period. You'll need separate tools for:

• Product analytics

• Session replay

• User segmentation

• Metric computation

Statsig bundles experimentation, feature flags, analytics, and session replay into one platform. Engineers define metrics once. Those metrics flow through every product surface - experiments, dashboards, alerts. The Brex team noticed this integration immediately:

"Our engineers are significantly happier using Statsig. They no longer deal with uncertainty and debugging frustrations. There's a noticeable shift in sentiment—experimentation has become something the team is genuinely excited about."

The unified data pipeline eliminates common integration headaches. No more mismatched user counts between tools. No more debugging why experiment metrics don't match analytics dashboards. One source of truth for all product decisions.

Pricing models and cost analysis

Core pricing structure

GrowthBook's pricing model creates immediate friction: $20 per user monthly for Pro features. Your 50-person product team? That's $1,000 monthly before running a single experiment. Hit 100 users and you're forced into opaque Enterprise pricing.

Statsig prices on event volume only. The math becomes stark:

GrowthBook Pro costs:

• 10 users: $200/month

• 50 users: $1,000/month

• 100 users: $2,000/month

• 101+ users: Contact sales

Statsig pricing:

• Unlimited users: Free up to 2B events

• Unlimited feature flags: Always free

• Session replay included: No extra charge

• Analytics included: Same pricing tier

The free tier comparison highlights philosophical differences. GrowthBook caps at three users - barely covering a founder team. Statsig's 2 billion monthly events support entire companies. Seat-based pricing punishes growth; usage-based pricing rewards efficiency.

Real-world cost scenarios

Consider typical team compositions and their costs:

Series A startup (25 people):

• GrowthBook: $500/month minimum

• Statsig: Likely free (under 2B events)

• Savings: $6,000 annually

Growth-stage company (150 people):

• GrowthBook: Enterprise pricing (likely $3,000+/month)

• Statsig: ~$500-1,000/month based on usage

• Savings: $24,000+ annually

Enterprise (500+ people):

• GrowthBook: Custom pricing ($10,000+/month typical)

• Statsig: $2,000-5,000/month based on volume

• Savings: $60,000+ annually

The bundled platform amplifies these savings. GrowthBook requires separate purchases for analytics (Amplitude: $2,000+/month) and session replay (FullStory: $1,000+/month). Statsig includes both capabilities. Total cost reduction often exceeds 70% when accounting for tool consolidation.

Engineering leaders consistently highlight this value: predictable scaling costs, no seat-based surprises, and comprehensive capabilities without vendor sprawl.

Decision factors and implementation considerations

Implementation complexity and time-to-value

Self-hosting GrowthBook demands significant DevOps investment. The typical deployment requires:

1. Database provisioning (PostgreSQL or MySQL)

2. Container orchestration setup (Kubernetes recommended)

3. Data warehouse connection configuration

4. SDK integration across your stack

5. Metric definition and computation setup

GrowthBook's documentation provides thorough guides, but expect 2-4 weeks for production readiness. You'll need dedicated engineering resources throughout - both for initial setup and ongoing maintenance.

Cloud deployments simplify some steps but warehouse connections remain complex. Each data source requires custom SQL queries for metric computation. Changed your schema? Update every metric definition. Added a new event? Manual configuration required.

Statsig's automated setup typically completes in under an hour. The platform auto-discovers events, suggests metrics, and handles schema changes gracefully. Pre-built integrations with Segment, Amplitude, and Mixpanel eliminate most configuration work.

Support and documentation quality

Open-source support models reveal harsh realities at scale. GrowthBook's community channels work well for basic questions. Complex issues? You're debugging alone. Production outages? Hope someone in Slack responds quickly.

The support limitations compound with technical complexity:

• No SLAs on response times

• No dedicated technical account managers

• Community-dependent bug fixes

• Limited troubleshooting for enterprise scenarios

Critical experiments failing at 2 AM? Your options are GitHub issues or Slack messages. Neither guarantees timely resolution.

Total cost of ownership considerations

"Free" open-source hides substantial costs. A realistic TCO calculation for self-hosted GrowthBook includes:

• Infrastructure: $500-2,000/month for servers and databases

• Engineering time: 0.5-1 FTE for maintenance (~$75,000-150,000 annually)

• Security overhead: Quarterly audits and patch management

• Opportunity cost: Delayed experiments while troubleshooting

Stuart Allen from Secret Sales captured this reality: "We wanted a grown-up solution for experimentation." The hidden complexity of self-managed platforms often outweighs perceived savings.

Enterprise teams face additional burdens: compliance certifications, vendor security reviews, and audit requirements. Open-source projects rarely provide SOC 2 reports or sign BAAs - creating legal roadblocks for regulated industries.

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

The statistical engine difference defines everything else. GrowthBook's Bayesian-only approach simplifies some decisions but limits sophisticated experimentation. Statsig's dual-engine supports both Bayesian and Frequentist methods, unlocking:

• Advanced variance reduction techniques

• Sequential testing for continuous monitoring

• Proper power calculations

• Switchback and time-series experiments

This flexibility matters when experiments drive real business decisions. Teams at OpenAI and Notion run hundreds of concurrent tests - complexity that demands more than basic Bayesian analysis.

GrowthBook's per-seat pricing creates immediate friction. Growing teams face escalating costs just for accessing the platform. Statsig charges only for usage - unlimited users and feature flags at every tier. A 200-person team saves $4,000+ monthly on seat licenses alone.

The integrated platform amplifies these advantages:

• Single SDK for flags, experiments, and analytics

• Unified metrics across all features

• No data pipeline synchronization issues

• Built-in session replay for debugging

Where GrowthBook appeals to self-hosting purists, Statsig offers true flexibility. Deploy in the cloud for instant setup. Run warehouse-native on Snowflake or BigQuery for data sovereignty. Switch between modes as needs evolve. Enterprise teams get options without sacrificing capabilities.

Performance at scale shows the infrastructure difference. Statsig processes 2.3 million events per second with 99.99% uptime. Automated monitoring detects metric regressions instantly. Feature flags remain lightning-fast regardless of rule complexity. As Sumeet Marwaha from Brex noted:

"The biggest benefit is having experimentation, feature flags, and analytics in one unified platform. It removes complexity and accelerates decision-making."

For teams ready to move beyond basic A/B testing, the choice clarifies. GrowthBook works for simple Bayesian experiments with small teams. Statsig handles the full complexity of modern product development - from advanced statistics to enterprise-scale infrastructure.

Closing thoughts

Experimentation platforms shape how teams build products. The statistical methods you choose - Bayesian-only or flexible frameworks - determine which questions you can answer. The pricing model affects who can access insights. The architecture defines your technical overhead.

GrowthBook's open-source approach solves specific problems well. For teams wanting basic Bayesian testing with full control, it delivers. But modern experimentation demands more: sophisticated statistics, integrated workflows, and infrastructure that scales with ambition rather than headcount.

Want to explore further? Check out Statsig's experimentation guides or dive into statistical engine comparisons. The examples from companies like Notion and OpenAI show what's possible when platforms remove rather than create constraints.

Hope you find this useful!