Experimentation & feature flags · Statsig

Statsig

Warehouse-native experimentation and feature-flag platform offering frequentist sequential testing, SPRT, Bayesian mode, and CUPED.

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Statsig combines feature flags with a statistics-heavy experimentation engine, and can run either on its own event pipeline or in a warehouse-native mode that reads directly from a customer's warehouse, unlike pure flag platforms (LaunchDarkly, Split, Unleash, Flagsmith) that are limited to their own SDK telemetry. It offers three analysis modes: frequentist with mixture Sequential Probability Ratio Test (mSPRT) sequential testing as the default, a dedicated SPRT mode that permits unlimited peeking and can accept the null hypothesis, and a Bayesian mode reporting chance-to-beat and expected loss. It applies CUPED-based variance reduction automatically by default, using a stratification approach that retains users with missing pre-experiment data, and reports the technique reduces variance on the large majority of metrics it has been applied to. Deployment is cloud SaaS, with the warehouse-native option available on Enterprise plans.

At a glance

Vendor Statsig
Pricing model Free tier + paid plans
Free tier Yes
Deployment Cloud
Open source No
Best for Teams wanting a choice of rigorous statistical methods (sequential, SPRT, Bayesian) with automatic variance reduction.

Pricing

Free Developer plan with 2M events/month; Pro is $150/month with 5M events included; Enterprise is custom, event- or experiment-based.

Plan Price Notes
Developer $0/month 2M events/month, unlimited flag/config checks, 50,000 session replays/month
Pro $150/month 5M events included, then $0.05 per additional 1K events; 100,000 session replays/month
Enterprise custom warehouse-native deployment option, volume discounts

Prices read from the vendor's own page on September 21, 2026. Vendors change prices; check the source before you budget.

Features

  • Feature flags and dynamic configs
  • Default mSPRT sequential testing (always-valid confidence intervals)
  • Dedicated SPRT mode for unlimited peeking
  • Bayesian mode (chance-to-beat, expected loss)
  • Automatic CUPED variance reduction with stratification
  • Optional warehouse-native deployment

Integrations

Profile last reviewed September 21, 2026

Head to head

Alternatives

GrowthBook Open-source, warehouse-native experimentation and feature-flag platform offering both Bayesian and frequentist sequential analysis with CUPED. Datadog Experiments (formerly Eppo) Warehouse-native experimentation platform known for its CUPED implementation, acquired by Datadog in 2025 and rebranded Datadog Experiments. LaunchDarkly Feature-flag management platform that also runs experiments on flagged rollouts, reading event data it collects itself rather than the warehouse.

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Terms to know

A/B testing A controlled experiment that compares two variants to measure which one produces a better outcome. Statistical significance A threshold judgment that an observed effect is unlikely to be due to chance alone, given the data and test used. Confidence interval A range calculated from sample data that, under repeated sampling, would contain the true value a stated percentage of the time. Sample size The number of observations in a study or test, which determines how precisely and reliably an effect can be measured. CUPED (controlled-experiment using pre-experiment data) A variance-reduction technique that uses each user's pre-experiment behavior to sharpen an experiment's estimate without adding more traffic. Null hypothesis The default assumption in a statistical test, typically that there is no effect or no difference, until evidence says otherwise.

Related guides

How to choose an experimentation and feature-flag platform Pick by where your experiment data comes from — flag telemetry or your own warehouse — and which statistical engine your team trusts, not feature count. How to set up an experimentation program From your first A/B test to a durable program — the plumbing, the statistics rules, the review ritual, and the tools that support each stage.