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Brief
Statsig

Statsig

Statsig consolidates feature flags, A/B testing, and product analytics into a single platform with warehouse-native architecture, enabling product teams to ship faster and experiment with the rigor of top-tier tech companies.

statsig.comBellevue, Washington, United StatesFounded 2021

Last updated May 11, 2026 by ATDb automated enrichment · Connections updated May 14, 2026

Industry
Experimentation & Feature Management / MarTech
Business Model
SaaS
Target Market
Mid-Market and Enterprise
Employee Count
51-200
Funding
$43.5M
Revenue Range
$10M-$50M ARR
API Available
Yes
Market Position

Emerging challenger to legacy experimentation platforms, positioned as the all-in-one modern alternative with warehouse-native capabilities and strong statistical rigor

Overview

Statsig is a feature management and product experimentation platform founded in 2021 by former Facebook engineers, including CEO Vijaye Raji. The company was built to democratize the kind of sophisticated experimentation infrastructure that only large tech companies like Facebook, Google, and Microsoft had previously been able to build internally. Statsig's unified platform combines feature flags, A/B testing, product analytics, session replay, and a data warehouse-native architecture into a single cohesive environment, eliminating the need for teams to stitch together multiple disparate tools. The platform is designed for high-velocity product teams that need to move fast without sacrificing statistical rigor. Statsig's warehouse-native offering allows companies to run experiments directly on top of their existing data infrastructure (Snowflake, BigQuery, Databricks, etc.), giving them full data ownership and eliminating data duplication concerns. Its CUPED and sequential testing methodologies provide enterprise-grade statistical accuracy, while its real-time analytics layer enables teams to monitor experiment results and feature rollouts as they happen. Statsig has gained significant traction among technology companies ranging from fast-growing startups to large enterprises, positioning itself as a credible alternative to legacy tools like Optimizely and LaunchDarkly, as well as homegrown experimentation systems. The company competes in the broader experimentation and feature management market, which is increasingly seen as critical infrastructure for product-led growth organizations. Statsig's strong engineering pedigree, competitive pricing, and all-in-one platform approach have made it a notable player in the MarTech and product analytics ecosystem.

Products & Features

Feature Flags

Gradual rollouts, targeted feature gating, and kill switches with real-time control over feature exposure across user segments

A/B Testing & Experimentation

Rigorous experiment framework with CUPED variance reduction, sequential testing, and Bayesian/frequentist statistical methodologies

Product Analytics

Real-time event-based analytics with funnels, retention, and user journey analysis tied directly to feature and experiment data

Session Replay

Visual session recording and playback to understand user behavior in context of feature rollouts and experiments

Statsig Warehouse Native

Run experiments and compute metrics directly on top of existing data warehouses like Snowflake, BigQuery, and Databricks without data leaving the customer environment

Autotune

Multi-armed bandit optimization that automatically allocates traffic to winning variants in real time

Metrics Catalog

Centralized metric definitions and a shared metrics layer ensuring consistency across experiments and analytics

Holdouts

Global holdout groups to measure the cumulative impact of all shipped features over time

Dynamic Config

Remote configuration management allowing teams to change app behavior without code deployments

Key Features
Warehouse-native experimentation (Snowflake, BigQuery, Databricks)CUPED variance reduction for faster experiment resultsSequential testing and always-valid p-valuesUnified feature flags and A/B testing in one platformReal-time analytics and pulse resultsMulti-armed bandit / Autotune optimizationGlobal holdouts for cumulative impact measurementSDKs for web, mobile, and server-side environmentsMetrics catalog with centralized definitionsSession replay integrated with experiment data
Use Cases
Product feature rollouts and gradual releasesA/B testing UI and UX changesPricing and monetization experimentsAlgorithm and ranking experimentsInfrastructure and backend experimentationMobile app feature managementMeasuring cumulative impact of shipped features via holdoutsReplacing homegrown experimentation platformsData warehouse-native experimentation for data-mature organizations
Customer Segments
High-growth technology startupsMid-market SaaS companiesEnterprise technology companiesE-commerce and marketplace platformsMobile-first product companiesData-mature organizations with existing warehouse infrastructure

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