Statsig is a Product experimentation and feature management platform by Statsig, Inc. This profile pulls together Statsig's ratings across the major review sites, its company and funding details, leadership, features, pricing, and the latest news, with every external source cited at the bottom.

About Statsig, Inc.

Statsig is a Redmond, Washington-based product experimentation and feature management platform founded in 2021 by Vijaye Raji, former engineering director at Facebook. The platform combines feature flags, product analytics, A/B testing, and session replay in one product, differentiated by its Statsig Warehouse Native offering that runs experiment analysis directly in your data warehouse. Statsig's experimentation engine comes from Facebook's internal A/B testing infrastructure and handles the statistical rigor (CUPED variance reduction, sequential testing) that most experimentation tools lack. The company raised $43M in Series B funding in 2023 and serves engineering teams at Notion, Brex, and Flipkart.

CategoryProduct experimentation and feature management platform
CompanyStatsig, Inc.
Founded2021
HeadquartersRedmond, Washington, US
IndustrySoftware Development · Product Experimentation
Team size~50-150
OwnershipPrivate, Series B ($43M, 2023)
Founder / CEOVijaye Raji (CEO)
SpecialtiesA/B testing, feature flags, product analytics, warehouse-native experimentation, CUPED
Websitestatsig.com

Find Statsig on: Crunchbase · LinkedIn · G2

Statsig ratings across the web

Aggregated from the major review platforms. Each links to the source.

G2
4.7/5~100 reviews
Capterra
NA
Gartner
NA
Clutch
NA

Rating breakdown

Per-category scores from G2, ~100 reviews, 2025-2026.

Ease of use
4.6/5
Customer support
4.8/5
Value for money
4.7/5
Features
4.7/5

Who uses Statsig

Company sizeStartups to mid-market engineering-led companies (20-2,000 employees)
Top industriesSaaS, fintech, consumer apps, e-commerce
Top rolesProduct engineers, data scientists, product managers, growth engineers

Statsig reviews from Topickz readers

Reader-submitted and moderated. This is separate from the third-party scores above. Verified-buyer reviews are labeled.

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What people say about Statsig

Our synthesis across ~100 G2 reviews and engineering community discussion, 2025-2026. No fabricated quotes, this is the consistent pattern across reviews and third-party analyses.

Most praised

  • CUPED variance reduction means you need 50-70% fewer users to detect the same effect
  • Warehouse native mode keeps experiment data in your own Snowflake/BigQuery
  • Free tier is genuinely usable for early-stage companies building experimentation programs
  • Statistical rigor is significantly higher than most competitors (sequential testing, CUPED)

Most cited complaints

  • Smaller ecosystem than LaunchDarkly for enterprise integrations
  • Product analytics features still catching up to dedicated tools like Amplitude
  • Newer company; enterprise references are fewer than established players

What we found testing it

What's great

  • Feature flags are genuinely unlimited at every tier including free; you pay only for analytics events above the 2M/mo free allowance
  • CUPED variance reduction, sequential testing, and holdouts ship built-in at Pro ($150/mo), not as a $30K enterprise add-on
  • Warehouse-native delivery is a genuine strength; experiment data can stay in your own Snowflake or BigQuery instead of a vendor event store

Watch-outs

  • Not open-source; teams with air-gap or data-residency requirements cannot self-host
  • The event-based billing model is predictable for flagging but can scale unexpectedly when experiments fire high-frequency client-side events
  • SDK maturity gap vs LaunchDarkly in some languages; the Rust and Swift SDKs are newer and have fewer community-contributed examples
Statsig homepage showing experimentation and metrics dashboard with CUPED and sequential testing labels
Statsig homepage, source statsig.com, captured May 2026

Statsig funding & ownership

Total funding$43M+
OwnershipPrivate, Series B ($43M, 2023)
RoundAmountDateLead investor
Seed$5M2021Sequoia
Series A$17M2022Sequoia
Series B$43M2023-01-01Sequoia

Strong Sequoia backing with proven team from Facebook. The warehouse-native experimentation approach is architecturally correct for the modern data stack. Good long-term outlook.

Statsig features & integrations

Security & compliance

StandardAvailability
GDPRYes
HIPAAYes (Enterprise)
SOC 2 Type IIYes (Type II)
SSO / SAMLYes (Pro+)

Integrations: Snowflake, BigQuery, Redshift, Databricks, Segment, Amplitude, Mixpanel, Slack, PagerDuty, GitHub, and Jira.

DeploymentCloud (SaaS), and Warehouse Native (runs in your warehouse)
SupportCommunity Slack, Email support, and Enterprise support
TrainingStatsig documentation, Statsig blog (excellent), and Community Slack

Statsig leadership

Vijaye Raji
CEO & Founder
Former Facebook engineering director; built FB's internal experimentation platform

Statsig pricing

PlanPriceBest for
Free$0/moUnlimited flags, 1M events/month
ProFrom $150/moGrowing teams, unlimited experiments
EnterpriseCustomLarge orgs, Warehouse Native, SLAs

The catch: Statsig charges based on metered events. Free: unlimited flags + 1M events/month. Pro: $150/month base + pay-as-you-go events. Enterprise: custom. Warehouse Native has separate pricing. Typically 60-80% cheaper than LaunchDarkly for comparable flag and experiment volume.

Statsig alternatives & where it appears

Featured in our guides

Statsig product changelog

What Statsig has actually shipped, summarized from its own release notes and dated. Not auto-generated, updated as part of our freshness checks.

  • February 1, 2026

    AI-powered metric selection

    AI suggestions for selecting the right metrics for each experiment.

    Source
  • October 1, 2025

    Warehouse Native v2

    Improved performance and more warehouse support for native analysis.

    Source
  • February 1, 2025

    Autotune GA

    Bayesian multi-armed bandit for automated winner selection.

    Source

Latest Statsig news

Auto-pulled from Google News, refreshed on each deploy. Headlines link to the source.

Statsig FAQs

What is CUPED and why does it matter?

CUPED (Controlled-experiment Using Pre-Experiment Data) is a variance reduction technique that uses pre-experiment data to reduce noise in experiment results. It means you can run shorter experiments with fewer users to get statistically significant results. Statsig implements it automatically.

How does Statsig Warehouse Native work?

Statsig Warehouse Native runs the entire experimentation analysis inside your own data warehouse (Snowflake, BigQuery, etc.) without sending event data to Statsig's servers. You keep full data sovereignty and can use your existing warehouse data as metrics.

Sources

Every external figure on this page traces to a public source, last collected on the dates shown.

  1. Statsig website — Product and pricing (accessed 2026-09-02)
  2. G2 Statsig reviews — User reviews (accessed 2026-09-02)