# Statsig

Statsig is a product experimentation and feature flagging platform rated 4.7/5 on G2 from 100+ reviews. Profile: pricing, warehouse-native analysis, and how it compares to LaunchDarkly.

**Category:** Product experimentation and feature management platform  
**Vendor:** Statsig, Inc.  
**Founded:** 2021  
**HQ:** Redmond, Washington, US  
**Website:** https://statsig.com  


## About

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.

## Ratings

| Source | Score | Reviews |
|---|---|---|
| G2 | 4.7/5 | ~100 |

## Funding

$43M+

| Round | Amount | Date | Lead |
|---|---|---|---|
| Seed | $5M | 2021 | Sequoia |
| Series A | $17M | 2022 | Sequoia |
| Series B | $43M | 2023-01-01 | Sequoia |

## Viability

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.

## Pricing

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.

| Plan | Price | Best for |
|---|---|---|
| Free | $0/mo | Unlimited flags, 1M events/month |
| Pro | From $150/mo | Growing teams, unlimited experiments |
| Enterprise | Custom | Large orgs, Warehouse Native, SLAs |

## What reviewers say

_~100 G2 reviews and engineering community discussion, 2025-2026_

**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)

**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

## Compliance

- **SOC 2:** Yes (Type II)
- **GDPR:** Yes
- **HIPAA:** Yes (Enterprise)
- **SSO:** Yes (Pro+)

## Integrations

- Snowflake
- BigQuery
- Redshift
- Databricks
- Segment
- Amplitude
- Mixpanel
- Slack
- PagerDuty
- GitHub
- Jira

## 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

- [Statsig website](https://statsig.com): Product and pricing
- [G2 Statsig reviews](https://www.g2.com/products/statsig/reviews): User reviews