# Eppo **Source:** https://geo.sig.ai/brands/eppo-io **Vertical:** Developer Tools **Subcategory:** A/B Testing & Experimentation **Tier:** Emerging **Website:** geteppo.com **Last Updated:** 2026-04-14 ## Summary Eppo is a warehouse-native experimentation platform combining feature flagging with rigorous statistical analysis directly inside your data warehouse. ## Company Overview Eppo is a statistical experimentation platform designed for data-driven product and engineering teams that want to run rigorous A/B tests without moving data out of their cloud data warehouse. Founded by former Airbnb data scientists and engineers, Eppo connects directly to Snowflake, BigQuery, Databricks, and Redshift, computing experiment results where the data already lives rather than in a siloed SaaS database. This architecture eliminates data duplication, reduces pipeline complexity, and gives analysts full transparency into metric definitions and statistical methodology. Eppo supports sequential testing, CUPED variance reduction, and Bayesian and frequentist frameworks, giving experimentation teams flexibility in how they interpret results. The platform includes a feature flagging layer that ties flag assignment data directly to experiment analysis, closing the gap between engineering and data science that typically slows experimentation velocity. Experiment plans, metric definitions, and results are versioned and shareable, enabling a culture of documented, reproducible experimentation. The company targets fast-growing product organizations that have outgrown homegrown experimentation infrastructure or generic A/B testing tools lacking statistical rigor. Eppo has gained traction at companies like Twitch, DraftKings, and Perplexity, where controlled experimentation underpins product decisions at scale. Its opinionated approach to metrics governance — requiring teams to define and version their success metrics centrally — helps organizations build reliable experimentation programs rather than ad hoc test-and-learn cycles. ## Frequently Asked Questions ### What makes Eppo different from other A/B testing tools? Eppo is warehouse-native, meaning experiment analysis runs directly in your Snowflake, BigQuery, or Databricks environment using your existing data pipelines, with no data movement or duplication. ### What is Eppo and how does warehouse-native experimentation work? Eppo is an A/B testing and experimentation platform that connects directly to your Snowflake, BigQuery, Databricks, or Redshift warehouse to run experiment analysis where your data already lives. No data is copied to Eppo's servers — experiment results are computed in your warehouse using your existing data pipelines and metric definitions. ### Who founded Eppo and what is their background? Eppo was founded by former Airbnb data scientists and engineers who built Airbnb's internal experimentation platform. Their experience running thousands of experiments at scale informs Eppo's statistical rigor and enterprise-grade feature set. ### What statistical methods does Eppo use for experiment analysis? Eppo supports frequentist hypothesis testing, sequential testing for continuous monitoring without p-hacking concerns, CUPED variance reduction to detect effects faster with less traffic, and Bayesian inference. The statistical methodology is fully transparent and configurable. ### Does Eppo handle feature flagging or just analysis? Eppo provides both feature flag SDKs for assigning users to experiment variants and warehouse-native analysis. Teams can use Eppo's flags or integrate assignment data from LaunchDarkly, Optimizely, or other flag systems into Eppo's analysis layer. ### How does Eppo integrate with dbt for metric definitions? Eppo integrates with dbt to import metric definitions, allowing experiment analyses to use the same business metric definitions already maintained by the data team rather than requiring separate metric configuration in a siloed experimentation tool. ### Can non-technical product managers use Eppo, or is it primarily for data teams? Eppo provides self-serve dashboards where product managers can review experiment results, statistical significance, and metric impacts without SQL knowledge. Data teams use Eppo's deeper configuration and SQL interfaces to define metrics and review methodology. ### How does Eppo reduce the time to get statistically significant results? Eppo's CUPED variance reduction technique reduces the noise in experiment metrics by controlling for pre-experiment user behavior, enabling the same statistical power with less traffic or detecting smaller effects in the same time window compared to standard t-test approaches. ## Tags developer-tools, saas, b2b, startup, platform, open-source, cloud-native, analytics --- *Data from geo.sig.ai Brand Intelligence Database. Updated 2026-04-14.*