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Brief
Eppo

Eppo

Eppo provides a warehouse-native experimentation platform that combines statistical rigor with ease of use, allowing companies to run trustworthy A/B tests while maintaining full control of their data in their own data warehouse.

eppo.cloudSan Francisco, California, United StatesFounded 2021

Last updated May 11, 2026 by the ATDb Editorial Team · Connections updated May 14, 2026

Industry
Experimentation & Analytics Platform
Business Model
SaaS
Target Market
Mid-Market, Enterprise
Employee Count
51-200
Funding
$47M
API Available
Yes
Market Position

Emerging warehouse-native experimentation platform competing with established players like Optimizely and LaunchDarkly

Overview

Eppo is a modern experimentation and feature flagging platform that enables companies to run statistically rigorous A/B tests and make data-driven product decisions. Founded to address the limitations of traditional experimentation tools, Eppo connects directly to companies' existing data warehouses (like Snowflake, BigQuery, and Databricks) rather than requiring data to be sent to a third-party system. This warehouse-native approach allows data teams to maintain control over their data while providing product teams with a user-friendly interface for experiment design and analysis. While not strictly an AdTech company, Eppo serves the broader marketing technology and product analytics ecosystem, with particular relevance to companies optimizing digital experiences, ad performance, and conversion funnels. The platform is designed for modern data-driven organizations that want to democratize experimentation across product, engineering, and marketing teams while maintaining statistical rigor. Eppo has gained traction among growth-stage technology companies and enterprises looking for a more flexible, transparent alternative to legacy experimentation platforms.

Products & Features

Experimentation Platform

Core A/B testing and experimentation platform with advanced statistical methods including sequential testing and CUPED variance reduction

Feature Flagging

Feature management system for controlled rollouts and experimentation

Warehouse-Native Architecture

Direct integration with data warehouses for analysis without data movement

Statistical Analysis Engine

Advanced statistical methods for experiment analysis with built-in guardrails against common pitfalls

Key Features
Warehouse-native architectureSequential testing and early stoppingCUPED variance reductionMulti-armed bandit optimizationAutomated experiment analysisMetric guardrailsFlexible randomization unitsReal-time experiment monitoringStatistical rigor and transparency
Use Cases
Product feature A/B testingMarketing campaign optimizationPricing experimentationUser experience optimizationConversion funnel optimizationPersonalization testingAd creative and targeting optimizationGrowth experimentation
Customer Segments
Technology companiesE-commerce platformsSaaS companiesDigital media companiesConsumer appsMarketplaces

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