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Statsig vs. Eppo
While Eppo is a strong tool for experiment analysis, Statsig is the platform of choice for companies looking for a warehouse-native experimentation platform that is reliable, trustworthy, and scalable
Statsig's key advantages over Eppo are:
80% of companies that trialed both Eppo and Statsig in 2024 chose Statsig
The world's most sophisticated experimentation programs rely on Statsig
Our data and product team will partner with you to build a successful program
We've solved the problems that block companies from scaling experimentation
We're loved by the entire team - from data, to product, to engineering
Our infrastructure enables an end-to-end experimentation system
Key Differences
Statsig and Eppo both offer product building platforms with Experimentation.
01
80% of companies that trialed both Eppo and Statsig in 2024 chose Statsig
Since we've launched our Warehouse Native product, 4 out of 5 companies that do a hands-on POC with both Eppo and Statsig have chosen Statsig - including HelloFresh, Monzo, Mistplay, Medium.com, and others.
02
The world's most sophisticated experimention programs rely on Statsig
Companies like OpenAI, Figma, Notion and Vista have built their experimentation programs around Statsig, because of our reliable end-to-end capabilities, advanced stats engine, developer experience, and breadth of test and metric types.
03
Our data and product team will partner with you
By working with companies like WhatNot (0 to 400 annual experiments), Notion (single digit to 1000s of annual experiments), and many others, we've developed frameworks and trainings to build a successful, trustworthy experimentation program. As a Statsig customer, you'll have direct access to all these resources & the people that created them.
04
We solve the problems that block experimentation at scale
With features like a standard logging layer, semantic layer support, team- and org-based policies, RBAC, and templates for experiments, you can eliminate friction when scaling experimentation. Eppo customers celebrate the time saved analyzing experiments, while Statsig customers get a 10 - 20X increase in experimentation velocity.
05
We're loved by the entire team - from data, to product, to engineering
Tens of thousands of people are building with Statsig, across all techincal functions. Our 30+ SDKs are highly performant and get 10 - 20X the usage of comparable Eppo SDKs. Advanced features enabled by SDKs (including holdouts, layers, and chained targeting) have been hardened on Statsig over 3 years - but are new features for Eppo.
06
Our infrastructure enables an end-to-end experimentation system
You can leverage Statsig's infrastructure to create a complete end-to-end experimentation system, with features like real-time logging from SDKs to your warehouse, real-time diagnostics, automated health checks, and more. Eppo does not support a comparable set of capabilities.
Feature Comparison
Core Experimentation
The basic features you need to measure feature impact.
Warehouse Native Experimentation
Run experiment analysis in your data warehouse
Hosted Experimentation Platform
Run experimentation in a platform-hosted environment
Bayesian & Frequentist
Support for both Bayesian and Frequentist methods
Experiment Templates & Summaries
Save experiment templates and export formatted reports
Holdouts
Create & manage long-term holdout groups
Mutually Exclusive Experiments
Ensure experiments do not interfere with each other
No-code experiments
Create experiments with a visual editor
Custom metric configuration
Create custom metrics using standard metric types
Advanced metrics settings
Modify metrics with filters, Winsorization, capping, and more
Performance metrics
Native support for A/B testing on percentile metrics
Growth accounting metrics
Out-of-the-box support for retention, stickiness, and churn
Days since exposure view
Cohort-based time series for identifying novelty effects
Advanced tests, methods, and techniques
Advanced experimentation features for powerful tests & advanced analysis
CUPED
Method to reduce experiment runtime and increase accuracy with historical data
Switchback Tests
Run tests when traditional randomization isn't feasible
Stratified Sampling
Balance heterogenous subjects across experiment groups
Sequential Testing
Prevent early-peeking on A/B test results
Winsorization
Reduce the influence of outliers
Bonferroni Correction
Adjust for multiple comparisons
Non-inferiority Tests
Tests to show a treatment is not worse than a control
Benjamini-Hochberg Procedure
Control the false discovery rate in multiple hypothesis tests.
Advanced explore queries
Filter results based dates, segments, or cohorts - with all advanced stats techniques (e.g., CUPED)
Automated heterogenous effect detection
Identify classes of users that are differentially impacted
Interaction effect detection
Detect effects of overlapping experiment
Statistical tests between all variant groups
Compare all variants to any other variant
Feature Management & Experimentation Infrastructure
Comprehensive features for flag and experiment management.
Feature Flags
Simple flags to enable or disable features
Environments
Support for multiple environments (dev, staging, prod)
Team-based Defaults
Default settings for teams in experiments
30+ Client & Server SDKs
A complete set of highly performant SDKs
Edge SDKs
Support for SDKs at the edge
No-code Dynamic Configs
Create dynamic configurations without coding
EU-hosting
Support for hosting in the EU
Managed real-time logging
Managed real-time logging to your SDKs to your warehouse
Warehouse Native Experimentation
Native support for popular data warehouses.
Snowflake Support
Support for Snowflake data warehouse
Bigquery Support
Support for Bigquery data warehouse
Redshift Support
Support for Redshift data warehouse
Databricks Support
Support for Databricks data warehouse
Athena Support
Support for Athena data warehouse
Flexible Hybrid Cloud/Warehouse Solutions
Support for hybrid cloud and warehouse solutions
Compatibility with 3rd party experiment assignment data
Compatible with other assignment sources
Warehouse Native Product Analytics
Product analytics native to your data warehouse
Fully transparent SQL queries in console
All experiment and analytics queries are directly visible with 1 click
* This comparison data is based on research conducted in November 2024.
Why the best build with us
At OpenAI, we want to iterate as fast as possible. Statsig enables us to grow, scale, and learn efficiently. Integrating experimentation with product analytics and feature flagging has been crucial for quickly understanding and addressing our users' top priorities.
OpenAI
Dave Cummings
Engineering Manager, ChatGPT
Brex's mission is to help businesses move fast. Statsig is now helping our engineers move fast. It has been a game changer to automate the manual lift typical to running experiments and has helped product teams ship the right features to their users quickly.
Brex
Karandeep Anand
President
At Notion, we're continuously learning what our users value and want every team to run experiments to learn more. It’s also critical to maintain speed as a habit. Statsig's experimentation platform enables both this speed and learning for us.
Notion
Mengying Li
Data Science Manager
We evaluated Optimizely, LaunchDarkly, Split, and Eppo, but ultimately selected Statsig due to its comprehensive end-to-end integration. We wanted a complete solution rather than a partial one, including everything from the stats engine to data ingestion.
SoundCloud
Don Browning
SVP, Data & Platform Engineering
We only had so many analysts. Statsig provided the necessary tools to remove the bottleneck. I know that we are able to impact our key business metrics in a positive way with Statsig. We are definitely heading in the right direction with Statsig.
Ancestry
Partha Sarathi
Director of Engineering