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Statsig vs. Eppo
Point solutions like Eppo can only take you so far with basic experimentation. Some might prefer the power and support of a flexible Cloud and Warehouse Native Experimentation platform.
Statsig's key advantages over Eppo are:
Most advanced experimentation, trusted by OpenAI & Notion
'Unscalable' customer obsession & support
A suite of collaboration tools
Built for enterprises and small teams alike
Easy analysis—not just for data scientists
Key Differences
Statsig and Eppo both offer product building platforms with Experimentation.
01
Most advanced Experimentation, Trusted by OpenAI & Notion
Statsig's experimentation, based on best practices from Facebook, is trusted as the most advanced and battle-tested Experimentation Platform available. With functionality like CUPED, Meta Analysis, A/A Testing, Stratified Sampling and more, Statsig has all of the bells and whistles for those who need them.
02
'Unscalable' customer obsession & support
At Statsig, we view support as an entirely different ballgame. We don’t have a regular customer support team, we have a Customer Engineering team. Our engineers, product managers, data scientists, and designers are our customer support team. Everyone at the company participates in Slack support, hops on calls with customers, and interacts with our users daily.
03
Flexible all-in-one platform
Love your assignment tool or looking to see other tools? Use Statsig for experiment analysis, leverage SDKs for end-to-end targeting and feature rollouts with holdouts and layers, or import offline experiments as part of a hybrid solution.
04
Built for enterprises and small teams alike
One of our core tenets is that we believe building products is a team sport. This is why we don’t charge based on seats: we believe everyone in the company should be in the Statsig Console, looking at dashboards, playing with data, and opting themselves in and out of new features to ensure they’re experiencing beta versions of the product. Whether you're a team of 1 or 100,000, Statsig scales with you.
05
Easy analysis—not just for data scientists
Build a culture or experimentation by making data more accessible. Empower team members with modern UI, collaboration tools, and no-code customer queries. Double-click on surprising results, create new Custom Metrics to track more granular results in future launches, or pin charts to your own Dashboards to check in on daily- all within a single platform.
Feature Comparison
Basic Experimentation
The basic features you need to measure feature impact.
Experiment Templates
Pre-defined templates for experiments
Bayesian & Frequentist
Support for both Bayesian and Frequentist experimentation methods
Exportable Experiment Summaries
Share or save experiment summaries
Holdouts
Ability to create holdout groups not exposed to any experiment treatments
Mutually Exclusive Experiments
Ensure experiments do not interfere with each other
Cloud Hosted Option
Cloud hosted experimentation supported
Warehouse Native Experimentation
Support for experimentation directly in your data warehouse
No-code experiments
Create experiments without coding
Advanced Experimentation
Advanced features for more complex experimentation needs.
CUPED
Method to reduce experiment runtime and increase accuracy with historical data
Switchback Tests
Testing method when traditional A/B testing is not possible due to implementation or Network effects
Stratified Sampling
Assign experiment subjects intelligently across groups
Sequential Testing
Method to 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.
Flag & Experiment Platform
Comprehensive features for flag and experiment management.
Basic Feature Flags
Simple flags to enable or disable features
Percentage Rollouts
Gradually roll out features to a percentage of users
Scheduled Rollouts
Roll out features based on a schedule
Environments
Support for multiple environments (dev, staging, prod)
Team-based Defaults
Default settings for teams in experiments
Edge SDKs
Support for SDKs at the edge
No-code Dynamic Configs
Create dynamic configurations without coding
Feature Gate Rollout Analysis
Analyze feature gates, not just experiments
EU-hosting
Support for hosting in the EU
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 Other Assignment Sources
Compatible with other assignment sources
Warehouse Native Product Analytics
Product analytics native to your data warehouse
* This comparison data is based on research conducted in July 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