Platform
Developers
Resources
KEY FEATURES
GETTING STARTED
Documentation
Documentation to get you started with implementation
Walkthrough Guides
Guides to get you started with Statsig in no time
SDKs and APIs
Explore REST API and SDKs in more than 20 frameworks
Integrations
Learn more about connecting Statsig to your existing tools
A/B Testing Calculator
Looking for a place to start your A/B Test? Try out our calculator
How Statsig Works
Get under the hood and check out how Statsig scales with you
LEARN & CONNECT
Blog
Peak Velocity is our blog where we cover the latest in data
Support
Need help getting set up or have questions about our product?
Customer Stories
Find out how leading companies are using Statsig to grow
Events
Find out about our online and offline events and RSVP to them
Build vs Buy
Compare building an in-house platform vs. buying
Contact Sales
Want to connect with someone from the Sales team?
FEATURED BLOGS
Feature Flags Liberated
Gating features is a core part of the development process. And with Statsig, it's free.
How AI Companies Use Statsig
The best AI companies use Statsig to accelerate growth. Learn how you can do the same.
What is Product Observability?
Product observability means being able to monitor, control, and gain insight into all of your features.KEY FEATURES
GETTING STARTED
Documentation
Documentation to get you started with implementation
Walkthrough Guides
Guides to get you started with Statsig in no time
SDKs and APIs
Explore REST API and SDKs in more than 20 frameworks
Integrations
Learn more about connecting Statsig to your existing tools
A/B Testing Calculator
Looking for a place to start your A/B Test? Try out our calculator
How Statsig Works
Get under the hood and check out how Statsig scales with you
LEARN & CONNECT
Blog
Peak Velocity is our blog where we cover the latest in data
Support
Need help getting set up or have questions about our product?
Customer Stories
Find out how leading companies are using Statsig to grow
Events
Find out about our online and offline events and RSVP to them
Build vs Buy
Compare building an in-house platform vs. buying
Contact Sales
Want to connect with someone from the Sales team?
FEATURED BLOGS
Experiments with Generative AI
We built a generative AI app in reactJS using OpenAI’s API and Statsig. Here’s what we learned:
Experimentation Platforms
The decision to build versus buy an experimentation and feature flagging platform is not an easy one.
CUPED Explained
CUPED is an implementation that uses pre-experiment data to explain the variance in the result data.Frequently Asked Questions
A curated summary of the top questions asked on our Slack community, often relating to implementation, functionality, and building better products generally.
How to interpret pre-experiment results in experimentation data
When reviewing experimentation results, it is crucial to understand the significance of pre-experiment data. This data serves to highlight any potential pre-existing differences between the groups involved in the experiment. Such differences, if not accounted for, could lead to skewed results by attributing these inherent discrepancies to the experimental intervention.
To mitigate this issue, a technique known as CUPED (Controlled-Experiment Using Pre Experiment Data) is employed.
CUPED is instrumental in reducing variance and pre-exposure bias, thereby enhancing the accuracy of the experiment results. It is important to recognize, however, that CUPED has its limitations and cannot completely eliminate bias. Certain metrics, particularly those like retention, do not lend themselves well to CUPED adjustments.
In instances where bias is detected, users are promptly notified, and a warning is issued on the relevant Pulse results. The use of pre-experiment data is thus integral to the process of identifying and adjusting for pre-existing group differences, ensuring the integrity of the experimental outcomes.
Join the #1 Community for Product Experimentation
Connect with like-minded product leaders, data scientists,
and engineers to share the latest in product experimentation.
What builders love about 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
CPO
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