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.
Statsig FAQs

Why is there a delay in reflecting changes in feature gates in Statsig?

When you make a change in the Statsig Console, such as updating a targeting rule, it is expected that the new rule may take around 5 seconds to propagate. This is in line with our Service Level Objective (SLO). While an instant update might be desirable, it's important to note that this slight delay typically has no real impact on key metrics and user experience.

In the context of feature gates, for example, if you open a feature for Mac users and later remove access for Mac users and make it available for Windows users, there will be a slight delay before the change is reflected for Mac users. This is due to the Time To Live (TTL) associated with any type of hosted service.

Regarding the capacity of ID lists, each project can handle up to 10 million IDs. This means if you have 3 projects, each can handle its own list of 10 million IDs.

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What builders love about us

OpenAI OpenAI
Brex Brex
Notion Notion
SoundCloud SoundCloud
Ancestry Ancestry
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.
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.
Karandeep Anand
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.
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.
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.
Partha Sarathi
Director of Engineering
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