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

Is there a limit to the number of dynamic configs in Statsig and what are the effects of having a large number?

In Statsig, there is no hard limit to the number of dynamic configs you can create. However, the number of configs can have practical implications, particularly on the response size and latency.

Having a large number of dynamic configs can impact the initialization for both server and client SDKs. For Server SDKs, they will have to download every single config and all of their payloads during initialization, and on each polling interval if there’s an update available. This won't necessarily impact user experience, but it does mean large payloads being downloaded and stored in memory on your servers. You can find more information on Server SDKs here.

On the other hand, Client SDKs, where 'assignment' takes place on Statsig’s servers by default, will have to download user-applicable configs and their payloads to the user’s device during initialization. This increases the initialization latency and could potentially impact user experience. More details on Client SDKs can be found here.

In conclusion, while there is no explicit limit to the number of dynamic configs, having a large number can increase complexity and affect performance due to the increased payload size and latency. Therefore, it's important to consider these factors when creating and managing your dynamic configs in Statsig.

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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.
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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.
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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.
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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.
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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.
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