Frequently Asked Questions

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Statsig FAQs
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GENERAL

How can we manage segment size limitations for early access features in Statsig?

Date of slack thread: 6/21/24

Anonymous: Context: we’re using a stat sig segment to control access to our ‘early access features’. We allow users in our app to toggle on if they want access to experimental features, and by doing so we add them to a segment, and reference that segment with feature gates to control access to features. However, the segment caps out at 1000 users, which we have just hit. As such, no one else can access this segment, and therefore the feature gates / features hooked up to this

My questions:

  • is there a way to increase the size of a segment to >1000?
  • is there a work around we could be using here, or how do other customers handle this type of situation/run an early access program w/o building something bespoke. thanks everyone! cc: <@U06TDA76S0H> <@U079JDNSJM6>

Cooper Reid (Statsig): <@U078VE7VD5G> The limit for ID Lists is now 1,000 — this can’t be “raised”.

You can use a conditional rule such as: Custom Field (doc) in_user_segment = true

and then, your application must determine if the user is in that large list of IDs and pass in bool true/false accordingly. When you initialize the SDK, the user object should the corresponding field:

user object

  userID: '<example>',
  custom: {
    in_user_segment: true|false
  }
}```

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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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