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In Statsig, feature flags are called feature gates. The terminology is interchangeable throughout this guide.
Both feature gates and experiments create control/test groups. Use this guide to pick the right tool for your launch and measurement goals.

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Quick Guidance

  • Choose a feature gate when you want to roll out a feature gradually or monitor impact as you ramp.
  • Choose an experiment when you need to compare multiple variants and quantify the lift across metrics.

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Key Differences

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Variants

  • Feature gate → Two experiences only: pass vs. fail.
  • Experiment → Any number of variants.
When viewing gate exposures you’ll see three buckets: Pass, Fail, and Fail – Not in Analysis. Only the balanced subset of the fail group is used for metric comparisons. Learn more in the gate exposure methodology.

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Return Values

  • Feature gate → Boolean (true/false) so your application toggles code paths.
  • Experiment → JSON config that describes the variant (colors, copy, thresholds, etc.).

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Ramping knobs

  • Feature gate → Adjust Pass % to send more traffic to the new experience. You can go beyond 50/50 (e.g. 99% vs 1%).
  • Experiment → Adjust Allocation % to enroll more users, but splits cap at 50/50.
Once a user is assigned, neither control reshuffles existing users—you can safely ramp without re-bucketing.
Pass% versus Allocation% controls

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When Experiments Shine

Use experiments when you need:
  1. Multiple variants or personalization – compare more than two options or tailor experiences via contextual bandits/layers.
  2. Stable identifiers and custom IDs – analyze behavior before signup with stable IDs, or use custom IDs for sessions, workspaces, or geography.
  3. Isolated universes – run parallel experiments safely by placing them in their own layers.

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When Feature Gates Shine

Feature gates are great for:
  • Safe rollouts – gradually increase exposure while observing metrics.
  • Targeting audiences – use gates as pre-filters before enrolling users in an experiment.
In experiment setups, gates often act as targeting criteria. The flow looks like this:
  1. Targeting gate picks the eligible audience.
  2. Allocation % (experiment) decides how much of that audience participates.
  3. Split % distributes participants across variants.
Once you choose a winner, you can lift the targeting gate and let the winning variant reach everyone.

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Putting It Together

  • Start with a feature gate if you have a single variant to launch carefully.
  • Reach for experiments when you need quantitative comparisons across variants.
  • Combine both when you want precise audience control plus rigorous measurement.
Need more depth? Check out: