When to Use Feature Gates vs. Experiments?

Use a feature gate to control who sees a feature and roll it out gradually; use an experiment to compare variants and measure how a change affects your metrics. Both create control and test groups, so this guide shows how to pick the right one for your launch and measurement goals.

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.

Key differences

Variants

• Feature gate → Two experiences only: pass vs. fail.
• Experiment → Any number of variants.

Return values

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

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.

After Statsig assigns a user, neither control reshuffles existing users. You can safely ramp without re-bucketing.

When experiments shine

Use experiments when you need:

1. Multiple variants or personalization: compare more than two options or tailor experiences using contextual bandits or 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.

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.

After you choose a winner, lift the targeting gate and let the winning variant reach everyone.

Summary: choosing the right tool

• Start with a feature gate if you have a single variant to launch carefully.
• Choose experiments when you need quantitative comparisons across variants.
• Combine both when you want precise audience control plus rigorous measurement.

For more detail, refer to:

• Feature gate exposures
• Experiments overview
• Layers for mutual exclusion