---
title: "When to Use Feature Gates vs. Experiments?"
description: "Decide whether to ship with a feature gate or run an experiment, and understand how the two work together."
product: general
token_estimate: 889
---
# When to Use Feature Gates vs. Experiments?

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> **Info:**
>
> Statsig calls feature flags **feature gates**. The terminology is interchangeable throughout this guide.

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.

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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

### Variants

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

When viewing gate exposures you see three buckets: _Pass_, _Fail_, and _Fail – Not in Analysis_. Statsig uses only the balanced subset of the fail group for metric comparisons. Learn more in the [gate exposure methodology](https://docs.statsig.com/feature-flags/view-exposures#gate-exposures).

### 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.

![Pass% versus Allocation% controls](https://docs.statsig.com/images/guides/featureflags-or-experiments/pass-vs-allocation.png)

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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 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.

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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.

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

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## 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](https://docs.statsig.com/feature-flags/view-exposures)
- [Experiments overview](https://docs.statsig.com/experiments/overview)
- [Layers for mutual exclusion](https://docs.statsig.com/experiments/layers-overview)