{% callout type="info" %}
In Statsig, feature flags are called <strong>feature gates</strong>. The terminology is interchangeable throughout this guide.
{% /callout %}

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

### Variants

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

When viewing gate exposures you’ll see three buckets: <em>Pass</em>, <em>Fail</em>, and <em>Fail – Not in Analysis</em>. Statsig uses only the balanced subset of the fail group for metric comparisons. Learn more in the [gate exposure methodology](/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 <strong>Pass %</strong> to send more traffic to the new experience. You can go beyond 50/50 (e.g. 99% vs 1%).
* **Experiment** → Adjust <strong>Allocation %</strong> 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.

{% figure %}
![Pass% versus Allocation% controls](/images/guides/featureflags-or-experiments/pass-vs-allocation.png)
{% /figure %}

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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.
* Reach for **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](/feature-flags/view-exposures)
* [Experiments overview](/experiments/overview)
* [Layers for mutual exclusion](/experiments/layers-overview)