Overview

When you define a targeting rule, you can serve a variant to a percentage of subjects that match your targeting filter. Datadog uses deterministic randomization based on the targetingKey in your evaluation context so the same subject consistently receives the same variant for a given flag.

Percentage rollouts

In the Targeting Rules & Rollouts section, set the percentage of the audience that should receive each variant. For a single-variant targeting rule, assign your desired traffic exposure to one variant. For example, roll out the Free Shipping variant of your promo banner to 50% of subjects matching your filter:

For a multi-variant rollout, assign percentages across multiple variants in the same targeting rule by selecting Serve > Split Traffic when editing or creating your targeting rule. The SDK distributes matching subjects across those variants according to the percentages you configure.

How the SDK evaluates percentage rollouts

When the SDK evaluates a targeting rule with a percentage rollout, it first checks whether the evaluation context matches the rule’s filter. If it matches, the SDK uses the flag key and the targetingKey from the evaluation context to assign the subject to a rollout bucket. That bucket determines whether the subject receives a variant from the current rule or passes through to the next rule.

Randomization is deterministic: a subject with the same targetingKey always lands in the same bucket for a given flag, so they receive the same variant on repeat evaluations. If you increase a rollout percentage later (for example, from 30% to 50%), subjects already in the treatment bucket stay there.

For multi-variant rules, the SDK applies the same bucketing logic to distribute subjects across variants according to the percentages defined on the rule.

Example evaluation context

await OpenFeature.setContext({
  targetingKey: 'user-123',
  user_id: 'user-123',
});

eval_ctx = EvaluationContext(
    targeting_key="user-123",
    attributes={"user_id": "user-123"},
)

evalCtx := openfeature.NewEvaluationContext(
    "user-123",
    map[string]interface{}{"user_id": "user-123"},
)

Further reading

Additional helpful documentation, links, and articles:

Targeting Rules and FiltersDOCUMENTATION