For a safe tenant-level rollout, evaluate flags with a stable tenant identity taken from authenticated server-side context, decide tenant eligibility separately from user or tenant allocation, and ramp exposure using a method with a defined monitoring and rollback plan. The details of targeting and rollout behavior depend on the flag provider; the examples below identify LaunchDarkly-specific behavior where relevant.
Start with a trusted, stable tenant identity
A tenant-targeted flag is only as reliable as the context used to evaluate it. Choose an identifier that remains stable for the tenant and is appropriate for use in your flag system. At the authenticated request boundary, obtain that identity from trusted application state—such as the tenant associated with the authenticated principal—not from an unchecked request parameter.
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Set the tenant context once for the request and make it available wherever flag evaluations happen in that request’s asynchronous execution path. OpenFeature’s JavaScript server SDK documents transaction-context propagation and an Express middleware example; the exact integration depends on your framework and provider. See the OpenFeature Node.js SDK documentation.
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Separate tenant eligibility from rollout allocation
Eligibility answers which tenants may receive the feature
A tenant rule answers whether a tenant is included, excluded, or otherwise eligible. Keep that decision distinct from the question of how broadly to expose a feature within the eligible population. This distinction matters when a change should be available only to selected organizations but introduced gradually to users inside them.
Allocation answers who receives the enabled variation
Choose the allocation unit to match the risk and desired consistency. A tenant allocation keeps a tenant together; a user allocation can expose a feature to a subset of users within an eligible tenant. Do not silently substitute a user key when the policy is tenant-level, or use a tenant key when you intend to compare or ramp individual users.
LaunchDarkly documents targeting by one context kind and percentage rollout by another using a multi-context, but describes this as a specialized configuration. Verify the actual context kinds, keys, and attributes sent by your application against the rule before relying on it. In LaunchDarkly attribute-based percentage rollouts, contexts with matching attribute-value pairs receive the same variation. The documented allocation behavior does not support non-string or non-integer numeric attribute values; those can result in arbitrary assignment. Details are in Percentage rollouts by context attribute.
Exercise the context combinations before enabling production traffic
Test representative cases using the same context-building path the service will use in production:
- A tenant explicitly included by the eligibility rule.
- A tenant explicitly excluded by the rule.
- Two or more users in one tenant when allocation is intended to be user-level.
- A request with no tenant context, to confirm the application handles it safely rather than evaluating against an unintended identity.
- A context with an unexpected attribute type, especially if a percentage rule depends on that attribute.
Define an explicit fallback for missing or invalid tenant context. For a tenant-restricted feature, fail closed at the application boundary rather than treating missing identity as permission to enable it. Confirm the provider’s evaluation and error behavior in your implementation; it is not identical across providers.
Choose a rollout mechanism that matches the release question
A fixed percentage, a scheduled ramp, a metric-guarded ramp, and an experiment answer different operational questions. The table describes documented LaunchDarkly behavior, not a guarantee about other vendors.
| Mechanism | What it does | Stability and monitoring | When it fits |
|---|---|---|---|
| Fixed percentage | Assigns a chosen proportion; it does not automatically increase exposure over time. | Changing the percentage can change which customers receive the variation. Restarting with unchanged configuration and context kind preserves the same contexts. | Use when you want a controlled share without an automatic schedule. |
| Progressive rollout | Increases exposure according to a schedule. | It does not include metric monitoring. A context’s variation changes only once as the rollout progresses. Stopping requires choosing what the rule should serve; a later new rollout can select a different cohort. | Use when you want exposure to rise over time and will monitor the service separately. |
| Guarded rollout | Ramps exposure while monitoring selected metrics. | Can notify or automatically revert after detecting a statistically significant negative impact, subject to eligible plans or add-ons and a minimum number of evaluated contexts per step. A new rollout can allocate a different cohort. | Use only if the feature is available in your account and your evaluation volume meets its conditions. |
| Experiment | Compares two or more variations against selected metrics. | Designed for comparative measurement, rather than simply deciding whether a release should advance. | Use when the question is which variation performs better, not just whether the new release is safe to expand. |
LaunchDarkly’s release options are described in Releasing features with LaunchDarkly, with additional details for progressive rollouts and guarded rollouts. Confirm current plan availability and configuration requirements in your account before choosing a mechanism.
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Start with a bounded exposure
Begin with a deliberately limited eligible population or allocation, then expand only after reviewing the service behavior. A staged rollout gives the team time to detect tenant-specific failures that a broad average can hide. The right stages depend on traffic, risk, and operational capacity; there is no universal safe percentage.
Choose metrics tied to the feature’s failure modes
Before exposure, identify the signals that would indicate harm. Depending on the change, these may include error rates, latency, failed jobs, data-integrity checks, or a product outcome. Compare the new path with a baseline and, where possible, segment by tenant or relevant cohort so a problem concentrated in one organization is not obscured by healthy traffic elsewhere.
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Set alert thresholds, the person or team responsible for acting, and the known-good variation before increasing exposure. If using LaunchDarkly guarded rollouts, select the metrics that should gate the ramp and check the documented plan and minimum-context conditions. Its documentation covers notifications and optional automatic rollback; do not assume those controls exist or behave the same way in another provider. See Creating guarded rollouts.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Define the pause and rollback procedure before launch
A flag is not a rollback plan by itself. Write down who can stop exposure, what the flag should serve after stopping, and how the team will verify the service has recovered. Make sure the on-call engineer can reach the flag control and understand whether a stop action freezes a cohort or selects a specific variation.
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- Pause: stop the ramp or change the rule to stop exposing the new path, using the provider’s documented controls.
- Serve the known-good behavior: select the intended safe variation rather than assuming that stopping automatically restores the old behavior.
- Check recovery: verify the affected service metrics and tenant-specific symptoms have returned to acceptable levels.
- Preserve evidence: record the affected contexts, rollout configuration, time of change, and relevant errors for diagnosis.
- Restart deliberately: reassess the cause and cohort behavior before starting a new rollout. In LaunchDarkly, a new progressive or guarded rollout can allocate a different cohort; a fixed percentage rollout retains the same contexts on restart only when its configuration and context kind remain unchanged.
For a progressive rollout specifically, LaunchDarkly requires choosing what the rule should serve when stopping. Review its progressive rollout management guidance before relying on stop and restart behavior.
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Keep context propagation and flag ownership operational
Keep context construction close to request authentication and authorization, not scattered among feature call sites. This reduces the chance that one handler evaluates by tenant while another unintentionally evaluates by user or omits tenant identity. Confirm that context is propagated through background work only when the job has an explicitly defined tenant identity and authorization model.
Assign an owner to each production flag and define its removal condition—for example, after the new behavior is fully adopted and the old path is no longer needed. Treat flags that affect tenant access or data handling as operational controls: document their purpose, expected safe variation, and the person responsible for cleanup. This is engineering practice, not a universal provider requirement.
Provider behavior, SDK integration, and account availability can change. The cited OpenFeature and LaunchDarkly documentation describes the behaviors referenced here; validate context construction, error handling, and rollout controls in the version and account you actually operate.
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