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What is the difference between feature flags and A/B testing?
A feature flag is a runtime delivery control: it lets a team enable or disable a code path for selected users without making a new code deployment just to change the setting. Flags can support internal previews, audience targeting, gradual exposure, and a rapid off switch if a change causes problems. Statsig calls these controls “feature gates”; its documentation describes targeting, gradual deployment, and real-time toggling in its own product. Statsig feature flag documentation
An A/B test is a controlled comparison. It assigns eligible users to two or more alternatives and measures outcomes to assess which performs better. That outcome might be a user action, such as completing a purchase, or a technical measure such as latency, errors, cost, or throughput. The test needs a defined question and suitable measurement; simply exposing a feature to more people does not establish that it caused a measured change.
The distinction is purpose, not necessarily tooling. A flag answers, “Who gets this, and when?” An experiment answers, “What changed in the measured outcome, and how much evidence supports that conclusion?” Some platforms combine them: a flag can control eligibility while an experiment assigns variants and records outcomes. Statsig’s decision guide LaunchDarkly experimentation documentation
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When should you use a feature flag or rollout?
Choose a flag when the immediate need is control over delivery rather than a comparison among competing alternatives. For example, use one to show a feature to an internal team first, release it to a beta audience, target a region, or increase exposure in stages while monitoring system behavior.
- Internal preview: limit access to staff or a designated allowlist while checking that the feature works in a realistic environment.
- Gradual release: expand access in stages to manage operational risk and observe for problems.
- Fast disablement: turn off a problematic code path through the flag rather than waiting for a new deployment, provided the application and flag service are set up to support that control.
- One known change, technical monitoring: use a rollout with metrics if the platform supports it. Measuring errors or latency during a staged release can be useful without claiming that a single-variant rollout is an A/B test.
A rollout typically increases exposure to one selected version. Optimizely’s Feature Experimentation documentation distinguishes its one-variation rollout rule from an A/B test rule with two or more variations. Those are product-specific rule definitions, but they illustrate the important general point: increasing exposure to one chosen version is not the same as comparing alternatives. Optimizely rollout documentation
When should you run an A/B test?
Run an experiment when you have competing alternatives and a measurable hypothesis about their effect. Before assigning users, decide what question the test should answer, which population it applies to, what counts as exposure, and which outcome is primary. Choose guardrail metrics as well when a change could affect reliability or other important outcomes.
- Use a baseline and alternatives: specify the existing experience and the variants being compared. A/B/n tests can include more than one alternative, depending on the platform.
- Define the outcome in advance: select a primary metric that reflects the question, rather than choosing a winner after looking across many measures.
- Plan assignment and logging: use a stable assignment unit, such as a user identifier, where appropriate, and record exposure and outcome events so the comparison can be analyzed.
- Set a decision approach: follow the platform’s statistical method and a planned stopping or decision rule. There is no universal sample size or duration established by the cited vendor guides.
Experiments can inform product choices as well as engineering ones. For example, a team might compare two onboarding flows using a completion metric, while also watching error rates if the implementation changes system behavior. Optimizely’s conceptual guidance describes tests as most useful when there is a specific measurable metric and a hypothesis about the effect of a change. Optimizely’s feature flags and A/B testing article
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| Situation | Prefer | Reason |
|---|---|---|
| Internal preview, beta audience, regional launch, staged release, or an off switch | Feature flag or rollout | Controls exposure and release risk; experiment analytics may not be needed. |
| Competing implementations and a measurable hypothesis | A/B test | Compares alternatives against chosen metrics. |
| Learn which version works, then ship it safely | Both, in sequence | Run the comparison first; then use rollout controls to expand exposure to the selected version. |
| Ship one known change gradually and observe technical impact | Rollout with metrics, if supported | Monitors the change without treating a single-variant release as a controlled comparison. |
Do not treat every vendor’s feature names, allocation options, statistical methods, or billing rules as universal definitions. For instance, Statsig describes feature gates as boolean controls and experiments as returning variant configuration, while Optimizely documents distinct rollout and experiment rule types. LaunchDarkly documents its own experiment analysis options. Check the current product documentation for the platform and edition you plan to use. Statsig decision guide Optimizely rollout documentation LaunchDarkly experimentation documentation
How to combine a flag and an experiment
- State the problem and primary outcome. Decide what user or business problem matters and what result would answer the question before building variants.
- Separate deployment from exposure. Put the code behind a flag and define the eligible audience, using an internal allowlist when an internal preview is appropriate.
- Assign eligible users to variants. Randomize a stable unit, such as a user identifier, into the baseline and one or more alternatives. Keep assignments consistent for the relevant test period.
- Validate assignment and instrumentation. Check that allocation behaves as intended and exposure and outcome events are recorded. An A/A test, which assigns equivalent experiences, can help reveal allocation or metric stability problems before testing a real difference. LaunchDarkly experimentation documentation
- Monitor outcomes and guardrails. Track the primary outcome and relevant technical measures such as errors or latency when the change could affect system behavior.
- Analyze using the planned method. Apply the platform’s statistical approach and the stopping or decision plan chosen for the test; do not assume a generic sample size or duration applies.
- Roll out or reduce exposure. If the result supports a launch, increase exposure progressively and monitor it. If the change causes a problem, reduce exposure or disable the flag.
- Assign ownership and remove temporary flags. Record who owns each temporary flag and the condition for removing it, then clean it up when it is no longer needed.
Google Cloud’s App Lifecycle Manager documentation describes allocation-based tests and stable bucketing, but labels the feature Preview / Pre-GA and warns of limited support. Its availability and launch stage are specific to that product, not a general requirement for experimentation. Google Cloud allocation and experimentation documentation
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What should you check when selecting a platform?
Compare tools against your implementation and governance needs rather than treating a vendor’s feature list as a definition of flags or experimentation. Relevant questions include:
- Technical fit: Does the platform support your application stack, SDKs, deployment pattern, and required targeting?
- Experiment measurement: Can it use the metrics, event data, and integrations your team needs to answer its questions?
- Operational controls: Does it provide the rollout, rollback, access, ownership, and audit controls your release process requires?
- Governance and data access: Understand where assignment and analytics data go, who can change targeting, and how the tool fits existing systems.
- Cost and constraints: Verify current plan limits, SDK requirements, analytics behavior, and allocation limits for the relevant product version. These vary by vendor and can change.
- Portability: Consider how tightly flag definitions, experiment configuration, and analysis depend on a vendor’s SDKs or data model.
Vendor documentation describes each vendor’s service; confirm current availability and terms before making a purchasing or architecture decision. Optimizely’s documentation, for example, notes that plan and SDK details apply to particular product versions. Optimizely rollout documentation
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Why flag cleanup and experiment quality matter
Flags are useful operational controls, but temporary ones can accumulate as their purpose and owner become unclear. For each temporary flag, record an owner and a removal condition so the code and configuration can be retired when the rollout or test is finished. Statsig’s feature-flag documentation covers flag operations including exposure monitoring; implementation details differ across platforms. Statsig feature flag documentation
For experiments, stable assignment and reliable exposure and outcome events are prerequisites for interpreting results. If users move between variants unexpectedly, or the logged events do not represent actual exposure and outcomes, the comparison may not answer the intended question. Use the platform’s documented analysis method, and treat any vendor-specific statistical option as a tool choice rather than a universal rule.
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