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A free scan shows the junk files, broken settings and background clutter dragging Windows down - then fixes them in one click.Free scan · Windows 10 & 11Choose client-side A/B testing when the variation belongs in browser or mobile-app code and can be applied using context already available on the device. Choose server-side testing when a backend needs to select the variation before returning an API response or behavior—such as pricing, recommendations, ranking, feature logic, or checkout. Either way, keep assignment stable and measure exposure at the point the participant actually encounters the tested experience.
What client-side and server-side testing mean
The distinction is where the experiment decision and variation are implemented. With client-side testing, an SDK or experiment logic runs on the user’s device and the browser or app applies the treatment. With server-side testing, backend code evaluates the assignment and returns content or behavior already shaped by that choice. As Optimizely puts it, “Server-side SDKs are incorporated into your backend services, letting you manage feature flags and experiments before content is delivered to the client.” Optimizely’s server-side SDK documentation describes this model.
Which architecture fits your experiment?
| Decision factor | Client-side tends to fit | Server-side tends to fit |
|---|---|---|
| Where the change lives | In browser or mobile-app code. | In a backend, API, or service. |
| When the choice is needed | The client has useful immediate context and can apply the variation locally. | The response should already reflect the assigned variation when it reaches the client. |
| Typical experiment scope | Presentation or client experience. | Business logic, feature behavior, recommendations, or service responses. |
| Control of decision logic | It is acceptable for evaluation logic and related details to reside on the device. | Decision-making and sensitive logic should remain in backend-controlled code. |
| System requirements | The app can evaluate locally and maintain the assignment key. | Backend services can evaluate a shared identity and return consistent treatment across clients. |
These are selection heuristics, not guarantees. Client-side evaluation may avoid an extra request, but the SDK, rendering path, and application determine the actual performance. Server-side evaluation can deliver a preselected experience, but it also adds implementation and operational responsibilities to backend services. Measure latency and rendering in your own system rather than relying on general vendor claims. Optimizely and ABsmartly describe common architecture patterns and use cases.
Choose client-side for local experience changes
A client-side approach is a natural fit when the treatment changes what the app displays or how it responds using information already available there. The decision can be applied locally, but the team must preserve a reliable assignment key and ensure that the relevant behavior and events are measured consistently.
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Choose server-side for backend behavior
Server-side testing is generally better suited to changes that affect service responses or decisions made by backend systems. ABsmartly identifies pricing, ranking and recommendation algorithms, API responses, feature toggles, and checkout as server-side use cases. The principle is broader than any one platform: when the tested behavior lives behind an API, evaluate the variant where that behavior is controlled.
Keep assignment separate from exposure
Assignment answers which variant a unit—such as a user, session, device, or account—has been allocated. Exposure answers whether and when that unit encountered the tested behavior. They are not necessarily the same event: a server may assign a variant before a page is rendered, or a client may receive configuration that is never used.
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Amplitude distinguishes assignment events from exposure events and describes assignment as a possible heuristic for some server-side cases when client-side exposure tracking is not possible. Use an assignment event as exposure only when it is a defensible proxy for actual encounter; otherwise instrument the point where the treatment is rendered or used. See Amplitude’s explanation of assignment and exposure.
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How to implement a valid experiment
- Define the control and treatments. Specify the actual behavior each group receives and the outcome you intend to measure. AWS AppConfig recommends clear treatment descriptions and starting a new run when treatment definitions change. See AWS AppConfig’s feature-flag experimentation guidance.
- Choose the randomization unit and stable identity key. Decide whether assignment is by user, session, device, or account, then test sign-in, device switching, and cleared local state. AWS AppConfig supports entity IDs such as user, session, and account; Firebase documents persistent assignment based on an experiment identifier and installation ID. See AWS AppConfig and Firebase A/B Testing configuration.
- Define exposure at the real encounter point. If a server assigns a treatment but rendering or a later client action determines whether the participant sees or uses it, instrument that sequence rather than assuming assignment equals exposure. Amplitude’s documentation covers assignment as a fallback heuristic where direct client exposure tracking is not possible: assignment and exposure events.
- Order activation and measurement correctly. Firebase says an activation event should occur after fetched experiment parameters are activated but before those parameters modify app behavior. Its documentation also distinguishes receiving parameters from inclusion in experiment results. See Firebase’s A/B Testing configuration guidance.
- Validate treatments before broad exposure. Use assignment overrides or another safe prelaunch method to check treatment behavior and metric logging. AWS AppConfig describes overrides for validation and recommends removing them when no longer needed: AWS AppConfig.
- Protect the run from mid-test changes. Changing targeting conditions or treatment behavior while a run is live can change assignment or undermine interpretation. Firebase warns that changing a shared condition during a running experiment can alter assignment and invalidate measurements: Firebase A/B Testing configuration.
Check rendering, stability, and measurement before scaling
- Confirm that each participant continues to receive the same treatment for the chosen randomization unit throughout the run. AWS AppConfig advises consistent treatment; Firebase documents persistent assignment tied to an experiment identifier and installation ID.
- Test the control and every treatment on the actual rendering path. A variation that is assigned but delayed, never rendered, or applied after the relevant behavior is not a valid exposure.
- Verify event order and logging end to end, including what happens when configuration is fetched but not activated or used.
- Remove temporary assignment overrides once validation is complete so they do not distort the live allocation.
- Treat claims such as “no flicker,” “minimal latency,” or “secure” as design goals or vendor-stated benefits, not properties guaranteed by choosing one architecture. Rendering, caching, identity, SDK, and network conditions in your application determine the result.
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