Prompt
I'm building a server-side experimentation setup for a web app
Latest observation
Oct 13, 2026
OpenAI APIWeb search: off
Great — a server-side experimentation setup is a solid choice for performance, control, and cleaner client code.
A typical setup looks like this:
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Experiment assignment happens on the server
- When a request comes in, the server determines which variant the user gets.
- Assignment should be deterministic when possible so the same user gets the same variant across requests.
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Persist the assignment
- Store the assigned variant in a cookie, session, user profile, or signed token.
- This prevents users from bouncing between variants.
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Expose the variant to the frontend
- Render it into the HTML, send it through an API response, or include it in page metadata.
- The client can then adjust UI or behavior if needed.
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Track exposure and conversion
- Log when a user is actually exposed to a variant.
- Separately log conversion events so you can calculate impact accurately.
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Use a feature flag or experimentation service
- You can build this yourself or use a platform like LaunchDarkly, Optimizely, GrowthBook, Statsig, or Unleash.
- These help with targeting, rollout rules, bucketing, and guardrails.
-
Be careful with caching
- If your app uses CDN or server caching, make sure variant-specific content doesn’t get mixed across users.
- Often this means varying by cookie or disabling cache for experiment-specific pages.
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Plan for analysis
- Decide upfront on metrics, sample size, and how you’ll handle segmenting, ramp-up, and stopping rules.
If you want, I can help you design:
- a simple custom architecture
- a database schema for assignments and events
- a middleware-based implementation
- or an end-to-end example in your stack (Node, Python, Ruby, Go, etc.)