Should we build or buy our experimentation platform?

Buy the decision service and SDK surface (LaunchDarkly/Statsig/Optimizely) or use GrowthBook if you need self-hosted. Build the paved road around it: OpenFeature integration, exposure schema, GitOps, and SLO guardrails. Your platform team shouldn’t maintain a stats engine or multi-SDK matrix long-term.

Server-side or client-side decisions?

Default to server-side for critical paths (checkout, pricing) so you have a single kill switch and audit trail. Client-side is fine for non-critical UI tweaks if you still route through a provider and emit the same exposure event schema.

How do we prevent p-hacking and peeking?

Use a consistent stats approach (e.g., GrowthBook’s sequential testing or Statsig’s CUPED) and separate guardrails (SLOs, error rate thresholds) from business lift. Guardrails fire automatically; business metrics get reviewed on cadence.

What about privacy and PII in exposure logs?

Don’t log raw PII. Hash user IDs with a stable salt, pass context keys from an allowlist, and rely on OTEL resource attributes for service metadata. Apply DLP at the sink (e.g., BigQuery or Redshift) and document the schema.

Can we do this with Kubernetes service meshes and canaries?

Yes. Use Istio/Linkerd for progressive delivery, but keep experiment decisions in the app via OpenFeature. Mesh canaries handle transport-level rollout; experiments handle user/bucket-level decisions. Both report into the same SLO guardrails.

Platform-productivity · Dec 8, 2025 · 8 minute read

Stop Rolling Your Own Experimentation: The Paved Road to Safe Feature Testing

You don’t need a bespoke stats engine and a graveyard of feature flags to test safely. Standardize the contracts, automate the guardrails, and keep the paved road boring on purpose.

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Stop Rolling Your Own Experimentation: The Paved Road to Safe Feature Testing

Key takeaways

Implementation checklist