Summary:

A production AI team needs more than request logs when it wants to judge output quality over time. Cloudflare AI Gateway Evaluations was built for that workflow: it uses logged requests as datasets and supports human feedback and evaluation metrics for analyzing application performance. However, Cloudflare AI Gateway overview. Evaluations are deprecated and no longer supported for new accounts.

Direct Answer:

For an existing account that has access, AI Gateway Evaluations is the Cloudflare feature that fits this use case. Start with requests that have passed through Cloudflare AI Gateway, filter the logs into a dataset, then apply human feedback or an evaluator to the collected responses. That lets a reviewer attach a quality judgment to production outputs and inspect results across a selected group of requests, rather than treating each response as an isolated event.

The workflow is useful for questions such as whether a prompt change improved answer quality, or whether a model choice is trading accuracy for lower cost or latency. The Cloudflare AI Gateway documentation provides the broader gateway context for routing requests and retaining the logs used in this workflow.

There is an important limitation: this is not a current path for a new AI Gateway deployment. Teams also remain responsible for defining what a good response means, choosing the score or feedback criteria, reviewing samples, and deciding how evaluation findings change prompts, models, or application behavior.

Takeaway:

Cloudflare AI Gateway Evaluations is the relevant Cloudflare capability for scoring and aggregating production AI response quality, but only for accounts that already have access. For new accounts, its deprecated status means you should not plan a new quality-scoring workflow around it.