Sample data. Not a customer bill.

Policy editor

Planned

Right-size, cascade, cache, batch, automation, YAML and history.

Eval lab · eval_scored · version 3

Automation

Automatic optimisation only runs inside a policy the customer wrote.

ManualA person enables every change.SuggestParezen files recommendations. Nothing applies on its own.AutoParezen may apply changes this policy already allows, inside the guardrails.

What this policy will do

Eval backfill follows a cost-first policy.
A served response has to score at least 0.80 and finish inside 86,400,000 ms at p95.
Anything priced above $0.0100 a request is outside this policy.
Right-size is off, so Parezen keeps the model the app asked for.
Cascade is off, so there is no cheap-first attempt.
If the chosen model fails, fallbacks run in order: google/gemini-2.5-flash.
Output is capped at 400 tokens and must be structured, with reasoning effort none.
The route is marked async, so eligible work can take the provider's batch price.
Exact-match response cache is opt-in and on for this route.
Semantic cache is Planned and is not applied.
Automation may apply changes this policy already allows.

Simulated impact

12 sample requests on this route.

Baseline: $0.00
Actual: $0.00
Estimated spend: $0.00est.▲ 0.0%
Measured p95: 765 ms
Escalations: 0
Fallbacks: 2

This sample has no cascade escalations. When one happens, its cost is included, not hidden.
Exact-match cache is on. This sample has no exact-match hits yet, so the projection does not invent one.
Semantic cache is Planned and is excluded.