A faster model server is only an improvement if it preserves the quality and reliability the application needs. Synthetic benchmarks help, but a production workload may contain longer prompts, burstier demand and different failure cases. Testing beside the existing service can expose those differences before a replacement reaches users.
Modal’s October 12 Endpoint Candidates release stages alternative deployments for Dedicated Endpoints. Candidates can receive sampled copies of production requests while the original endpoint continues to return the user-facing response. Each candidate also has a separate URL for quality evaluation. Promotion starts a rollout at the existing endpoint URL, and mirrored computation is billed under normal compute pricing. Primary source.
The mirror can drop work
Modal describes bounded request buffers that protect production from slow candidate readers. Under pressure, mirrored requests may be cancelled while the original continues. The service calls this best-effort mirroring. That is an important operational safeguard and an important measurement limitation.
If the hardest requests are more likely to disappear from a candidate’s sample, its observed performance can look better than it would on the complete workload. An evaluation should track attempted mirrors, completed mirrors and cancellations together. Latency among successful requests alone is not a sufficient comparison.
Promotion needs a quality gate
The separate candidate URL provides a place to run a fixed test suite, including cases poorly represented in live traffic. Teams can compare answer quality, structured-output validity and tool-call behavior alongside throughput. A model change and a serving-recipe change should ideally be tested separately so that regressions have an identifiable cause.
Costs also need a complete accounting. Mirroring consumes additional compute during evaluation; a candidate that saves money per request may require a long enough operating period to justify the experiment. For a high-volume service that may be straightforward, while a small application may need a more selective sample.
This workflow could make inference upgrades more routine by reducing the work required to stage and compare alternatives. The next evidence is a candidate that meets predetermined quality, latency and reliability thresholds on representative traffic, including missing requests. An easy promotion control is useful only when the decision behind it is well supported.
References
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