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Evaluation And Case Studies

Start with Enterprise FAQ when the evaluation is driven by practical buyer concerns such as provider keys, private models, team autonomy, savings evidence, auditability, rollout risk, or client compatibility.

Evaluate GenAI Smart Router by proving that each model group completes the intended workload while meeting cost, latency, governance, and operational evidence requirements. Do not treat one provider, one benchmark, or one historical model-group name as universally best.

Evaluation Flow​

  1. Define the workload and success criteria.
  2. Discover the caller-visible model groups available to the test token.
  3. Run the same client shape planned for production: chat, Responses, Anthropic Messages, tools, images, structured outputs, or reasoning controls.
  4. Verify task outcome with a workload-appropriate checker such as unit tests, extraction accuracy checks, OCR targets, tool-call correctness, browser tasks, golden datasets, Harbor, or product acceptance tests.
  5. Compare cost, latency, throughput, attempts, fallbacks, and provider/model mix.
  6. Promote only groups and targets that meet the workload contract; roll back or isolate targets that fail.

Evidence Package​

For a commercial or production evaluation, collect:

  • allowed model groups from /v1/models;
  • client request examples and response compatibility;
  • selected upstream provider/model evidence;
  • outcome pass/fail evidence;
  • usage, cost, savings, latency, throughput, fallback, and error reports;
  • security, license, deployment, and operational readiness checks.