An AI API can return HTTP 200 and still fail the job you actually care about.
The request may have reached a fallback model you did not intend to use. A retry may have doubled the cost. The response may be technically valid but empty, truncated, too slow, or unusable by the next step in your workflow.
For production AI systems, “success” needs a stronger definition. Record the exact requested model ID and the model or route that actually served the request. A transparent fallback is useful. A silent fallback makes quality, latency, and cost harder to explain.
One HTTP 200 can hide several failed attempts. Count retries, route changes, and fallback calls. A response that succeeds after three paid attempts has a different cost and reliability profile from a response that succeeds once.
Track input tokens, output tokens, and the charged amount. Large outputs, repeated context, agent loops, and retries can matter more than the model’s listed input price.
Transport success is not task success. Validate the output your application needs: non-empty text, valid JSON, required fields, acceptable refusal behavior, or a result that passes the next workflow step.
A request that completes after the user has left is operationally different from a request that meets the product’s latency budget. Record end-to-end latency together with route and retry data.
A useful request log should let you answer:
The practical metric is not the cheapest listed model. It is closer to the cheapest successful route: a route that completes the intended task with visible cost, acceptable latency, and an explainable failure path.
TackleKey exposes OpenAI-compatible model access together with public model IDs, pricing references, project keys, logs, balances, and route operations. Prices and availability are live signals, not permanent guarantees.
See the current route and cost view: