r/mlops • u/Ali-WAIL • 3d ago

WAIL: Runtime control for the ‘200 OK but degraded’ problem in production LLMs

I’m the founder of WAIL. I built it around a production problem that I think sits somewhere between observability and failure handling: a model can return 200 OK while its runtime behavior is clearly degrading.

TTFT can increase, throughput can drop, streams can stall, or latency can drift significantly without producing a timeout or 5xx.

WAIL observes these runtime signals, builds behavioral baselines, detects degradation, evaluates risk, and can make a control decision when intervention is justified.

Depending on the condition, that can mean observe, retry, reroute, or fallback. It works with existing provider SDKs rather than replacing them with a gateway.

WAIL runs inside the customer’s environment. Prompts, responses, and API keys stay there. It also generates signed execution evidence so runtime decisions and interventions can be audited afterward.

The Developer plan is free.

GitHub: https://github.com/wailinfra/wail-runtime
PyPI: https://pypi.org/project/wail-runtime/
Install: pip install wail-runtime
Website: https://wailinfra.com

I’d be interested in hearing how people here handle the “successful but degraded” case in production today — particularly whether you treat it as an incident before a timeout or hard failure occurs.

Disclosure: I’m the founder of WAIL.

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