Error Handling Strategies

Benched.ai Editorial Team

Error handling strategies define how an AI platform surfaces, retries, and logs failures such as timeouts, malformed inputs, or model crashes. A predictable error taxonomy accelerates debugging for both providers and clients.

Error Categories

Code	Category	Example Cause
400	Client input	Invalid JSON payload, too many tokens
401	Authentication	Missing/expired API key
429	Rate limit	Exceeded quota window
500	Model runtime	Out-of-memory, CUDA launch failure
503	Capacity	No healthy replicas available

Retry Guidelines

Do not retry 4xx codes except 429; fix the request instead.
For 5xx or network timeouts, use exponential back-off starting at 200 ms.
Jitter delays to avoid thundering-herd effect.
Cap total retry window to service SLA (e.g., 10 s).

Design Trade-offs

Aggressive retries hide transient outages but increase background load.
Verbose error messages aid debugging yet may leak internal details.
Client-side validation reduces bad requests but duplicates server logic.

Current Trends (2025)

Structured JSON errors with machine-parseable type and param fields.
Automatic circuit breakers in SDKs that trip after three consecutive 5xx responses.
Correlation-ID headers propagated through model microservices for unified tracing¹.

Implementation Tips

Return error IDs; logs can map opaque IDs to stack traces.
Expose status endpoint (/healthz) for load balancers to detect faulty instances.
Simulate faults in staging (chaos testing) to verify retry logic.

OpenTelemetry Working Group, End-to-End Trace Correlation, 2024. ↩