Debug your AI API route before blaming the model for failures

When AI API calls fail with errors like 401, 429, or model_not_found, developers often switch models or providers, but the root cause is frequently a misconfigured route rather than a model-quality issue. Key factors to verify include the API key's scope and permissions, the correct base URL, and the exact model ID being used, as small mismatches can cause hard-to-trace failures. Different HTTP error codes each point to distinct problems — authentication, authorization, availability, or rate limits — and treating them all as provider instability wastes debugging time. Retry logic, SDK fallbacks, and multi-step pipelines like RAG or agents can silently multiply requests and serve a different model than the one originally requested. Experts recommend running a single small test request and fully tracing which key, model, route, token count, and charge were involved before scaling up traffic.