How to Diagnose 429 Rate Limit Errors in OpenAI-Compatible APIs Before Switching Models

HTTP 429 rate limit errors in OpenAI-compatible APIs are often misattributed to provider instability, when the root cause may be local issues such as shared API keys, aggressive retries, or request amplification in agent workflows. A single user action can trigger dozens of backend model calls — including routing, retrieval, tool calls, and fallbacks — making amplification a common but overlooked source of pressure. Developers are advised to isolate workloads using separate project keys for production, staging, batch jobs, and experiments so that the offending workload can be identified quickly. Retry strategies like exponential backoff can mask deeper problems if retries fire after non-retryable errors or cause multiple workers to flood the API simultaneously. Structured logging that captures model IDs, routing paths, token counts, retry counts, and error timing is essential; without it, switching models or gateways amounts to guesswork and can silently escalate into a cost incident.