Programming Tutorials

  Moving generative AI applications from development to production exposes them to the realities of scale. A single prototype handles API calls gracefully, but simultaneous user requests during traffic spikes inevitably trigger an Azure OpenAI 429 error. When these limits are breached, the application stops generating responses, degrading the user experience and potentially failing critical backend pipelines. Addressing a RateLimitExceeded Azure OpenAI error requires more than generic error handling. It demands a dual approach: intelligent code-level retries that respect Azure-specific telemetry, and infrastructure-level scaling to distribute the load across multiple geographic regions. Understanding the Root Cause: TPM and RPM Limits Azure OpenAI Service enforces strict throttling mechanisms based on two primary metrics: Requests-Per-Minute (RPM) and Tokens-Per-Minute (TPM). These are not soft limits. They are enforced via a token bucket algorithm at the subscription and region le...