AI rate limiting for voice: How to handle concurrency limits

The guide explores AI rate limiting for voice applications, emphasizing that concurrency, not requests per minute, is the primary constraint when using ElevenLabs models. It outlines how concurrency involves the number of requests being processed simultaneously, impacting the server's workload. The guide details client-side strategies to manage concurrency effectively, such as bounded concurrency pools, token and leaky buckets, and exponential backoff with full jitter. It explains that reaching the concurrency limit queues requests rather than rejecting them outright, with HTTP 429 errors indicating the need to reduce request rates. The document discusses using WebSockets to enhance capacity by counting only active audio generation periods toward limits. Additionally, it addresses multi-tenant fairness with strategies like per-tenant buckets and weighted fair queuing, while highlighting the importance of monitoring concurrency utilization through available headers. The guide advises optimizing client behavior and model selection before considering plan upgrades to manage growing demands, and it underscores the role of ElevenAPI in building scalable voice applications.