Batch Processing and Async Calls: Efficiently Handling Large Volumes of AI Requests
When processing large volumes of text (batch summarization, translation, classification), how to design an efficient, cost-controlled batch processing system.
Batch Processing Scenarios
Scenarios requiring processing large amounts of text at once: batch contract review, batch news summarization, batch product description generation, batch sentiment analysis, batch translation.
Queue Design
Use a message queue (e.g., Redis, RabbitMQ) to accept batch tasks, with background workers processing asynchronously. Avoid timeouts and resource waste from synchronous calls.
Concurrency Control
Set a maximum concurrency limit (5-20 recommended) to avoid triggering API rate limits. Adding 50-200ms intervals between tasks effectively reduces throttling risk.
Cost Control
For batch tasks, use low-cost models (like Qwen3.5 Flash) for initial filtering to save costs; only do secondary processing on high-value tasks.
Fault Tolerance
Each task retries independently (up to 3 times). Failed batches go to a dead-letter queue for manual handling or later retry.