Batch vs. Synchronous Processing
Explore the differences between batch and synchronous processing in Claude AI systems. Learn to identify appropriate scenarios for each method, implement batch retries, and evaluate cost versus latency trade-offs to optimize AI-driven workflows in production.
When Claude processes a single document and returns the result before the next request is sent, that is synchronous processing. When hundreds or thousands of documents are submitted together and results are retrieved after the batch completes, that is batch processing. The correct choice is not a matter of preference; it depends on whether the calling system needs the result immediately, how many documents arrive at once, and what the cost and throughput constraints are. This lesson covers both modes, when each fits, and how the retry pattern from the previous lesson must adapt when individual retries are no longer immediate. By the end of this lesson, we will be able to:
Identify scenarios where synchronous processing is required and where batch processing is preferred
Submit a batch of extraction requests using the Message Batches API and retrieve results by
custom_idExplain why the immediate retry loop from the previous lesson does not apply to batch processing
Design a batch retry strategy that resubmits only failed documents
Choosing the right mode
The decision between synchronous and batch processing turns on three factors: whether a human or downstream system is waiting for the result, how many documents arrive at once, and whether cost optimization matters more than speed.
Factor | Use Synchronous | Use Batch |
Latency requirement | Real-time: a user or API is waiting | Offline: results are needed within hours, not seconds |
Volume | One document or a small burst | Hundreds to thousands of documents |
Throughput | Low; one request at a time | High; many requests processed in parallel |
Cost | Standard API pricing | 50% discount on input and output tokens |
Retry granularity | Immediate per-document retry in the same loop | Re-submit only failed documents in a follow-up batch |
The 50% cost reduction for batch processing is significant for high-volume workflows. A pipeline that processes 10,000 invoices per day can cut its Claude API costs in half by switching from synchronous to batch processing, at the cost of adding latency from minutes to hours.
Synchronous processing: The baseline
Synchronous processing runs one request at a ...