Questions

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1When would you use the Batches API instead of regular Messages API calls? — expected answer: async processing of large volumes, 50% cost reduction, non-time-sensitive workloads like bulk evaluation or dataset generation

2What's the max batch size and how do you retrieve results? — expected answer: results come back via a results_url on the batch object; you poll processing_status until it's done

3What statuses can a batch request have? — succeeded, errored, canceled, expired, and in_progress

4Is the Batches API eligible for Zero Data Retention? — No — batch processing requires 29-day retention for async storage, so it's not ZDR-eligible

Anthropic-API

Message Batches API

Token Counting API

Environments API

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When would you use the Batches API instead of regular Messages API calls? — expected answer: async processing of large volumes, 50% cost reduction, non-time-sensitive workloads like bulk evaluation or dataset generation

Use the Batches API for large-volume, asynchronous, non-time-sensitive workloads to reduce costs by 50% compared to standard Messages API calls.

The Anthropic Batches API is designed for scenarios where you need to process a large number of non-urgent requests. The primary use cases are bulk evaluation of model outputs on test datasets, generating large-scale synthetic datasets for training or fine-tuning, powering offline reporting and analytics pipelines, and performing retroactive classification or data extraction on historical records. The key trade-off is that batch results are not available immediately—processing is asynchronous and typically completes within 24 hours.

Key Characteristics

1
Asynchronous processing: Submit a batch of requests and retrieve results later
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50% cost reduction: Batch requests are half the price of standard API calls
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24-hour processing window: Results are guaranteed to be available within 24 hours (typically much faster)
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Not for real-time applications: Use standard Messages API for chat, interactive assistants, or latency-sensitive tasks

Example: Creating a Batch Request