

Message Batches API
Overview :
The Message Batches API by Anthropic allows developers to process a high volume of queries asynchronously, with each batch containing up to 10,000 queries. This API is especially suited for non-time-sensitive tasks that do not require real-time responses, such as customer feedback analysis and language translation. It offers high throughput while costing only half of a standard API call, making large-scale data processing more economical.
Target Users :
The target audience primarily consists of businesses and developers who need to handle large volumes of data, such as data analysts, machine learning engineers, and corporate IT teams. The Message Batches API enables them to efficiently and cost-effectively process large datasets without the complexities of queue systems or rate limitations, freeing up more time to focus on more valuable tasks.
Use Cases
Quora utilizes the Message Batches API for summarization and highlight extraction, creating new user features.
Companies can leverage it to analyze their entire document repository, potentially involving millions of files.
Machine learning engineers can use it for classifying large datasets or for model evaluation.
Features
Batch process up to 10,000 queries per batch
Complete processing within 24 hours, usually faster
50% lower cost compared to standard API calls
Provides higher throughput without consuming standard API rate limits
Supports large-scale data processing, such as dataset analysis, categorizing large datasets, or extensive model evaluation
Offers infrastructure cost savings with a 50% discount on both input and output tokens
Supports Claude 3.5 Sonnet, Claude 3 Opus, and Claude 3 Haiku models
How to Use
Visit the Anthropic API documentation to learn how to use the Message Batches API.
Register and obtain your API key.
Prepare your query batches according to the documentation guidelines.
Send your query batches via HTTP POST request using the API key.
Wait for Anthropic to process your query batches.
Download the results after processing is complete.
Analyze and utilize the processed data as needed.
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