Unlocking Efficiency with OpenAI's Batch API: Streamline Large-Scale Processing - But Cheaper

Super interesting new API endpoint released by OpenAI: Batch API, which allows users to process large volumes of requests asynchronously. This is useful for tasks that do not require immediate responses or when rate limits prevent the rapid execution of numerous queries.

Some good use cases for batch processing could be classifying large document sets, due diligence analysis by taking financial records, filings, articles etc..

The Batch API provides a simple set of endpoints that enable users to collect multiple requests into a single file, initiate a batch processing job to execute these requests, monitor the status of the batch while the underlying requests are being processed, and ultimately retrieve the aggregated results upon batch completion.

Compared to using the standard endpoints, the Batch API offers several advantages:

1. Improved cost efficiency: Users can benefit from a 50% cost reduction compared to synchronous APIs. This is huge, sure GPT token costs are low, but when you want to process 10,000 legal documents - saving 50% of the cost is massive.
2. Higher rate limits: The Batch API provides substantially more headroom compared to the synchronous APIs, up to 250 million tokens.
3. Quick completion times: Each batch is completed within 24 hours, and can often be quicker.​​​​​​​​​​​​​​​

I'm looking forward to adding this functionality to the infrastructure layer we're working on - allowing teams to say "You know what, yeah this can wait a day" and make huge savings whilst being slightly more patient.