OpenAI batch API

What is OpenAI batch API?

‍OpenAI batch API is an asynchronous endpoint for sending groups of requests to OpenAI at a lower cost, with results returned within a 24-hour window. It is designed for workloads that do not need an immediate response, such as large-scale classification, embeddings, and evaluation jobs. (platform.openai.com)

Understanding OpenAI batch API

‍In practice, batch API lets you bundle many requests into a JSONL input file, upload it once, and then track the batch until the outputs are ready. OpenAI documents it as a fit for asynchronous processing, with support for endpoints like responses, chat completions, embeddings, completions, and moderations. (platform.openai.com)

‍Teams usually reach for batch API when throughput and cost matter more than latency. The 50% discount and 24-hour completion window make it useful for offline pipelines, backfills, and repeated large-scale runs where you want predictable execution rather than real-time interaction. (platform.openai.com)

‍Key aspects of OpenAI batch API include:

1. Asynchronous execution: Requests are processed in the background instead of blocking for a live response.
2. Batch file input: Jobs start from a pre-uploaded JSONL file with one request per line.
3. Lower cost: OpenAI positions the batch endpoint at a 50% discount versus synchronous APIs.
4. 24-hour turnaround: Batches are designed to complete within a day.
5. Broad offline use: It works well for evaluations, dataset labeling, embeddings, and moderation workflows.

Advantages of OpenAI batch API

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1. Cost efficiency: You can process high-volume jobs at half the synchronous API cost.
2. Operational simplicity: One uploaded file can replace many individual calls.
3. Better throughput planning: Batch execution is easier to schedule and monitor than ad hoc jobs.
4. Good fit for offline pipelines: It matches workloads that do not depend on live user interaction.
5. Useful for repeatable runs: The same input file can support backfills, audits, and evaluation cycles.

Challenges in OpenAI batch API

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1. Not real-time: It is a poor fit when a user expects an immediate answer.
2. File-based workflow: Teams need to prepare and validate batch inputs before submission.
3. Longer feedback loop: Debugging is slower than with synchronous requests.
4. Use-case fit matters: It works best for jobs that can wait, not interactive product flows.
5. Pipeline coordination: Downstream systems must handle delayed outputs and job status checks.

Example of OpenAI batch API in action

‍Scenario: A product team wants to label 200,000 support tickets by category before training a routing model. Instead of calling the API one ticket at a time, they package the requests into a batch file and submit it through OpenAI batch API.

‍The batch runs asynchronously, the team checks status while it processes, and the output file later contains the completed labels. That workflow is easier to schedule, cheaper to run, and more suitable for offline analysis than a live request loop.

How PromptLayer helps with OpenAI batch API

‍PromptLayer helps teams manage the prompts, versions, and evaluation logic that often sit around batch workflows. If you are using OpenAI batch API for large offline jobs, PromptLayer can help you track prompt changes, compare outputs, and keep experimentation organized across runs.

Ready to try it yourself? Sign up for PromptLayer and start managing your prompts in minutes.