OpenAI Batch API: How to Cut Costs by 50% on Bulk Requests
A practical guide to OpenAI's Batch API — how it works, which models support it, real savings calculations, and how to combine it with prompt caching for maximum cost reduction.
OpenAI's Batch API lets you submit large sets of requests asynchronously and receive a 50% discount on standard API pricing. If your workload doesn't require real-time responses — data processing, content generation, classification, embeddings — the Batch API is one of the simplest ways to cut your OpenAI bill in half.
Batch API Pricing: Regular vs Batch
Real-World Savings Example
Say you're processing 100,000 documents per day — summarizing each with GPT-4o using ~2,000 input tokens and ~500 output tokens:
How the Batch API Works
Batch API vs Prompt Caching: Which Saves More?
Both techniques cut costs, but they address different scenarios:
Combining Both for Maximum Savings
The biggest cost reductions come from combining techniques. A batch job with a large, cached system prompt can achieve 60–80% total cost reduction. Example: batch summarization with a 5,000-token system prompt:
When NOT to Use Batch API
- Real-time features: Chatbots, live search, user-facing generation — anything that needs immediate response.
- Streaming required: Batch API doesn't support streaming responses.
- Time-sensitive pipelines: If downstream tasks depend on quick turnaround (under 1 hour), batch isn't reliable enough.
Calculate your batch savings: Open cost calculator → | Batch processing cost guide →