Comparison
Batch API vs Inference
Batch API and Inference are both common AI/LLM terms but cover different ideas. Here is a quick side-by-side.
When you would reach for Batch API
Any time the work is bulk, async, and not user-facing — embedding pipelines, evals, synthetic data, batch labeling.
Generating embeddings for 10M support tickets via OpenAI Batch: $0.05 / 1M tokens instead of $0.10, completed overnight.
When you would reach for Inference
Inference comes up when the question is fundamentally about inference.
A ChatGPT response: one inference call per turn.
Frequently asked
What is the difference between Batch API and Inference?
Batch API: Batch APIs (OpenAI, Anthropic) accept up to 50K LLM requests in a single submission, run them asynchronously over hours, and return results at ~50% of the synchronous price. The cheap option for bulk processing. Inference: Inference is what happens when you actually run a trained model on new input. For LLMs that means generating tokens one at a time, with sampling and a KV cache.
When should I use Batch API vs Inference?
Any time the work is bulk, async, and not user-facing — embedding pipelines, evals, synthetic data, batch labeling. Inference applies when you are focused on inference.
Are Batch API and Inference the same thing?
No. Batch API is inference; Inference is inference. They are related but address different parts of the AI stack.