Comparison
Batch API vs Embedding
Batch API and Embedding 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 Embedding
Embedding comes up when the question is fundamentally about architecture.
OpenAI's text-embedding-3-large produces 3,072-dim vectors.
Frequently asked
What is the difference between Batch API and Embedding?
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. Embedding: An embedding is a list of numbers (a vector) that represents a piece of input — a word, a sentence, an image — in a space where similar things end up close together.
When should I use Batch API vs Embedding?
Any time the work is bulk, async, and not user-facing — embedding pipelines, evals, synthetic data, batch labeling. Embedding applies when you are focused on architecture.
Are Batch API and Embedding the same thing?
No. Batch API is inference; Embedding is architecture. They are related but address different parts of the AI stack.