Term
Batch API
Asynchronous API mode that collects many requests and processes them at a significant discount — results are typically delivered within 24 hours.
Batch API — in more detail
A batch API is an asynchronous processing mode offered by AI providers such as OpenAI, Anthropic or Google. Instead of answering each request individually in real time, users upload a bundle file with hundreds or thousands of requests; the provider processes them off-peak and returns the results within a service-level window (commonly 24 hours). In exchange, the token price typically drops by around 50 % compared to the real-time API.
Example / practical context
Common use cases are non-interactive workloads: classifying a large corpus, generating embeddings for a knowledge base, analyzing customer feedback at scale, or producing synthetic training data. The workflow is always similar — prepare a JSONL file of requests, upload it, wait for the completed status, then download the results file.
Delineation from similar terms
A batch API differs from the regular (synchronous) API in latency and price: synchronous means answers in seconds at full tariff; batch means answers in hours at a discounted tariff. It should also not be confused with streaming, which delivers a single response token by token within one request.
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Glossar$/MTok (Cost per Million Tokens)
Standard pricing unit for AI APIs — cost in US dollars per one million processed tokens. Listed separately for input, output and sometimes cache.
LexikonFunction Calling / Tool Use
How an LLM uses tools: define a tool as a schema, the model picks the function and arguments, the result returns to the chat — the basis of every agent.