batch processing

advancedwidespread

Submitting many AI requests at once in a batch, where the response arrives within hours rather than immediately, in exchange for a much lower price.

In plain terms

When chatting with a chatbot, you expect an immediate response. For many tasks, however, it does not matter whether the result is ready in two seconds or half a day: examples include classifying ten thousand customer reviews or translating the product descriptions of an entire online store. Batch processing is designed for these: requests are submitted not individually, but in one large package.

It is like a laundry service. If you want a shirt cleaned immediately and out of turn, you pay a surcharge. If you drop off the entire week's laundry and pick it up the next day, it is cheaper because the laundry starts the machine when it has spare capacity.

Language model providers think the same way: they do non-urgent work when their servers are less busy. In return, they give a substantial discount, typically around 50%, compared with immediate responses.

The quality of the response is the same, as the same model is working, just not in real time. What you sacrifice is speed and the possibility of dialogue: you cannot ask follow-up questions or clarify mid-process.

How it works

You assemble the package: all requests go into a single file or list, each with its own identifier, so you can later know which answer belongs to which question.
You submit the package to the provider's Batch API interface, which confirms receipt and assigns an identifier to the batch.
The provider processes requests in the background according to its own schedule, usually within a predefined deadline, typically 24 hours, but often much sooner.
Meanwhile, you can query the progress: how many requests have been completed, how many errors occurred, and how many remain.
Once the batch is ready, download the results together and match them to the original requests using identifiers; faulty or expired items can be resubmitted.

An everyday example

An online store submits three thousand customer reviews received during the day in the evening, so the AI can mark which is a complaint, which is praise, and what the topic is. By morning, the table is ready, and customer service starts the day with the organized list, all at half the cost of handling them individually and immediately.

Who is affected

developerThey must decide which tasks can tolerate waiting, and write the program so that it can also handle results that arrive delayed and all at once. → Code

entrepreneur, executiveThe cost of large-scale, non-urgent AI tasks can thus be reduced by up to half, which often determines whether a project is worthwhile.

marketerProcessing thousands of product descriptions, newsletter variants, or customer feedback is typically done in batches overnight. → Marketing

A common misconception

MythCheaper batch processing gives lower-quality answers because a less capable model works in the background.

In realityThe same model responds with the same quality. The discount price is the wait and the inability to intervene during the process: the provider performs the work when there is available computing time.

Limits and risks

Not suitable for anything where someone is waiting for an answer: chat, customer-service conversations, or an agent that must decide immediately, step by step.
If your instruction is flawed, it only becomes apparent after the entire batch has run, so it is advisable to test first on a few requests in immediate mode.
The deadline is an upper limit, not a promise of speed: requests not completed by the deadline expire and must be resubmitted.
The discount rate, maximum batch size, and deadline vary by provider and may change over time, so always check the current terms.

Similar, but not the same

response streaming – In streaming, the answer appears immediately, bit by bit, as the model writes it. In batch processing, however, you receive all answers later, even hours later, in one go.
prompt caching – Caching saves by reusing repeated text segments and works even with instant responses, while batch processing offers a discount in exchange for waiting.
batch size in training – It means how many examples the model sees at once during training, not how you send requests to the finished model.

Related concepts

Broader

API, inference, large language model, asynchronous processing

Narrower

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On the edge

token, prompt caching, streaming, latency, rate limit

A brief history

Batch processing is one of the oldest computing workflows: mainframes of the 1950s and 1960s received tasks in stacks of punch cards, lined up in sequence. For language models, it became a standard service in 2024, when OpenAI and Anthropic each launched a separate, discounted Batch API.

Where things stand

At the end of 2026, almost all major model providers and cloud platforms offer a discounted batch mode. It has become a standard, cost-reducing tool for large-scale data processing, evaluation, and content production.

Status: 2026 November

Also known as

batch processing · batch processing · batch execution · batch request · batch processing · Batch API · batch inference · Message Batches API

Sources

This entry was machine-edited and has undergone machine expert review; the “In the news” section updates automatically from Agent Age stories.