Whats is Batch Inference? (2027)

Whats is Batch Inference? (2027) | DigitalOcean

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AI/ML

Whats is Batch Inference? (2027)

By Surbhi

Published: October 26, 2026 16 min read

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Last week&#x2019;s support tickets, a 50,000-item product catalog, and a backlog of contracts to summarize. What do these jobs have in common? None of them needs a response in two seconds. But if you run them through the same real-time endpoints as your live traffic, you pay live-traffic rates for every one.

The costs add up: Gartner forecasts &#x24;2.7 trillion in worldwide AI spending in 2026 and reports that enterprises are asking their providers to help them manage costs. Batch inference is built for exactly this gap.

On DigitalOcean, batch requests to OpenAI and Anthropic models cost up to 50% less than real-time rates and run on rate limits separate from your production traffic. Let&#x2019;s explore how batch jobs work, where they pay off, and where they don&#x2019;t, so you can decide when to use them for your AI workloads.

Key takeaways :

Batch inference runs a large set of AI requests as one asynchronous job, trading an instant response for lower cost and higher throughput.

On DigitalOcean, batch requests cost up to 50% less than real-time rates, finish within a 24-hour completion window, and run on rate limits separate from your live traffic.

Batch inference suits work where results can wait, such as document extraction, ticket triage, and evaluations, but it doesn&#x2019;t suit interactive features or jobs that need results in minutes.

DigitalOcean Batch Inference runs on the Inference Engine and supports OpenAI and Anthropic text models today. Open-weight models aren&#x2019;t currently supported.

What is batch inference?

Batch inference (also called offline inference or asynchronous inference) is running an AI model over a large set of requests as a single asynchronous job, with results returned later instead of immediately. Think of jobs like scoring an evaluation suite, tagging a year of product reviews, or generating synthetic training data. You hand over a file of requests, the system processes them when capacity allows, and you collect the outputs when the job finishes. The trade-off involves giving up an instant response to get a lower cost per request and higher throughput.

Batch inference vs. real-time inference

Real-time inference answers one request at a time while a user or application waits. Batch inference processes thousands of requests in parallel, so nobody waits for the response for any single request.

Parameter Real-time inference Batch inference

Response time Seconds, per request Results will be available when the job completes, within a 24-hour window on DigitalOcean

Cost per request Standard per-token rate Up to 50% lower on DigitalOcean for OpenAI and Anthropic models

Rate limits Counts against your live quota Separate limits on DigitalOcean, so a running job doesn&#x2019;t use the real-time quota

How results arrive In each response A results file plus a separate error file, kept up to 30 days on DigitalOcean

Best-fit workloads Chat, agents, search, anything a user waits on Bulk extraction, classification, enrichment, and evaluations

How batch inference works

A real-time inference system handles one request, returns one response, and repeats. A batch system collects the requests first, then runs them as a group:

Real time: app &#x2192; request &#x2192; model &#x2192; response (repeated for every request)

Batch: app &#x2192; file of requests &#x2192; job queue &#x2192; model &#x2192; results file + error file

Batch inference follows the same five-step workflow on most platforms, including DigitalOcean. You prepare and submit the job, the platform runs it, and you collect the results:

Collect your inputs : Gather the prompts you want to run&#x2014;one request per line in a JSON Lines (JSONL) format, each with a unique ID so that you can match results back to inputs.

Submit them as one job : Upload the file and create the job, setting a completion window, the time the job has to finish. On DigitalOcean, that&#x2019;s 24 hours.

Queue and run the job : The platform validates the file, queues the job, and processes requests as capacity allows within the completion window.

Review the output : Outputs are shared in a results file; failed requests go to a separate error file.

Download and use the results : Download the files, match each line to its input by ID, and write the outputs to your database or pipeline.

Batch inference vs. continuous batching

Continuous batching (also called dynamic batching) is a scheduling technique inside a model-serving engine: instead of waiting for a group of requests to finish, the engine adds and removes requests from the running batch at every decoding step. Batch inference is more about how you submit work: one asynchronous job, results delivered later.

Note that a provider can use continuous batching for real-time or batch traffic, so one doesn&#x2019;t replace the other.

What happens inside the serving engine? Static batching waits for the slowest request in a batch to finish, while continuous batching updates the active batch at every generation step, so queued requests can start as soon as another one finishes. Our guide to continuous batching vs. static batching explains the scheduling behind both and how vLLM implements it.

Benefits of batch inference

Batch inference pays off when volume is high, and speed isn&#x2019;t the priority.

The gains show up on your bill, in your rate limits, and in the code you don&#x2019;t have to write:

Lower cost : On the DigitalOcean Inference Engine, you pay up to 50% less than real-time rates for batch requests to OpenAI and Anthropic models. You&#x2019;re billed only for completed requests, so unprocessed work from a failed or expired job never appears on your invoice.

Higher throughput without rate-limit pain : Batch jobs use separate rate limits from serverless and dedicated inference , so a running job doesn&#x2019;t consume your real-time quota. Push bulk work through without crowding out the endpoints your users depend on.

Simpler operations : Skip writing queueing code, because the platform retries transient errors (429, 408, and 5xx) up to two times on your behalf. One failed request doesn&#x2019;t fail your batch; failures are logged in a separate error file you can review.

Optimized resource use : Your jobs run at a lower scheduling...