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Not every AI task needs an answer in 200 milliseconds. Nightly report summaries, bulk content classification, large-scale data enrichment — these don't need real-time responses, and treating them like they do means paying for synchronous, on-demand handling you don't actually need.
Why This Matters
Batch inference lets you submit a large collection of requests as a single asynchronous job, instead of firing them off one at a time and waiting on each. You submit the job, it processes in the background, and you retrieve the results once it's done — typically within a defined processing window.
This matters for token efficiency in a less obvious way than caching or model-sizing: it's less about the token cost of any individual request and more about eliminating the overhead of managing thousands of individual synchronous calls, retry logic, and rate-limit handling for work that was never time-sensitive in the first place.
- Real-time chat, live user-facing features → synchronous requests
- Bulk classification, summarization, enrichment, offline analysis → batch requests
- If a job doesn't finish before its processing window closes, work completed up to that point is still saved and billed — nothing completed is lost
A Simple Framework
- Identify your non-time-sensitive workloads — anything that doesn't need an answer in real time
- Group those requests into a single batch job instead of a loop of individual API calls
- Submit the job and poll (or wait) for completion, rather than blocking on each request
- Build in handling for partial completion, in case the job doesn't finish within its window
- Reserve synchronous requests for genuinely interactive use cases
Example
Before: A content team runs a script that loops through 10,000 product descriptions overnight, calling the inference API synchronously for each one, one at a time, with manual retry logic for rate limits.
After: The same 10,000 descriptions are submitted as a single batch job. The job processes in the background, and the team retrieves all 10,000 results the next morning — with far less custom retry and rate-limit code to maintain.
> Tip: Batch jobs are a good fit for anything you'd otherwise run as an overnight cron script. If you're already treating a task as "run this later," it's very likely a batch inference candidate.
Common Mistakes
- Running large bulk workloads through synchronous, one-at-a-time API calls
- Not planning for a job that doesn't complete within its processing window
- Building custom retry and backoff logic for problems that batch processing already handles
- Treating every task as urgent when most bulk workloads genuinely aren't
DigitalOcean documents batch processing behavior, including how partial completions are handled, in its Inference documentation.
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The token cost doesn't change dramatically with batching — the engineering overhead does. Less custom retry code, fewer rate-limit headaches, and a cleaner separation between "needs an answer now" and "can wait."