The Model Serving article closed on a promise: processing many requests together rather than one at a time. This article covers exactly that technique: batch inference.
The simple definition
Batch inference is the practice of grouping many requests together and processing them as one unit, rather than handling each request the moment it arrives. Recall from the Batch article, back in the Training Mechanics phase, that batching groups multiple training examples together for efficiency during training. Batch inference applies that same core idea at the opposite end of a model’s life — not during training, but when the trained model is actually being used, grouping many separate inference requests together instead of running the model once per request.
Why grouping requests together is genuinely more efficient
Recall from the GPU article’s core strength — thousands of cores doing the same operation simultaneously. Running one single request through a model leaves most of a GPU’s parallel capacity sitting idle, since one request’s worth of computation doesn’t come close to filling all those cores. Batch inference solves this directly: bundling many requests together lets the GPU’s full parallel capacity actually get used, since the same underlying matrix multiplication, covered throughout the Neural Networks phase, can process many inputs at once almost as fast as processing one.
flowchart LR
A[Request 1] --> D[Grouped into one batch]
B[Request 2] --> D
C[Request 3] --> D
D --> E[GPU processes the whole batch together, efficiently]
E --> F[Results returned once the batch completes]The real, honest trade-off: speed for the group versus speed for any one request
This is worth being direct about, since it’s the central, unavoidable trade-off this technique makes. Batch inference dramatically improves overall throughput — covered fully in its own article later in this phase — since the GPU spends its time genuinely working rather than sitting idle between requests.
But no single request in that batch gets its answer back until the entire batch finishes processing, meaning any one individual request waits longer than it would have if handled immediately and alone. Batch inference is precisely the right choice when nobody is waiting on the other end of an individual request in real time.
ANALOGY vs. TECHNICAL REALITY
Analogy: Think of a commercial bakery that could bake one single loaf of bread at a time in a massive industrial oven, or wait until it has 200 loaves ready and bake them all together, filling the oven’s actual capacity. Baking one loaf at a time wastes almost all of the oven’s real capacity; baking 200 together is dramatically more efficient per loaf, but any single customer’s specific loaf isn’t ready until the whole batch finishes.
Where this breaks down: A baker chooses batch size somewhat flexibly based on oven space. Batch inference’s actual grouping is governed by real, automated scheduling logic inside the inference server, covered in its own article — deciding, often continuously and dynamically, exactly which requests to group together and when to actually run the batch, a precise, algorithmic decision rather than a baker’s practical judgment call.
The real, industry-standard pricing this trade-off enables
This is worth grounding in genuine, published numbers, since three major AI labs independently arrived at essentially the same real pricing structure. OpenAI, Anthropic, and Google all offer a dedicated Batch API, letting developers submit large volumes of requests that don’t need an immediate response, in exchange for a standard 50 percent discount on both input and output token pricing, with results typically returned within 24 hours.
Anthropic’s own published Batches API, for instance, lets developers submit up to 10,000 queries at once at this same half-price rate — a genuine, real, and remarkably consistent industry convention, reflecting how directly batching reduces the actual compute cost of serving those requests.
A concrete example, layered
For a simple beginner example: a company wanting to generate short summaries for 50,000 archived support tickets overnight would use batch inference, submitting the entire job at once and receiving results the next morning at half the per-token cost of processing each ticket individually and immediately. For a production example: real companies use batch APIs from OpenAI, Anthropic, and Google specifically for large-scale, non-urgent workloads — bulk content classification, dataset labeling, and large-scale model evaluation — exactly the kinds of tasks referenced in Anthropic’s own published Batches API documentation as ideal use cases.
Common misconception
Match every result to its request
Each batch item should carry a unique request identifier:
ticket-1042 -> summarize text A
ticket-1043 -> summarize text B
ticket-1044 -> summarize text CResults may not return in input order. The application uses the identifier to connect each output or error to the correct ticket.
Partial failure and safe retry
A job containing 10,000 items might finish with 9,950 successes and 50 failures. Retrying all 10,000 could waste money or duplicate downstream writes. A better flow is:
collect failures -> classify retryable errors -> resubmit only safe failuresAn idempotent operation can be repeated without creating a different final result. Storing a summary under its ticket ID can be made idempotent; “send this email” needs stronger duplicate protection.
Current provider caution
OpenAI, Anthropic, and Google provide asynchronous batch options, but their supported models, discounts, file limits, and completion targets are provider-specific and can change. The application should store the provider response and check the current API documentation instead of assuming all three services have identical rules.
Additional verified source
Follow an overnight batch job
50,000 support tickets
-> write one request per line
-> upload batch file
-> provider processes requests asynchronously
-> download successes and errors
-> validate and store summariesThe application does not keep 50,000 live connections open. It submits work, receives a job identifier, and checks later for completion.
Batch inference versus server-side batching
| Term | Who groups the work? | When does the caller expect results? |
|---|---|---|
| Batch API or offline inference | The customer submits a large asynchronous job | Minutes or hours later |
| Dynamic/continuous batching | The inference server combines live requests internally | While users are waiting |
Both improve hardware use, but they solve different product problems.
A numerical decision
If 100,000 requests cost 1,000**. A provider discount of 50% would reduce the API charge to $500, but the job may take much longer and requires retry and result-matching logic.
OpenAI, Anthropic, and Google publish asynchronous batch services for non-urgent work. Exact limits, completion windows, supported models, and prices can change, so production code should read the provider’s current documentation rather than hard-code assumptions from an article.
Verified sources
A frequent beginner assumption: that batch inference is simply a slower, cheaper version of normal inference, suitable only when a company wants to save money at the cost of speed. As this article has explained, this understates the real distinction — batch inference isn’t just “slower,” it’s architecturally different, processing many requests as one genuinely more efficient unit of GPU work, which is precisely why it can be offered at half the price rather than simply being throttled, artificially delayed, real-time processing.
Where this fits in what comes next
You now understand grouping requests for efficiency when immediate responses aren’t needed. The next article, Streaming, covers the opposite instinct — a technique specifically for when a user genuinely is waiting in real time, and every fraction of a second of perceived delay matters.
In one sentence
Batch inference groups many requests together to use a GPU’s parallel capacity efficiently, trading immediate response for genuine, real cost savings — a trade-off so consistently valuable that OpenAI, Anthropic, and Google all independently converged on the same real, published 50% discount for exactly this technique.
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