Batch Inference
Batch inference processes many LLM requests asynchronously instead of one-at-a-time interactively — typically at ~50% discount via provider batch APIs.
Batch inference submits many LLM requests as one asynchronous job and collects the results later, typically at about half the interactive price. Anything with no human waiting qualifies: classification backfills, synthetic data, bulk summarization, evaluation runs, re-embedding. Same model, same outputs, half the bill, and it stacks with prompt caching.
Batch inference is running LLM requests asynchronously in bulk — submit a job of many requests, collect results when ready — instead of the interactive request-response loop, usually at a steep discount.
It exists because providers can schedule deferred work into idle capacity: the standard batch tier prices at roughly half of interactive rates for results within a stated window. The candidates are everything without a user waiting — labeling and classification backfills, synthetic-data generation, periodic summarization, bulk evaluation runs, embedding regeneration — which in many products is the majority of token volume, hiding in plain sight at full price.
The practical pattern: audit your traffic, split it into interactive (humans waiting — pay for latency) and deferrable (move to batch), and stack the discounts — batch pricing composes with prompt caching on repeated prefixes. It's one of the three blunt levers in LLM cost engineering, alongside caching and model right-sizing — and the only one that's purely logistical: same model, same outputs, half the bill.
Frequently asked questions
- When should I use a batch API?
- Whenever no human is waiting: backfills, dataset labeling, synthetic-data generation, nightly summarization, embedding refreshes, bulk evals. Provider batch tiers typically cost about half of interactive pricing in exchange for results within a window (commonly up to 24 hours, usually much faster) — free money for offline workloads.
- Is batch inference the same as batching in serving?
- Different layers, same word. Provider batch APIs are a product tier: submit a file of requests, collect results later, pay less. Serving-level batching (continuous batching in engines like vLLM) is an engine technique packing concurrent requests onto the GPU. One is how you buy; the other is how the GPU stays busy.
Filed under
batch · inference · cost · throughput
Related
- Prompt CachingPrompt caching reuses the computed state of a repeated prompt prefix across requests — dramatically cutting cost and time-to-first-token for stable context.
- InferenceInference is running a trained model to produce output — for LLMs, generating tokens one at a time. Its cost and latency define the economics of AI products.
- LLM Cost and Latency Engineering: Caching, Right-Sizing, and p95 BudgetsA practical playbook for cutting LLM cost and tail latency — caching, model right-sizing, prompt trimming, and enforced p95 budgets — without losing quality.
- Synthetic DataSynthetic data is training or eval data generated by a model rather than collected from the world — filling gaps, balancing classes, bootstrapping fine-tunes.
- LLM API Pricing in 2026: Every Major Model ComparedPer-million-token prices for Claude, GPT, Gemini, DeepSeek, Mistral, and Grok — plus caching and batch discounts — verified against vendor pricing pages.
- Flash AttentionFlashAttention is an IO-aware, exact attention algorithm that runs standard attention far faster and with less memory by tiling on-chip.