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 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.
Related
- 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.
- 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.
- 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.