Batch inferencedefinition and how it works in 2026
- Batch inference
- Running model inference asynchronously over a large batch of inputs, traded for latency. OpenAI/Anthropic batch APIs are typically 50% cheaper than sync calls.
Batch inference sends a large set of prompts to the model in a single async submission. The provider processes the batch when capacity is available (typically within 24h) and returns results in one delivery. You give up real-time latency in exchange for cost — OpenAI's and Anthropic's batch APIs are 50% the price of synchronous calls.
The right workloads for batch: nightly data enrichment, bulk content generation, evaluation runs, training-data labeling, periodic summarization. Anything where no human is waiting for the next token. The wrong workloads: chat, agent loops, interactive coding — anywhere latency matters.
Most serious AI deployments use both. Sync API for interactive work (chat, agents, real-time generation) + batch API for the rest. The cost split for a mature production setup is often 60–70% batch / 30–40% sync — most workloads turn out to be batchable once you look closely.
Frequently asked
How much do I save with batch inference?+
OpenAI: 50% off synchronous prices. Anthropic: 50% off. Google: 50% off Gemini. The discount is roughly standard across the frontier vendors.
How long does batch processing take?+
Typically 1–24 hours depending on provider and current load. SLA is "within 24 hours" — actual median is more like 1–4 hours during normal conditions.