When should I use batch inference vs. real-time?

Use real-time for user-facing interactions that need immediate responses—chat, voice, live coding suggestions. Use batch for background processing where a few hours of latency is acceptable—document classification, content generation, data enrichment, report generation. Many AI agents use both: real-time for interactive workflows and batch for bulk operations.

Batch Inference

Written by Max Zeshut

Founder at Agentmelt

Processing multiple AI model requests together as a batch rather than one at a time. Batch inference is significantly cheaper than real-time inference—Anthropic's batch API offers 50% cost reduction, and OpenAI's batch API is similar. AI agents use batch inference for non-time-sensitive tasks: processing overnight support ticket categorization, bulk document analysis, weekly report generation, and large-scale data enrichment. The tradeoff is latency: batch results arrive hours later rather than in seconds.

Example

An AI marketing agent needs to generate personalized email subject lines for 50,000 contacts. Instead of making 50,000 individual API calls at full price, it submits them as a batch job overnight, saving 50% on inference costs and receiving all results by morning.

Frequently asked questions

When should I use batch inference vs. real-time?: Use real-time for user-facing interactions that need immediate responses—chat, voice, live coding suggestions. Use batch for background processing where a few hours of latency is acceptable—document classification, content generation, data enrichment, report generation. Many AI agents use both: real-time for interactive workflows and batch for bulk operations.

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