Streaming vs Batch LLM Inference, a Production Tradeoff Analysis

Streaming inference gets treated as the default these days, mostly because chat interfaces made it the expected UX pattern. For a client's internal pipeline that generated nightly summary reports across a few thousand documents, defaulting to streaming would have been actively worse, and the reasons are specific enough to be useful beyond this one case.

Streaming exists to solve a perceived-latency problem for a human watching a screen — the first token appearing quickly matters more to a waiting user than total completion time. A nightly batch job has no human watching it in real time, so the entire premise streaming optimizes for doesn't apply.

What batch actually bought us

• Access to batch API pricing, which ran meaningfully cheaper per token than the equivalent real-time streaming calls
• The ability to submit thousands of requests at once and let the provider schedule them against available capacity, instead of managing our own concurrency limits
• Simpler error handling — a failed item in a batch job gets retried as a batch, with no need to manage a half-streamed response mid-failure
• No need to build or maintain any UI-facing streaming infrastructure for a process that never had a UI in the first place

Streaming solves a human's perception of latency. A batch job has no human watching it, so it's solving a problem that doesn't exist there.

Where streaming is still clearly right

Anything synchronous and user-facing — a chat interface, a live support agent, an interactive coding assistant — should stream, because the latency-perception problem streaming solves is real and significant for someone actively watching a response build. The mistake is treating streaming as a universal default instead of a specific fix for a specific UX problem; for workloads without a human in the loop, batch inference is usually cheaper, simpler, and has no downside to offset against the pricing and reliability it buys back.