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Decision guide

Streaming vs Synchronous vs Batch LLM Calls

How should your application call an LLM for a given feature?

Side by side

CriterionStreamingSynchronous request/responseBatch API / background queue
User-perceived latencyLowest (first token fast)Full generation timeMinutes–hours
CostList priceList priceDiscounted (~50% on some providers)
Validation before useAfter stream completesImmediatePer result
Rate-limit handlingPer requestPer requestProvider-managed (batch) or your queue
Failure handlingPartial output; cancel on disconnectRetry requestPer-item errors/expiry; map by custom_id
Best fitChat UIsAPIs and pipelines stepOffline jobs

When to choose each

Streaming

Tokens stream to the client as they're generated (SSE relay).

Choose when

  • Chat and assistants
  • Long outputs (avoids HTTP timeouts)
  • Perceived latency matters

Avoid when

  • Machine-consumed outputs that need validation before use

Synchronous request/response

Wait for the complete response, then use it.

Choose when

  • Short structured outputs (classification, extraction)
  • Server-side steps inside a request

Avoid when

  • Long generations (timeouts)
  • Bulk processing

Batch API / background queue

Submit many requests asynchronously; collect results later.

Choose when

  • Bulk classification, summarization, backfills
  • Cost-sensitive offline work (discounted on several providers)

Avoid when

  • Users waiting for results

Verdict

Stream anything a person reads as it's generated, call synchronously for short machine-consumed results (ideally with structured outputs), and batch everything nobody is waiting for. Many products mix them: streaming chat, synchronous intent classification, nightly batch summarization.

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