Batch vs Synchronous Processing
Also: Batch API vs real-time · async batch · message batches · offline processing
Choosing between interactive real-time requests and the Batch API's cheaper, high-latency asynchronous processing for large non-urgent jobs.
Why CCAR-P tests this
CCAR-P tests matching workload latency needs to the right execution mode and knowing the Batch API's cost and timing tradeoffs.
Synchronous processing serves a request and returns the result immediately — the right choice for interactive, user-facing work where latency matters (chat, live agents).
The Batch API processes large volumes asynchronously: you submit many requests at once and collect results later. It runs at roughly 50% of standard token cost, completes within 24 hours (often much sooner), and supports tool use. That makes it ideal for offline, non-time-sensitive jobs — bulk classification, evaluations, dataset generation, large-scale summarization.
The design decision is latency tolerance versus cost and throughput. If a user is waiting, go synchronous. If the work can wait and volume is high, batch it to halve cost and avoid rate-limit pressure. Many systems use both: synchronous for the interactive path, batch for the heavy offline backlog.
Exam trap
Thinking the Batch API is only for simple prompts or can't use tools — it supports tool use, runs at ~50% cost, and completes within 24h; the real disqualifier is needing a low-latency interactive response.
Commonly confused with
Related concepts
Independent, unofficial study material from Claude Cert Prep. Not affiliated with Anthropic.