Batch processing submits many LLM requests together for processing without real-time response requirements — often at meaningfully lower cost than real-time API calls, in exchange for higher, less predictable latency (results may take longer, sometimes hours).
When Batch Processing Fits
| Good Fit | Poor Fit |
|---|---|
| Nightly categorization of the day's support tickets | A live chatbot responding to a user in real time |
| Bulk summarization of a large document archive | An interactive coding assistant |
| Periodic content moderation review of a backlog | Real-time fraud detection requiring immediate response |
Conceptual Pattern
# Conceptual — real batch APIs have their own specific submission
# and polling/retrieval mechanisms per provider
requests = [
{"id": "req-1", "prompt": "Summarize ticket #101..."},
{"id": "req-2", "prompt": "Summarize ticket #102..."},
# ... potentially thousands more
]
batch_job = llm_client.submit_batch(requests)
# ... time passes, potentially hours ...
results = llm_client.get_batch_results(batch_job.id)Why Batch Processing Is Often Cheaper
Providers can schedule batch workloads more flexibly against available compute capacity, rather than reserving capacity for immediate response — this operational flexibility is typically reflected in lower per-token pricing for batch versus real-time requests. Exact discounts and turnaround-time guarantees vary by provider and should be checked against current documentation.
Practical Use Case
A company processing 50,000 archived documents for classification doesn't need results in seconds — batch processing at reduced cost is the more sensible architectural choice than making 50,000 real-time API calls.
Common Mistakes
- Using real-time API calls for genuinely non-time-sensitive bulk workloads, paying a real-time cost premium unnecessarily
- Using batch processing for a task that actually needs a timely result, and being surprised by multi-hour turnaround
- Not building retry/error-handling logic for individual failed items within a large batch — a batch job succeeding overall doesn't mean every individual request within it succeeded
Interview Relevance
"When would you use batch processing instead of real-time API calls?" — non-time-sensitive, high-volume workloads where cost matters more than immediate turnaround is the core signal.
Practice Question
Identify which of these should use batch vs real-time processing: (1) live customer chat support, (2) monthly report generation from 10,000 records, (3) real-time content moderation on user posts.
