Lesson 2004 of 2116
AI Batch Processing: Run 1,000 Prompts Cheaply
Batch APIs run prompts asynchronously for ~50% off — perfect for non-urgent bulk work.
Lesson map
What this lesson covers
Learning path
The main moves in order
- 1The premise
- 2batch-api
- 3cost
- 4async-processing
Concept cluster
Terms to connect while reading
Section 1
The premise
Anthropic, OpenAI, and others offer batch endpoints with deep discounts and 24-hour SLAs. Great for backfills and analyses.
What AI does well here
- Process thousands of prompts with no rate-limit churn.
- Return results in a single downloadable file.
- Reduce per-request cost by ~50%.
- Free up sync capacity for live traffic.
What AI cannot do
- Return results in real time.
- Help with anything user-facing or interactive.
Key terms in this lesson
End-of-lesson quiz
Check what stuck
15 questions · Score saves to your progress.
Tutor
Curious about “AI Batch Processing: Run 1,000 Prompts Cheaply”?
Ask anything about this lesson. I’ll answer using just what you’re reading — short, friendly, grounded.
Progress saved locally in this browser. Sign in to sync across devices.
Related lessons
Keep going
Creators · 9 min
AI Tool Modal for Distributed Evaluation: Drafting a Fan-Out Job
AI can scaffold an AI Modal distributed evaluation job, but the cost ceiling and result aggregation policy are operator decisions.
Creators · 11 min
Tracing Every LLM Call With Inputs and Costs
Capture each call so you can debug and budget.
Creators · 11 min
Using Prompt Caching to Cut Cost and Latency
Reuse the static prefix of long prompts across calls.
