47. Cost-Optimized Workflows
Build efficient AI workflows that minimize cost while maintaining quality.
By Jacques Botte, founder of Toptronic®. Last updated 12 September 2026.
The lesson
Tiered model workflow: Use cheaper/smaller models for initial drafts and classification, then escalate to frontier models only for final review or complex cases.
Batch processing: Process multiple items together when possible to reduce per-request overhead.
Context management: Remove unnecessary text, truncate long inputs to relevant sections, and cache repeated context.
Caching and reuse: Save and reuse good prompts instead of regenerating similar requests.
Monitoring: Track your token usage and model costs to identify optimization opportunities.
Check yourself
Question 1: What is a tiered model workflow?
- Always using the most expensive model
- Using cheaper models for drafts and frontier models for final review — correct
- Only using local models
- Random model selection
Answer: Using cheaper models for drafts and frontier models for final review
Tiered workflows match model cost to task value - cheap for drafts, premium for final review.
Question 2: When processing 10,000 support tickets, which strategy saves costs?
- Use GPT-5.5 Pro for every ticket
- Use a fine-tuned smaller model or classification system with escalation to frontier models only for complex cases — correct
- Process them manually
- Skip analysis entirely
Answer: Use a fine-tuned smaller model or classification system with escalation to frontier models only for complex cases
Batch processing benefits from using appropriate models per task complexity, escalating only when needed.
Question 3: How can you reduce costs when using frontier models?
- Increase context window to maximum always
- Cache results, reuse prompts, batch requests, and truncate unnecessary context — correct
- Send duplicate requests
- Always request maximum output length
Answer: Cache results, reuse prompts, batch requests, and truncate unnecessary context
Cost optimization includes caching, context trimming, and batching rather than wasteful repetition.
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