cost-audit

name: cost-audit description: Use when AI inference costs are growing unexpectedly, when comparing model choices by cost/quality ratio, or when optimizing token usage across a multi-model pipeline — produces an actionable cost reduction plan metadata: category: workflow agent_type: general-purpose

Cost Audit

Audit AI inference costs and optimize token usage across multi-model pipelines. This is not about cutting capabilities — it is about eliminating waste, right-sizing models, and keeping costs predictable.

When to Use

• AI API costs growing faster than usage justifies
• Unsure whether you are using the right model tier for each task
• Want to compare cost-quality trade-offs before committing to a model
• Preparing for production traffic and need a cost baseline
• Running fleet mode or parallel agents and want to avoid runaway spend

Model Cost Tiers

Use the most capable model necessary — not the most capable model available.

Workflow

1. Identify high-cost call sites

Scan for:

• Long system prompts that repeat across calls
• Premium models used for simple transforms
• Entire file contents passed when only relevant sections are needed
• No context caching on static instructions
• Fleet mode with all agents on premium tiers

2. Measure baseline

3. Apply reduction patterns

Model downgrade

• Does this task require premium reasoning? If not, drop a tier.
• Classification, routing, simple edits → use fast/cheap tier
• Reserve premium for tasks that demonstrably need it

Context pruning

• Pass a summary instead of the full history when prior turns are less relevant
• Slice file ranges with view_range instead of full-file reads
• Remove redundant boilerplate from system prompts

Prompt deduplication

• Repeated instructions in every call → move to a system prompt / shared prefix
• Static context that never changes → candidate for caching (if platform supports it)

Task batching

• Small independent tasks → batch into one call instead of N separate calls
• Fan-out agents → assign right tier per task, not fleet-wide premium

4. Estimate savings

For each change:

Change: Replace claude-opus-4.7 on doc-summary with claude-haiku
Before: ~4,000 tokens × $0.015/1K = $0.06/call
After:  ~4,000 tokens × $0.00025/1K = $0.001/call
Savings: ~$0.059/call, ~$590/10K calls

Use approximate public pricing for estimation. Actual prices vary; check your provider dashboard.

5. Prioritize

6. Report format

## Cost Audit Report

### Summary
Estimated waste: ~$X/day at current scale
Top three opportunities: [list]

### Findings

#### [HIGH] Premium model for boilerplate generation
Location: [file or workflow name]
Issue: `claude-opus-4.7` used for all code generation including templates and stubs.
Recommendation: Use `claude-haiku-4.5` for boilerplate; reserve opus for complex tasks.
Estimated savings: ~80% cost reduction on boilerplate tasks.

#### [MEDIUM] Entire codebase passed as context on every PR review
...

Common Waste Patterns

See Also

• multi-model-strategy — when to use which model tier
• team-planner — assigning models per agent in a team
• orchestration/templates/orchestrator-template.md — model selection guidance in orchestration context