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.

Tier	Models	Best for
Premium	claude-opus-4.7, claude-opus-4.6, claude-opus-4.5	Architecture decisions, complex multi-file reasoning, security audits
Standard	claude-sonnet-4.6, claude-sonnet-4.5, gpt-5.2	Most coding tasks, code review, test generation, documentation
Fast / Cheap	claude-haiku-4.5, gpt-5-mini, gpt-4.1	File edits, boilerplate, classification, triage, simple summaries

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

Metric	How to measure
Total tokens / task	Compare before and after context changes
Model mix	Tally which models are called per workflow
Prompt size distribution	Log avg/max token counts per call type

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

Priority	Criterion
High	Premium model on a task a fast model handles well
High	Context window > 50K tokens when shorter would suffice
Medium	Duplicate context passed on every call
Medium	Fleet agents with mismatched model tiers
Low	Minor prompt size variations

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

Pattern	Fix
Entire conversation history on every call	Summarize old context, keep recent turns
Full file reads when only one function matters	Use view_range for targeted reads
Premium model for all parallel agents in fleet	Assign tier per task type
Same instructions repeated in every prompt	Move to shared system prompt
No caching on static reference docs	Check if your API client supports prompt caching

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