AI Stack Spend Audit

Stop overpaying for AI. This skill audits your actual LLM usage across every provider, finds the waste, and projects your runway at current burn rate.

What it does

• Pulls usage data from OpenAI, Anthropic, Google AI Studio, AWS Bedrock, Azure OpenAI, and self-hosted (Ollama, vLLM, TGI)
• Computes true cost per feature, per user, per request — not just per token
• Detects waste: oversized models for the task, idle always-on endpoints, redundant calls (same prompt twice), broken streaming
• Projects runway at current burn rate and trend
• Generates a budget plan with concrete reduction actions
• Outputs a one-page exec report plus a detailed CSV for finance

When to use it

• Your OpenAI bill jumped 3x and you don't know why
• You have multiple LLM providers and no unified view
• Engineering says "we need to cut AI spend" and you need a real plan
• You're pitching to investors and need a credible burn-rate story
• You want to know if self-hosting (Llama 3.1 70B) would actually save money
• You're a fractional CTO auditing a portfolio company's stack

Why it's better than ad-hoc prompting

Most "audit my LLM spend" prompts give you generic advice. This skill is different:

• Reads your actual logs — usage_log.jsonl, CloudWatch exports, provider dashboards
• Statistical rigor — uses Tukey fences for outlier detection, not vibes
• Cross-provider normalization — converts everything to $/Mtok with provider-specific pricing
• Actionable — every finding has a save_per_month estimate and a how_to_fix link
• Forecasting — projects 30/60/90 day spend with confidence bands

Architecture

┌─────────────────────────────────────────────────────────┐
│                Agent (Claude/Cursor)                    │
│  - Asks for cost data sources                            │
│  - Calls analyzer script                                │
└───────────────┬─────────────────────────────────────────┘
                │
                ▼
┌─────────────────────────────────────────────────────────┐
│           skills/ai-stack-spend-audit/                  │
│  scripts/                                               │
│    ├── ingest.py        # Multi-source data loader       │
│    ├── analyze.py       # Cost & waste computation       │
│    ├── project.py       # Runway + forecasting           │
│    ├── report.py        # Markdown + CSV output          │
│    └── optimize.py      # Concrete cut recommendations   │
│  references/                                            │
│    ├── provider-pricing-2026.md  # $/Mtok tables        │
│    ├── self-host-tco-calculator.md                       │
│    └── finops-playbook.md                               │
└─────────────────────────────────────────────────────────┘

Quick start

# 1. Install
pip install pandas matplotlib

# 2. Ingest from any provider
python scripts/ingest.py --source openai --api-key $OPENAI_ADMIN_KEY --out usage.csv
python scripts/ingest.py --source anthropic --api-key $ANTHROPIC_ADMIN_KEY --out usage.csv
python scripts/ingest.py --source aws-bedrock --s3-bucket my-llm-logs --out usage.csv
python scripts/ingest.py --source local --log-file ./vllm.log --out usage.csv

# 3. Analyze
python scripts/analyze.py usage.csv

# 4. Project runway
python scripts/project.py usage.csv --current-cash 500000 --out report.md

# 5. Get the cut plan
python scripts/optimize.py usage.csv --target-cut 30

Sample output (excerpt)

## AI Stack Spend Audit — June 2026

Total spend MTD:        $14,231.55
Projected month-end:    $28,400
vs. last month:         +47%  ⚠️
Cost per active user:   $0.47/day
Cost per 1k requests:   $3.12

### Top 5 waste sources
1. ❌ Customer-support-bot using gpt-4o for FAQ: $4,200/mo
   → Switch to gpt-4o-mini, save $3,800/mo (90% reduction)
2. ❌ Embeddings regenerated on every request: $2,100/mo
   → Add Redis cache (1h TTL), save $1,900/mo
3. ❌ Context window always max (128k) for 200-token prompts: $1,800/mo
   → Truncate to 4k, save $1,500/mo
4. ⚠️ Anthropic Claude 3 Opus for "summarize this email": $1,200/mo
   → Switch to Claude 3.5 Haiku, save $1,100/mo
5. ⚠️ Self-hosted Llama 3.1 70B on A100x4 running 3% utilized: $3,400/mo
   → Scale to 1 GPU or shut down, save $2,500/mo

Runway at $500k cash, current burn: 17.6 months
After recommended cuts: 31.4 months (+14 months)

Supported data sources

| Source | Method | Auth | |--------|--------|------| | OpenAI | Admin API /v1/usage | Admin key | | Anthropic | Admin API /v1/organizations/usage | Admin key | | Google AI Studio | Cloud Logging | Service account | | AWS Bedrock | CloudWatch Logs | IAM | | Azure OpenAI | Cost Management API | AAD | | Ollama | Log file parsing | None | | vLLM | Log file parsing | None | | Custom (JSONL) | Direct file | None |

The waste patterns it catches

1. Oversized models — using Opus/GPT-4 for trivial tasks
2. Uncached embeddings — same text re-embedded thousands of times
3. Streaming abandonment — clients disconnect mid-stream, you still pay for generated tokens
4. Context stuffing — sending 100k tokens for a 200-token answer
5. Idle endpoints — always-on GPU instances at <10% utilization
6. Redundant calls — same prompt sent multiple times (no idempotency)
7. Function-call loops — agents calling tools recursively without exit conditions
8. Test/prod mixing — dev traffic on production keys
9. Expensive fallbacks — retrying on an expensive model when a cheap one would do
10. Time waste — long-running requests for cheap work (over-provisioned)

Pricing

Single-purchase, lifetime access. $12.00.

Includes:

• 5 Python scripts (ingest, analyze, project, report, optimize)
• Provider pricing reference (updated quarterly)
• Self-host TCO calculator
• FinOps playbook (12 optimization patterns)
• Sample data for testing
• Future updates for the same major version

Example usage

"Here's our OpenAI Admin key. We have $50k in the bank. What are we wasting and when do we run out?"

The skill will:

1. Pull last 90 days of usage from OpenAI
2. Categorize by feature (requires tagging, falls back to model)
3. Compute per-feature cost
4. Identify top 5 waste sources
5. Project runway at current burn
6. Output report.md + spend-detail.csv

Compliance

Generates evidence suitable for:

• SOC2 cost-monitoring controls
• FinOps Foundation certification
• Board-level budget reviews
• Customer audits (pass-through billing)

Compatibility

Works with any agent that supports the SKILL.md standard and can execute Python: Claude Code, OpenClaw, Codex CLI, Cursor, Gemini CLI, Cline, Windsurf, Aider. Tested on Linux, macOS, Windows.

Tags

finops, cost-optimization, llm, openai, anthropic, aws, budget, agent-ops, observability, runway