modbender AI Spend Audit
Audit your company's AI spending — find waste, measure ROI, and right-size your tool stack.
When to Use
- Quarterly AI budget reviews
- Before renewing AI tool subscriptions
- When AI spend exceeds 3% of revenue without clear ROI
- Evaluating build vs buy decisions for AI capabilities
The Framework
Step 1: Inventory Every AI Line Item
Map all AI spending across these categories:
| Category | Examples | Typical Waste |
|---|---|---|
| Foundation Models | OpenAI, Anthropic, Google API keys | 40-60% (unused capacity, wrong model tier) |
| SaaS with AI | Salesforce Einstein, HubSpot AI, Notion AI | 30-50% (features enabled but unused) |
| Custom Development | Internal ML teams, fine-tuning, RAG pipelines | 25-45% (duplicate efforts, over-engineering) |
| Infrastructure | GPU instances, vector DBs, embedding compute | 35-55% (over-provisioned, always-on dev instances) |
| Data & Training | Labeling services, training data, synthetic data | 20-40% (one-time costs recurring unnecessarily) |
Step 2: Score Each Tool (0-100)
Usage Score (0-30)
- 0: Nobody uses it
- 10: <25% of licensed users active
- 20: 25-75% active
- 30: >75% active, daily use
ROI Score (0-40)
- 0: No measurable business impact
- 10: Saves time but no revenue/cost link
- 20: Measurable cost reduction (<2x spend)
- 30: Clear ROI (2-5x spend)
- 40: High ROI (>5x spend)
Replaceability Score (0-30)
- 0: Commodity (10+ alternatives at lower cost)
- 10: Some alternatives exist
- 20: Few alternatives, moderate switching cost
- 30: Irreplaceable, deep integration
Action Thresholds:
- Score 0-30: CUT — cancel immediately
- Score 31-50: REVIEW — renegotiate or find alternative
- Score 51-70: OPTIMIZE — right-size tier/usage
- Score 71-100: KEEP — monitor quarterly
Step 3: Model Cost Optimization
For every API-based AI tool, check:
Model Selection: Are you using GPT-4 where GPT-3.5 suffices? Claude Opus where Sonnet works?
- Rule: Use the cheapest model that meets quality threshold
- Test: Run 100 production queries through cheaper model, measure quality delta
Caching: Are you re-processing identical or similar queries?
- Semantic cache can cut 20-40% of API calls
- Exact-match cache catches another 5-15%
Batch vs Real-time: Which requests actually need sub-second response?
- Batch processing is 50% cheaper on most providers
- Queue non-urgent requests for batch windows
Token Optimization:
- Trim system prompts (every token costs money at scale)
- Use structured output to reduce response tokens
- Implement max_tokens limits per use case
Step 4: Vendor Consolidation
Map overlapping capabilities:
Current State → Target State
─────────────────────────────────────────
ChatGPT Teams + Claude Pro + Gemini → Pick ONE primary + ONE backup
Jasper + Copy.ai + ChatGPT for content → Single content tool
3 different vector databases → Consolidate to 1
Internal embeddings + OpenAI embeddings → Standardize on one
Consolidation savings: Typically 25-40% of total AI spend.
Step 5: Build the Audit Report
AI SPEND AUDIT — [Company Name] — [Quarter/Year]
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
Total AI Spend: $___/month ($___/year)
AI Spend as % Revenue: ___%
Industry Benchmark: 2-5% (early adopter) / 0.5-2% (mainstream)
WASTE IDENTIFIED
├── Unused licenses: $___/month
├── Over-provisioned infra: $___/month
├── Model tier downgrades: $___/month
├── Vendor consolidation: $___/month
└── TOTAL RECOVERABLE: $___/month ($___/year)
ACTIONS
┌─ CUT (Score 0-30): [list tools]
├─ REVIEW (Score 31-50): [list tools]
├─ OPTIMIZE (Score 51-70): [list tools]
└─ KEEP (Score 71-100): [list tools]
90-DAY PLAN
Week 1-2: Cancel CUT items, begin REVIEW negotiations
Week 3-4: Implement model downgrades and caching
Week 5-8: Vendor consolidation migration
Week 9-12: Measure savings, establish ongoing monitoring
Company Size Benchmarks (2026)
| Company Size | Typical AI Spend | Typical Waste | Recoverable |
|---|---|---|---|
| 10-25 employees | $2K-$8K/mo | 35-50% | $700-$4K/mo |
| 25-50 employees | $8K-$25K/mo | 30-45% | $2.4K-$11K/mo |
| 50-200 employees | $25K-$80K/mo | 25-40% | $6K-$32K/mo |
| 200-500 employees | $80K-$300K/mo | 20-35% | $16K-$105K/mo |
| 500+ employees | $300K-$1M+/mo | 15-30% | $45K-$300K/mo |
Red Flags
- AI spend growing faster than revenue (unsustainable)
- More than 3 overlapping tools in same category
- No usage tracking on AI SaaS licenses
- GPU instances running 24/7 for dev/test workloads
- Paying for enterprise tiers with startup-level usage
- No A/B testing between model tiers
- "Innovation budget" with no success metrics
Industry Adjustments
- SaaS/Tech: Higher AI spend acceptable (5-8%) if it's in the product
- Professional Services: Focus on billable hour impact — $1 AI spend should save $5+ in labor
- Manufacturing: AI spend should tie to defect reduction or throughput gains
- Healthcare: Compliance costs inflate spend 20-30% — factor in before judging waste
- Financial Services: Model risk management adds 15-25% overhead — legitimate cost
- Ecommerce: Measure AI spend per order — should decrease as volume scales
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