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waste, measure ROI, and right-size your tool stack.\n\n## When to Use\n- Quarterly AI budget reviews\n- Before renewing AI tool subscriptions\n- When AI spend exceeds 3% of revenue without clear ROI\n- Evaluating build vs buy decisions for AI capabilities\n\n## The Framework\n\n### Step 1: Inventory Every AI Line Item\nMap all AI spending across these categories:\n\n| Category | Examples | Typical Waste |\n|----------|----------|---------------|\n| **Foundation Models** | OpenAI, Anthropic, Google API keys | 40-60% (unused capacity, wrong model tier) |\n| **SaaS with AI** | Salesforce Einstein, HubSpot AI, Notion AI | 30-50% (features enabled but unused) |\n| **Custom Development** | Internal ML teams, fine-tuning, RAG pipelines | 25-45% (duplicate efforts, over-engineering) |\n| **Infrastructure** | GPU instances, vector DBs, embedding compute | 35-55% (over-provisioned, always-on dev instances) |\n| **Data & Training** | Labeling services, training data, synthetic data | 20-40% 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suffices? Claude Opus where Sonnet works?\n   - Rule: Use the cheapest model that meets quality threshold\n   - Test: Run 100 production queries through cheaper model, measure quality delta\n\n2. **Caching**: Are you re-processing identical or similar queries?\n   - Semantic cache can cut 20-40% of API calls\n   - Exact-match cache catches another 5-15%\n\n3. **Batch vs Real-time**: Which requests actually need sub-second response?\n   - Batch processing is 50% cheaper on most providers\n   - Queue non-urgent requests for batch windows\n\n4. **Token Optimization**:\n   - Trim system prompts (every token costs money at scale)\n   - Use structured output to reduce response tokens\n   - Implement max_tokens limits per use case\n\n### Step 4: Vendor Consolidation\n\nMap overlapping capabilities:\n\n```\nCurrent State → Target State\n─────────────────────────────────────────\nChatGPT Teams + Claude Pro + Gemini → Pick ONE primary + ONE backup\nJasper + Copy.ai + ChatGPT for content → Single content tool\n3 different vector databases → Consolidate to 1\nInternal embeddings + OpenAI embeddings → Standardize on one\n```\n\n**Consolidation savings**: Typically 25-40% of total AI spend.\n\n### Step 5: Build the Audit Report\n\n```\nAI SPEND AUDIT — [Company Name] — [Quarter/Year]\n━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\n\nTotal AI Spend: $___/month ($___/year)\nAI Spend as % Revenue: ___%\nIndustry Benchmark: 2-5% (early adopter) / 0.5-2% (mainstream)\n\nWASTE IDENTIFIED\n├── Unused licenses: $___/month\n├── Over-provisioned infra: $___/month\n├── Model tier downgrades: $___/month\n├── Vendor consolidation: $___/month\n└── TOTAL RECOVERABLE: $___/month ($___/year)\n\nACTIONS\n┌─ CUT (Score 0-30): [list tools]\n├─ REVIEW (Score 31-50): [list tools]\n├─ OPTIMIZE (Score 51-70): [list tools]\n└─ KEEP (Score 71-100): [list tools]\n\n90-DAY PLAN\nWeek 1-2: Cancel CUT items, begin REVIEW negotiations\nWeek 3-4: Implement model downgrades and caching\nWeek 5-8: Vendor consolidation migration\nWeek 9-12: Measure savings, establish ongoing monitoring\n```\n\n## Company Size Benchmarks (2026)\n\n| Company Size | Typical AI Spend | Typical Waste | Recoverable |\n|-------------|-----------------|---------------|-------------|\n| 10-25 employees | $2K-$8K/mo | 35-50% | $700-$4K/mo |\n| 25-50 employees | $8K-$25K/mo | 30-45% | $2.4K-$11K/mo |\n| 50-200 employees | $25K-$80K/mo | 25-40% | $6K-$32K/mo |\n| 200-500 employees | $80K-$300K/mo | 20-35% | $16K-$105K/mo |\n| 500+ employees | $300K-$1M+/mo | 15-30% | $45K-$300K/mo |\n\n## Red Flags\n\n- AI spend growing faster than revenue (unsustainable)\n- More than 3 overlapping tools in same category\n- No usage tracking on AI SaaS licenses\n- GPU instances running 24/7 for dev/test workloads\n- Paying for enterprise tiers with startup-level usage\n- No A/B testing between model tiers\n- \"Innovation budget\" with no success metrics\n\n## Industry Adjustments\n\n- **SaaS/Tech**: Higher AI spend acceptable (5-8%) if it's in the product\n- **Professional Services**: Focus on billable hour impact — $1 AI spend should save $5+ in labor\n- **Manufacturing**: AI spend should tie to defect reduction or throughput gains\n- **Healthcare**: Compliance costs inflate spend 20-30% — factor in before judging waste\n- **Financial Services**: Model risk management adds 15-25% overhead — legitimate cost\n- **Ecommerce**: Measure AI spend per order — should decrease as volume scales\n\n---\n\n*Built by [AfrexAI](https://afrexai-cto.github.io/context-packs/) — AI operations context packs for business teams. Run the [AI Revenue Calculator](https://afrexai-cto.github.io/ai-revenue-calculator/) to find your biggest automation opportunities.*\n","readmeExcerpt":"AI Spend Audit Audit your company's AI spending — find waste, measure ROI, and right-size your tool stack. 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