AI features can be expensive surprises. Unlike traditional software where costs are mostly fixed infrastructure, AI costs scale with usage, vary by model, and include hidden expenses like fine-tuning, evaluation, and safety testing. This template helps you estimate costs accurately before committing resources and track them throughout development.
Why AI Cost Estimation is Different
Traditional vs AI Cost Structures
Traditional Software:
- Fixed infrastructure costs
- Predictable compute scaling
- Linear cost-to-user ratio
- One-time development cost
AI Features:
- Variable per-request costs
- Non-linear scaling patterns
- Model choice impacts cost 100x
- Ongoing training & evaluation
AI Cost Categories Framework
AI costs fall into five main categories. Use this framework to ensure you don't miss hidden expenses:
┌─────────────────────────────────────────────────────────────────┐ │ AI COST CATEGORIES FRAMEWORK │ ├─────────────────────────────────────────────────────────────────┤ │ │ │ 1. INFERENCE COSTS (Per-Request) │ │ ├── API calls to LLM providers │ │ ├── Token usage (input + output) │ │ ├── Embedding generation │ │ └── Model hosting (if self-hosted) │ │ │ │ 2. INFRASTRUCTURE COSTS (Fixed + Variable) │ │ ├── Vector databases │ │ ├── GPU compute (training/fine-tuning) │ │ ├── Storage (models, embeddings, logs) │ │ └── CDN/edge deployment │ │ │ │ 3. DATA COSTS (Often Overlooked) │ │ ├── Data acquisition/licensing │ │ ├── Data labeling & annotation │ │ ├── Data cleaning & preprocessing │ │ └── Synthetic data generation │ │ │ │ 4. DEVELOPMENT COSTS (One-Time + Ongoing) │ │ ├── Prompt engineering & testing │ │ ├── Fine-tuning experiments │ │ ├── Evaluation & benchmarking │ │ └── Safety & red-teaming │ │ │ │ 5. OPERATIONAL COSTS (Ongoing) │ │ ├── Monitoring & observability │ │ ├── Human review & moderation │ │ ├── Model updates & retraining │ │ └── Incident response │ │ │ └─────────────────────────────────────────────────────────────────┘
Copy-Paste Cost Estimation Template
Copy this template into your planning documents. Fill in each section for comprehensive cost visibility:
╔═══════════════════════════════════════════════════════════════════╗ ║ AI FEATURE COST ESTIMATION ║ ║ Feature: [Feature Name] ║ ║ Date: [YYYY-MM-DD] ║ ║ Prepared by: [PM Name] ║ ╠═══════════════════════════════════════════════════════════════════╣ EXECUTIVE SUMMARY ───────────────── Estimated Monthly Cost (at launch): $________ Estimated Monthly Cost (at scale): $________ Cost per 1,000 users: $________ Break-even usage: ________ requests/month ═══════════════════════════════════════════════════════════════════ 1. INFERENCE COSTS ───────────────── Primary Model: [e.g., GPT-4, Claude 3, Gemini Pro] Fallback Model: [e.g., GPT-3.5, Claude Instant] ┌────────────────────┬───────────┬───────────┬─────────────────────┐ │ Cost Component │ Unit Cost │ Est. Vol. │ Monthly Cost │ ├────────────────────┼───────────┼───────────┼─────────────────────┤ │ Input tokens │ $__/1M │ ____M │ $________ │ │ Output tokens │ $__/1M │ ____M │ $________ │ │ Embeddings │ $__/1M │ ____M │ $________ │ │ Image generation │ $__/image │ ____ │ $________ │ │ Voice/audio │ $__/min │ ____ min │ $________ │ ├────────────────────┼───────────┼───────────┼─────────────────────┤ │ SUBTOTAL │ │ │ $________ │ └────────────────────┴───────────┴───────────┴─────────────────────┘ Token Estimation Notes: - Avg input tokens per request: ________ - Avg output tokens per request: ________ - Requests per user per day: ________ - Expected daily active users: ________ ═══════════════════════════════════════════════════════════════════ 2. INFRASTRUCTURE COSTS ─────────────────────── ┌────────────────────┬───────────┬───────────┬─────────────────────┐ │ Component │ Provider │ Tier/Size │ Monthly Cost │ ├────────────────────┼───────────┼───────────┼─────────────────────┤ │ Vector database │ ________ │ ________ │ $________ │ │ Cache layer │ ________ │ ________ │ $________ │ │ Object storage │ ________ │ ____ GB │ $________ │ │ Compute (API) │ ________ │ ________ │ $________ │ │ GPU (if needed) │ ________ │ ________ │ $________ │ │ Logging/monitoring │ ________ │ ________ │ $________ │ ├────────────────────┼───────────┼───────────┼─────────────────────┤ │ SUBTOTAL │ │ │ $________ │ └────────────────────┴───────────┴───────────┴─────────────────────┘ ═══════════════════════════════════════════════════════════════════ 3. DATA COSTS ───────────── ┌────────────────────┬───────────┬───────────┬─────────────────────┐ │ Data Need │ Source │ Volume │ Cost (One-time) │ ├────────────────────┼───────────┼───────────┼─────────────────────┤ │ Training data │ ________ │ ________ │ $________ │ │ Evaluation dataset │ ________ │ ________ │ $________ │ │ Data labeling │ ________ │ __ items │ $________ │ │ Data licensing │ ________ │ ________ │ $________ /month │ ├────────────────────┼───────────┼───────────┼─────────────────────┤ │ SUBTOTAL │ │ │ $________ │ └────────────────────┴───────────┴───────────┴─────────────────────┘ ═══════════════════════════════════════════════════════════════════ 4. DEVELOPMENT COSTS (One-Time) ─────────────────────────────── ┌────────────────────┬───────────┬───────────┬─────────────────────┐ │ Activity │ Est Hours │ Rate │ Cost │ ├────────────────────┼───────────┼───────────┼─────────────────────┤ │ Prompt engineering │ ____ │ $____/hr │ $________ │ │ Fine-tuning │ ____ │ $____/hr │ $________ │ │ Evaluation setup │ ____ │ $____/hr │ $________ │ │ Safety testing │ ____ │ $____/hr │ $________ │ │ Integration work │ ____ │ $____/hr │ $________ │ ├────────────────────┼───────────┼───────────┼─────────────────────┤ │ SUBTOTAL │ │ │ $________ │ └────────────────────┴───────────┴───────────┴─────────────────────┘ Fine-tuning compute costs (if applicable): - Training runs: ____ x $________ = $________ - GPU hours: ____ hrs @ $____/hr = $________ ═══════════════════════════════════════════════════════════════════ 5. OPERATIONAL COSTS (Monthly) ────────────────────────────── ┌────────────────────┬───────────┬───────────┬─────────────────────┐ │ Activity │ Frequency │ Cost/Unit │ Monthly Cost │ ├────────────────────┼───────────┼───────────┼─────────────────────┤ │ Human review │ ____% │ $____/rev │ $________ │ │ Model retraining │ ____/mo │ $____/run │ $________ │ │ Eval runs │ ____/mo │ $____/run │ $________ │ │ Incident buffer │ ____/mo │ $____ │ $________ │ ├────────────────────┼───────────┼───────────┼─────────────────────┤ │ SUBTOTAL │ │ │ $________ │ └────────────────────┴───────────┴───────────┴─────────────────────┘ ═══════════════════════════════════════════════════════════════════ TOTAL COST SUMMARY ────────────────── ┌────────────────────────────────┬─────────────────────────────────┐ │ Category │ Cost │ ├────────────────────────────────┼─────────────────────────────────┤ │ One-Time Costs │ │ │ Development │ $________ │ │ Data (one-time) │ $________ │ │ ───────────────────────── │ ───────── │ │ One-Time Subtotal │ $________ │ ├────────────────────────────────┼─────────────────────────────────┤ │ Monthly Costs │ │ │ Inference │ $________ │ │ Infrastructure │ $________ │ │ Data (recurring) │ $________ │ │ Operations │ $________ │ │ ───────────────────────── │ ───────── │ │ Monthly Subtotal │ $________ │ ├────────────────────────────────┼─────────────────────────────────┤ │ Year 1 Total │ $________ + (12 × $________) │ │ │ = $________ │ └────────────────────────────────┴─────────────────────────────────┘ ╚═══════════════════════════════════════════════════════════════════╝
LLM Pricing Quick Reference (Dec 2025)
Note: Prices change frequently. Always verify current pricing with providers.
| Model | Input ($/1M) | Output ($/1M) | Context |
|---|---|---|---|
| GPT-4 Turbo | $10.00 | $30.00 | 128K |
| GPT-4o | $2.50 | $10.00 | 128K |
| GPT-4o-mini | $0.15 | $0.60 | 128K |
| Claude 3.5 Sonnet | $3.00 | $15.00 | 200K |
| Claude 3 Haiku | $0.25 | $1.25 | 200K |
| Gemini 1.5 Pro | $1.25 | $5.00 | 1M |
| Gemini 1.5 Flash | $0.075 | $0.30 | 1M |
Scaling Cost Projections
Use this template to project costs as usage grows. AI costs often don't scale linearly:
COST SCALING PROJECTIONS ════════════════════════ Assumptions: - Requests per user per day: ____ - Avg tokens per request: ____ input, ____ output - Cost reduction from caching: ____% - Bulk discount threshold: ________ requests/month ┌──────────────┬──────────┬────────────┬────────────┬────────────┐ │ Metric │ Launch │ Month 3 │ Month 6 │ Month 12 │ ├──────────────┼──────────┼────────────┼────────────┼────────────┤ │ MAU │ ________ │ ________ │ ________ │ ________ │ │ Requests/day │ ________ │ ________ │ ________ │ ________ │ │ Tokens/month │ ________M│ ________M │ ________M │ ________M │ ├──────────────┼──────────┼────────────┼────────────┼────────────┤ │ Inference │ $_______ │ $_______ │ $_______ │ $_______ │ │ Infra │ $_______ │ $_______ │ $_______ │ $_______ │ │ Operations │ $_______ │ $_______ │ $_______ │ $_______ │ ├──────────────┼──────────┼────────────┼────────────┼────────────┤ │ TOTAL/month │ $_______ │ $_______ │ $_______ │ $_______ │ │ Per user │ $_______ │ $_______ │ $_______ │ $_______ │ └──────────────┴──────────┴────────────┴────────────┴────────────┘ Scaling Optimizations Planned: Month 3: ________________________________________________ Month 6: ________________________________________________ Month 12: _______________________________________________
Cost Optimization Strategies
Quick Wins (Week 1)
- Prompt optimization: Shorter prompts = fewer input tokens
- Response length limits: Set max_tokens appropriately
- Caching: Cache common responses (30-50% savings)
- Model routing: Use cheaper models for simple tasks
Medium-Term (Month 1-3)
- Fine-tuning: Smaller fine-tuned model vs large general model
- Embedding compression: Reduce vector dimensions
- Batch processing: Group requests where latency allows
- Committed use discounts: Lock in volume pricing
Common Cost Estimation Mistakes
- Forgetting output tokens: Output often costs 2-4x more than input. Long AI responses are expensive.
- Ignoring retries: Rate limits, timeouts, and quality retries can add 10-30% to costs.
- Underestimating dev/test: Prompt iteration and testing can cost $1-5K+ during development.
- Missing evaluation costs: Running eval suites on every deploy adds up quickly.
- No buffer for incidents: AI failures often require expensive fallbacks or human intervention.
- Assuming linear scaling: Volume discounts exist, but so do tier cliffs and rate limits.
Budget Approval Request Template
Use this template when requesting budget approval from finance or leadership:
AI FEATURE BUDGET REQUEST ═════════════════════════ Feature: [Name] Requested by: [PM Name] Date: [YYYY-MM-DD] Priority: [P0/P1/P2] BUDGET REQUEST ────────────── One-time investment: $________ Monthly run rate: $________ Year 1 total: $________ BUSINESS JUSTIFICATION ────────────────────── Expected impact: - [Metric 1]: +___% improvement = $________ revenue/savings - [Metric 2]: +___% improvement = $________ revenue/savings - [Metric 3]: +___% improvement = $________ revenue/savings ROI Calculation: - Year 1 investment: $________ - Year 1 expected return: $________ - ROI: _____% - Payback period: ____ months COST CONTROLS ───────────── □ Hard spending cap: $________ /month □ Alert threshold: $________ (70% of cap) □ Auto-scaling limits: [Defined/Not defined] □ Fallback to cheaper model at: $________ /month □ Kill switch criteria: ________________________________ APPROVAL ──────── □ Finance review: __________ (Date: __________) □ Eng lead review: __________ (Date: __________) □ Final approval: __________ (Date: __________)
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