AI Model Selection Template: Choose the Right Model for Your Product
A complete AI model selection framework with scoring rubrics, evaluation criteria, cost-latency tradeoff matrices, and a ready-to-use decision template for AI PMs.
By Institute of AI PMMarch 16, 202612 min read
Choosing the wrong AI model is one of the most expensive mistakes a PM can make. Too powerful and you blow your cost budget. Too weak and users churn. This template walks you through a structured evaluation process so the selection decision is defensible, documented, and reversible.
Why Model Selection Is a PM Decision
The Four Tensions Every PM Must Balance
Capability vs. Cost
Frontier models are often 10-100x more expensive per token than smaller alternatives that may be sufficient for your use case.
Latency vs. Quality
Larger models produce better outputs but add hundreds of milliseconds of latency that users notice and abandon over.
Control vs. Convenience
Hosted API models are fast to ship but limit fine-tuning, data privacy, and cost optimization at scale.
Vendor Lock-in vs. Speed
Committing to a single provider accelerates early development but creates migration risk as pricing and capabilities shift.
Engineers optimize for technical metrics. PMs must translate business requirements — latency SLAs, unit economics, user trust, compliance constraints — into a model selection decision that the whole team can execute against.
AI Model Selection Template
Copy and customize this template for your model evaluation process:
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║ AI MODEL SELECTION DOCUMENT ║
╠══════════════════════════════════════════════════════════════════╣
DECISION OVERVIEW
────────────────────────────────────────────────────────────────────
Product / Feature: [Name of the AI feature or product]
Decision Owner: [PM Name]
Date: [YYYY-MM-DD]
Review Cadence: [Quarterly / Per major release]
Status: [ ] Evaluating [ ] Decided [ ] In Production
USE CASE DEFINITION
────────────────────────────────────────────────────────────────────
Primary Task: [e.g., text summarization, code generation, classification]
Input Format: [Text / Image / Audio / Structured data / Multimodal]
Output Format: [Text / JSON / Code / Image / Embedding]
Avg Input Tokens: [e.g., 500 tokens]
Avg Output Tokens: [e.g., 200 tokens]
Daily Request Volume: [e.g., 50,000 requests/day]
Peak QPS: [e.g., 20 requests/second]
HARD REQUIREMENTS (Must-Have)
────────────────────────────────────────────────────────────────────
[ ] Max latency (P99): [e.g., < 3 seconds]
[ ] Max cost per request: [e.g., < $0.005]
[ ] Data residency: [e.g., US-only, no data leaves VPC]
[ ] Context window minimum: [e.g., 32K tokens]
[ ] Fine-tuning required: [Yes / No]
[ ] On-premise / private: [Yes / No]
[ ] Multimodal input needed: [Yes / No]
[ ] Structured output (JSON): [Yes / No]
Candidate Evaluation Matrix
Score each candidate model from 1 (poor) to 5 (excellent) on each dimension. Multiply by the weight to get a weighted score. The model with the highest total is your recommended choice — but document your reasoning for any override.
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║ MODEL SCORING MATRIX ║
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Criteria Wt Model A Model B Model C
────────────────────────────────────────────────────────────────────
Output Quality 25% [1-5] [1-5] [1-5]
Latency (P99) 20% [1-5] [1-5] [1-5]
Cost per Request 20% [1-5] [1-5] [1-5]
Context Window 10% [1-5] [1-5] [1-5]
Fine-tune Support 10% [1-5] [1-5] [1-5]
Safety / Guardrails 10% [1-5] [1-5] [1-5]
Vendor Reliability 5% [1-5] [1-5] [1-5]
────────────────────────────────────────────────────────────────────
WEIGHTED TOTAL 100% [X.XX] [X.XX] [X.XX]
MODEL DETAILS
────────────────────────────────────────────────────────────────────
Model A
Name / Version: [e.g., GPT-4o, Claude 3.5 Sonnet, Gemini 1.5 Pro]
Provider: [OpenAI / Anthropic / Google / Meta / Mistral / Self-hosted]
Pricing: Input $[X]/1M tokens | Output $[X]/1M tokens
Context Window: [X] tokens
P50 Latency: [X] ms | P99 Latency: [X] ms
Fine-tuning: [Available / Not available]
Data Privacy: [Zero data retention / Standard logging / Self-hosted]
Notes: [Key strengths or limitations for this use case]
Model B
Name / Version: [e.g., Claude 3 Haiku, GPT-4o-mini, Llama 3.1 70B]
Provider: [Provider name]
Pricing: Input $[X]/1M tokens | Output $[X]/1M tokens
Context Window: [X] tokens
P50 Latency: [X] ms | P99 Latency: [X] ms
Fine-tuning: [Available / Not available]
Data Privacy: [Zero data retention / Standard logging / Self-hosted]
Notes: [Key strengths or limitations for this use case]
Model C
Name / Version: [e.g., Mistral 7B, Llama 3.2 3B, fine-tuned custom]
Provider: [Provider name]
Pricing: Input $[X]/1M tokens | Output $[X]/1M tokens
Context Window: [X] tokens
P50 Latency: [X] ms | P99 Latency: [X] ms
Fine-tuning: [Available / Not available]
Data Privacy: [Zero data retention / Standard logging / Self-hosted]
Notes: [Key strengths or limitations for this use case]
COMPLIANCE REQUIREMENTS
────────────────────────────────────────────────────────────────────
Regulation Applies? Model A Model B Model C
────────────────────────────────────────────────────────────────────
GDPR / Data Privacy [Y/N] [✓/✗] [✓/✗] [✓/✗]
HIPAA (if healthcare) [Y/N] [✓/✗] [✓/✗] [✓/✗]
SOC 2 Type II [Y/N] [✓/✗] [✓/✗] [✓/✗]
EU AI Act (High Risk) [Y/N] [✓/✗] [✓/✗] [✓/✗]
Data Residency (US) [Y/N] [✓/✗] [✓/✗] [✓/✗]
Zero Data Retention [Y/N] [✓/✗] [✓/✗] [✓/✗]
SAFETY PROPERTIES
────────────────────────────────────────────────────────────────────
[ ] Content moderation built-in (harmful output filtering)
[ ] Jailbreak resistance tested against adversarial prompts
[ ] PII detection and redaction available
[ ] Hallucination rate acceptable for use case risk level
[ ] Toxicity / bias benchmarks reviewed
[ ] Model card and training data documentation available
[ ] Human-in-the-loop fallback defined for high-stakes outputs
Decision Record
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║ FINAL DECISION ║
╠══════════════════════════════════════════════════════════════════╣
Selected Model: [Model name and version]
Decision Date: [YYYY-MM-DD]
Decided By: [PM Name, Engineering Lead]
Approved By: [Stakeholder / Head of Product]
Rationale (2-3 sentences):
[Why this model won. What tradeoffs were accepted. What alternatives
were ruled out and why.]
Key Tradeoffs Accepted:
• [e.g., Higher latency vs Model B in exchange for 40% cost reduction]
• [e.g., No fine-tuning support, mitigated by system prompt engineering]
• [e.g., US-only data residency limits international expansion plans]
Assumptions:
• [e.g., Usage stays below 100K requests/day for 6 months]
• [e.g., Provider pricing remains stable within 20%]
Trigger to Revisit:
• [e.g., Provider raises prices > 30%]
• [e.g., User latency complaints exceed 5% of sessions]
• [e.g., Competitor releases a significantly better model at same cost]
• [e.g., New compliance requirement emerges]
Next Review Date: [YYYY-MM-DD]
Common AI Model Selection Mistakes
Defaulting to the Frontier Model
GPT-4o and Claude Opus are impressive but often 20-50x more expensive than smaller models for tasks like classification, extraction, or short-form generation. Always benchmark a smaller model first.
Evaluating on Demos, Not Real Data
A model that wows in a playground demo may fail on your specific input distribution. Always benchmark on at least 100-200 representative production examples before deciding.
Ignoring Vendor Stability
Models get deprecated, pricing changes, and APIs break without notice. Factor in migration cost when evaluating smaller or newer providers against established ones.
Not Defining a Fallback
Every AI product needs a fallback for when the primary model is unavailable or returns unacceptable output. Define this in the architecture before launch.
Selecting Without Compliance Sign-off
Legal and security teams are often surprised late in the process. Pull them in during model evaluation, not post-launch.
Treating the Decision as Permanent
The AI model landscape changes quarterly. Document your decision with explicit triggers to revisit so you can upgrade or swap without it becoming a political battle.
Model Tier Reference (2026)
Use this as a starting point for your candidate list. Prices and capabilities change frequently — always verify before finalizing your evaluation.
Requires more prompt engineering; weaker on open-ended tasks
Model Selection Readiness Checklist
Use case is clearly defined with input/output format specified
Hard requirements documented (latency, cost, compliance)
At least 3 candidate models shortlisted
Evaluation dataset of 100+ real examples prepared
Latency benchmarked under realistic load conditions
Monthly cost projection calculated at target volume
Quality benchmarked using task-specific metrics
Safety and hallucination rate assessed
Compliance requirements reviewed with legal/security
Fallback model or degradation strategy defined
Vendor lock-in risk assessed and mitigation planned
Decision record written with explicit revisit triggers
Learn to Apply This in a Real AI Product
The AI Product Management Masterclass walks you through model selection, PRDs, metrics, and every other decision an AI PM makes — with live instruction and real products.
