Why this matters

Poor model decisions result in:

• 3–12 months of rework
• Escalating cloud spend
• Latency failures in production
• Compliance and governance exposure
• Erosion of executive trust

Disciplined selection leads to:

• Faster product iteration
• Controlled operating margins
• Sustainable AI scaling
• Reduced architectural churn

The 6-step model selection framework

1. Define the business outcome. Start with a measurable KPI—select 1 primary metric plus guardrails (cost, latency, safety).
2. Classify the task type. Match the problem to the right family (classic ML, RAG, lightweight LLM, multimodal, etc.).
3. Map constraints. Identify non-negotiables (latency, cost ceilings, privacy/residency, interpretability, licensing, capability).
4. Build a trade-off matrix. Score shortlisted models on quality, speed, cost, privacy, operability, and compliance using weighted criteria.
5. Run the cheap evaluation loop. Baseline → rapid prototype → offline eval → small A/B (1–5% traffic) to generate signal before scaling.
6. Decide and plan for change. Architect for adaptability; monitor KPI/cost/drift; re-evaluate every 6–12 months.