Azure AI Foundry: Choosing the Right Model Without Analysis Paralysis

Azure AI Foundry's model catalog keeps growing — GPT-4o, Llama, Mistral, Phi, and hundreds more. Choice is good. Paralysis is not. Here is how to decide fast.

The Three-Question Filter

• Data sensitivity? — If data cannot leave your tenant, use deployed models (GPT-4o, Llama on managed compute). This eliminates serverless endpoints. 60% of options gone.
• Latency budget? — Real-time customer-facing: sub-2 seconds. Batch processing: 30+ seconds acceptable. This determines model size class.
• Task complexity? — Classification and extraction work great with smaller models (Phi, Mistral 7B). Complex reasoning needs frontier models (GPT-4o).

The Evaluation Sprint

Top 3 candidates. 50 test cases from your real data. 3-day evaluation. Score on accuracy, latency, cost per 1K requests. Ship the winner.

Design for Swappability

The model you choose today will not be the model you use in 6 months. Abstract the LLM call behind an interface. When a better model drops, switching becomes a configuration change — not a rewrite.