The Decision Framework I Use to Select AI Models for Enterprise Clients

"Which model should we use?"

I get asked this question at least once a day. And the honest answer is: it depends. But that's not helpful, so let me give you the framework I actually use.

The Four Dimensions of Model Selection

Every model decision comes down to four factors:

1. Capability Match

Does the model have the skills your task requires?

2. Cost Economics

What's the total cost of ownership?

*Based on 100k requests/month, 1k tokens in, 500 tokens out

3. Operational Requirements

Can you actually run this in production?

• •Latency requirements - Real-time vs. batch
• •Uptime SLAs - 99.9% vs. best effort
• •Data residency - Where can data go?
• •Compliance - SOC 2, HIPAA, etc.

4. Strategic Fit

Longer-term considerations:

• •Vendor lock-in risk - How portable is your implementation?
• •Model roadmap - Where is the provider heading?
• •Support quality - Who helps when things break?

The Selection Process

Here's my step-by-step approach:

Step 1: Define Success Criteria

Before looking at any model, write down your must-haves, nice-to-haves, and budget constraints.

Step 2: Create a Test Suite

Build a representative evaluation set with examples from each category of tasks you need to handle.

Step 3: Run Comparative Evaluation

This is where ModelMix shines. Run the same inputs through multiple models:

Step 4: Score and Decide

Use a weighted scoring matrix to make the final decision.

Common Mistakes to Avoid

Mistake 1: Benchmark Worship

Public benchmarks are directionally useful but don't reflect your specific use case. Always test on your own data.

Mistake 2: Ignoring Variance

A model that scores 90% average but has high variance might be worse than one that scores 85% consistently.

Mistake 3: Optimizing for Today

The model landscape changes fast. Build for flexibility by abstracting the model layer.

Video: Model Selection in Practice

*Watch me evaluate models for a real client use case*

Quick Decision Guide

When you need a fast answer:

But remember: always validate for your specific use case.

The ModelMix Workflow

Here's how I use ModelMix for model selection:

1. Input my evaluation prompts - One by one or in batch
2. Select candidate models - Usually 3-4 finalists
3. Compare outputs side-by-side - Quality, style, accuracy
4. Export results - For documentation and stakeholder review
5. Iterate - Refine prompts, retest edge cases

This process takes hours, not weeks. And the decision is data-driven, not gut-driven.

*What factors do you prioritize in model selection? Let me know on Twitter.*