How to Choose an AI Model for Production
A practical framework for comparing frontier, small, open-weight, and commercial AI models by quality, latency, privacy, cost, and operational fit.
Start with the workload
The best model is not automatically the largest model. Begin with the task: classification, extraction, retrieval, coding, multimodal understanding, long-form generation, or autonomous tool use. Each workload creates a different quality and latency threshold.
- Define acceptable output quality with real examples.
- Measure the maximum response time users will tolerate.
- Identify whether requests contain regulated or sensitive data.
- Estimate normal and peak token volume before comparing prices.
Compare capability and reliability
Public benchmarks are useful for orientation, but production evaluation should use your own inputs and pass criteria. Test failure modes, structured-output consistency, refusal behavior, context retention, and tool-call accuracy—not only average answer quality.
Decide between hosted and open models
Hosted frontier APIs reduce operational work and provide rapid access to new capabilities. Open-weight models offer more control over privacy, deployment location, fine-tuning, and unit economics at sustained scale. Hybrid stacks often route difficult requests to frontier systems and predictable tasks to smaller models.
Calculate the full production cost
Token price is only one component. Include retries, output length, caching, evaluation, observability, moderation, infrastructure, and engineering time. A cheaper model with inconsistent output can cost more after recovery logic and human review.