How-To · 1 minute read
How to Choose an AI Model
To choose an AI model, match five factors to your task: capability (is it good enough for the job?), cost per request, latency, privacy and data control, and whether you can self-host. Don't default to the biggest or highest-benchmark model—evaluate shortlisted models on your own data and use case, since real performance on your task should decide. Often the best choice is the smallest, cheapest model that meets your quality bar, with larger models reserved for the hardest tasks. Many systems use several models for different jobs.
Choosing an AI model isn't about picking the biggest. Here's how to match capability, cost, and privacy to your task—and test on your own data.
The five factors
| Factor | Question |
|---|---|
| Capability | Good enough for the job? |
| Cost | Per-request economics |
| Latency | Fast enough? |
| Privacy/control | Data can leave? |
| Self-hosting | Needed? |
Match these to your task—see open-source vs proprietary LLMs and cloud vs on-premise AI.
Don't default to the biggest
The most capable model is often overkill—slower and pricier than needed. For well-defined tasks, a smaller model frequently meets the bar at a fraction of the cost and latency. Reserve big models for the hardest tasks.
Evaluate on your own data
Use benchmarks only to shortlist, then evaluate candidates on your data and metrics. Real performance on your task—not a leaderboard—should decide, per how to measure AI success.
Consider privacy and control
If data can't leave your environment, favor self-hosted or private models—the data residency constraint.
Often the answer is several models
Many systems use different models for different jobs—a strong model for hard tasks, a cheap one for simple, high-volume, or sensitive work.
Why FISTA
FISTA Solutions selects models by evaluation on your task—right-sized for capability, cost, and privacy—through AI enablement, backed by 150+ projects across 12+ countries.
Choosing the right model? Talk to FISTA.
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Straightforward guidance for evaluating scope, fit, and the next step.
01How do I choose an AI model?
Match capability, cost, latency, privacy, and control to your task, then evaluate shortlisted models on your own data. Choose the smallest model that meets your quality bar, reserving larger ones for the hardest tasks.
02Should I always use the most capable model?
No. The most capable model is often overkill—slower and more expensive than needed. For well-defined tasks, a smaller or cheaper model frequently meets the quality bar at a fraction of the cost and latency.
03How do I compare AI models fairly?
Use benchmarks only to shortlist, then evaluate candidates on your own use case with your own data and metrics. Real performance on your task—not leaderboard scores—should decide the choice.
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