"Which model should we use?" is the question every AI project starts with — and the one teams most often answer by habit rather than fit. GPT, Claude, and open-source models like Llama and Mistral each win on different axes. Here is the framework we use to choose.
The four trade-offs that actually matter
1. Quality on your task
Benchmarks are a starting point, not a verdict. A model that tops a leaderboard can still lose on your prompts. We build a small evaluation set from real examples and score candidates on it before committing — the only benchmark that counts is your own.
2. Cost and latency
Hosted frontier models (GPT, Claude) bill per token and can get expensive at scale, but need no infrastructure. Open-source models are free to license, yet you pay for the GPUs and engineering to run them. High volume flips the maths in favour of self-hosting; low or spiky volume favours a hosted API.
3. Privacy and control
- Hosted APIs — fastest to ship, but your data leaves your infrastructure. Fine for many use cases, a blocker for regulated or highly sensitive data.
- Open-source, self-hosted — the model runs in your cloud or on-prem, so data never leaves. Essential for healthcare, finance, and strict-compliance workloads.
4. Lock-in and portability
Designing against a thin abstraction layer — rather than one vendor's SDK — lets you swap models as prices drop and capabilities change. We treat the model as a replaceable component, not a foundation.
A quick decision guide
- Ship fast, general task, no data-residency constraint → a hosted frontier model (GPT or Claude).
- Long documents, careful reasoning, safety-sensitive tone → Claude tends to shine.
- Sensitive data, high volume, or you need full control → an open-source model self-hosted.
- Narrow, repetitive task → a smaller fine-tuned model often beats a big general one on cost and speed.
There is rarely one "best" model — only the best fit for your quality bar, budget, and compliance needs. The right answer often changes as you scale.
How FlexGrew helps you decide
We run a short evaluation across candidate models using your real data, weigh the four trade-offs against your constraints, and design the system so the model can be swapped later without a rewrite. You get a clear recommendation with the reasoning behind it — and an architecture that won't trap you.
Not sure which model fits your product? Book a consultation and we'll benchmark the options against your use case.
