“Which LLM should we use?” feels like it needs one answer — but the better answer is “it depends on the task, and you shouldn’t lock to one.” Here’s how to choose well, and how a model-agnostic approach keeps you flexible. (dgm implements osFoundry, a separate company’s platform — we are not osFoundry.)

The criteria that matter

Evaluate candidate LLMs on:

  • Task fit — different models excel at different things (reasoning, speed, long context, cost).
  • Accuracy on your use case — tested on your real tasks, not just public benchmarks.
  • Cost — per-use cost varies widely.
  • Data and privacy terms — is your data used to train the model? Get it in writing.
  • Speed — latency matters for real-time uses.

Don’t lock to one model

The most important principle: don’t standardize on a single LLM. Models change constantly, and the best one for each task differs. Locking to one ties your cost and capabilities to that provider — a real risk in a market where new models arrive monthly (see bring your own model and how to avoid lock-in).

The famous model isn’t always the answer

The best-known model isn’t necessarily the cheapest, fastest, or most accurate for your task. Choose on fit and tested performance, not brand — and keep the option to switch.

Test on your real use case

Public benchmarks are a starting point, but real-world fit on your tasks is what counts. Run your actual use case and data through candidate models and compare on the metrics that matter — accuracy, relevance, cost, speed. A model-agnostic platform makes this comparison easy (and lets you switch based on the results).

How dgm helps

dgm builds on osFoundry, a model-agnostic platform — so the LLM choice stays flexible and per-task: use the best model for each job and switch freely as models evolve. dgm helps select and test models against your real use case as part of implementation ($399 assessment, $3,999/month). If you’d rather explore the platform yourself first, go straight to osFoundry; if you want the model choice handled and kept flexible, that’s where dgm comes in.