One of the first AI stack decisions teams face is whether to start with local models, hosted providers, or a mix of both.
The right answer depends less on hype and more on what the team is optimizing for: privacy, speed of evaluation, model quality, cost control, or operational simplicity.
A good default rule is simple: choose the path that helps you learn fastest without creating unnecessary operational pain.
Why teams start local
- to keep early experiments private
- to test workflows without sending data to external APIs
- to use tools such as Ollama for local AI paths
- to explore the stack before committing to provider cost
Why teams start hosted
- to evaluate model quality quickly
- to avoid operating local inference at the start
- to compare business value before optimizing infrastructure
- to move faster when local hardware is not the priority
Why mixed strategies matter
A lot of teams eventually want both: local paths for privacy or cost-sensitive work, and hosted paths for stronger models or faster evaluation. That is one reason gateway layers such as LiteLLM become useful later.
Good next read
The AI experiments article shows this decision in a broader operational context.