What Ollama is for
Running local models for private assistants, development experiments, RAG prototypes, and secure document AI patterns. Use it when the job is specific enough to test against a real workflow, not as a generic platform purchase.
- local AI
- RAG prototypes
- private assistants
- model testing
How to use Ollama
Start with one repeatable task, one owner, and one success measure. The useful test is whether Ollama improves a workflow the team already performs.
- Name the workflow, input, expected output, and human approval point.
- Run a small pilot with Ollama using non-sensitive or approved data first.
- Compare output quality, time saved, error rate, and support burden against the manual baseline.
- Write the operating rule before adding more users, more data, or automation permissions.
Implementation workflow
Ollama belongs in the stack only when it has a clear place in the work sequence.
- Stage fit: Build, Govern.
- Primary users: engineers, technical founders, privacy-conscious teams.
- Deployment model: Local model runtime.
- Pricing check: Open-source local runtime; hosting and hardware costs depend on the deployment.
Governance checklist
Before Ollama touches production work, make the operating boundary visible to the team.
- Classify the data allowed in the tool and the data that must stay out.
- Limit credentials, connectors, and automation permissions to the pilot workflow.
- Keep a review queue for important outputs and actions.
- Log the decision, owner, cost expectation, and rollback path.
When to use another option
Do not keep Ollama just because it is capable. Use another option when the workflow is better served by lower-risk tooling, existing systems, or a simpler manual process.
- hardware and model quality matter
- local does not remove the need for governance
- Choose a different tool when the team cannot name the owner, review point, or success measure.
