Self-Hosted Models for Enterprise AI Agents: What Stays Inside Your Walls
technology
A closer look at the self-hosted model option: what it keeps on your infrastructure and when it makes sense.
For some organizations, the question is not which model is best but where the model runs. A self-hosted option answers that directly for AI agents.
What self-hosting keeps in-house
With a self-hosted model, data and model weights stay inside your own infrastructure. Prompts and company knowledge are not sent to an outside model provider.
When it makes sense
- Regulated or contractually restricted data
- Internal policies that require on-premises processing
- Teams that want a clear answer for security review
What stays the same
Governance does not change with the model. Roles, scopes, and approvals still decide what every agent can see and do. Company memory is still scoped by team, project, and role.
Mixing model choices
Model policy is yours to set. You might use a self-hosted model for sensitive projects and another for general work, then swap as better models arrive. Your memory, rules, and agents stay put.
Questions to ask any vendor
- Where do data and weights live?
- Can we change models without rebuilding?
- Who can see what an agent reads?
Read more on trust and controls or Constella for enterprise.