Providers and models
Where AI models come from, who enables them and how they reach an Agent.
A Provider is an account with an AI model service: AUMO, OpenAI, Bedrock and the like. A Model is a concrete option within a provider, selectable in an Agent's form.
The two live at different levels on purpose: the Provider belongs to the Organization, the Model is enabled per Workspace.
Providers
Providers live under Organization › Providers and are where the company's AI credentials are stored. Registering a provider is what gives the platform the right to call that service.
Only the Superadmin role opens that screen. This is deliberate: it is where the secret lives.
The AUMO provider is the special case. Instead of bringing your own account, AUMO provides the access, with approval and quotas negotiated between your organization and the platform.
Models
Models live under Workspace › Settings › Models. That is where you decide which options, out of everything the organization's providers offer, show up for whoever creates Agents in that workspace.
Each model carries the data that determines its behavior and its cost:
- Context window: how much text fits in one call, adding up the prompt, history and retrieved passages.
- Maximum output tokens: the ceiling for one answer.
- Input and output price: the cost per token, used to calculate consumption and quotas.
A workspace with no model enabled won't let you create an Agent: the Model field is required and would be empty.
The path to an answer
The full chain has four links, and it is worth knowing where each one breaks:
- The Organization registers the Provider, with the credential.
- The Workspace enables the Models from that provider it wants to offer.
- The Agent picks one of those models.
- Each Chat message consumes tokens from that model, counted against the workspace.
If a model disappears from the list of an existing Agent, the broken link is almost always number 2: someone disabled it in the workspace.
Choosing a model
Three criteria are usually enough:
- Context size: Agents with many Collections or long conversations need larger windows.
- Cost: larger models charge more per token, and the Dashboard shows the effect at the end of the month.
- Behavior: the same prompt yields different answers across models. It is worth testing the same Agent on two before publishing.
Starting with the cheapest model that handles the task is the most predictable path. Switching later means editing one field.
Next steps
- See how the consumption of these models is capped in Quotas and consumption.
- Understand where the model fits in building the answer in Agents and SuperAgents.