Deployment

Deployment models and data boundaries

Context deploys four ways: managed, in your VPC, on-premises, or air-gapped. In every mode the entire platform deploys, not just model inference — the architecture, data boundaries, and operating responsibilities for each are documented below.

Deployment scope

The unit of deployment is the whole platform — the control plane that schedules and governs work, the isolated environments where agents execute, the context filesystem agents read, and the run history that records what happened. Context deploys the entire platform, not just inference: whichever perimeter you choose, all of it deploys there together.

You pick the perimeter; model endpoints, connectors, the update process, and the support model are configured for that architecture, and the resulting data-flow boundaries are documented during implementation.

Deployment model

Managed

In a managed deployment Context stands up and operates the platform — control plane, execution, and run history — for your workspace. Your team connects through SSO, defines runbooks, and governs policy; Context handles infrastructure operations, updates, and support.

Run traces stay inside the deployment. What does cross a boundary is model inference when a workflow uses a commercial endpoint. That provider's retention and training settings are documented and verified with you during implementation, and endpoints can be restricted to the providers your policy allows.

Perimeter
A deployment Context operates for your workspace
Operations
Context operates the platform; you govern policy and access
Run traces
Stay inside your deployment
Model endpoints
Commercial or open-weight endpoints, configured per policy

Deployment model

Your VPC

In a VPC deployment the entire platform runs inside a cloud account you own on AWS, Azure, or GCP — not just model inference behind an API. The control plane, execution environments, the context filesystem, and run history all live under your cloud controls: your network rules, your monitoring.

Sensitive data is not moved into someone else's cloud. Model inference can use private endpoints so traffic does not leave your network, and where a workflow is configured to call an external provider, that flow is documented during implementation.

Perimeter
Your own AWS, Azure, or GCP account
Operations
Runs under your cloud controls; operations shared per the deployment design
Run traces
Stay inside your VPC
Model endpoints
Private endpoints; traffic need not leave your network

Deployment model

On-premises

On-premises, Context runs on hardware you rack and operate, inside the perimeter your security team already defends. The update process and the support model are configured for this architecture during implementation — how releases arrive, how they are verified, and what Context can see when you ask for help are all decided with your operations team.

The data-flow boundary is the one you already enforce at the network edge. Which model endpoints are reachable from inside — and whether any are — is a configuration decision documented during implementation.

Perimeter
Your data center, on infrastructure you operate
Operations
You operate the infrastructure; updates and support configured for it
Run traces
Stay inside your perimeter
Model endpoints
Endpoints available inside your perimeter, per the deployment design

Deployment model

Air-gapped

Air-gapped deployment exists for environments designed for disconnected operation. The entire platform — control plane, execution, the context filesystem, and run history — deploys inside the boundary. Updates and support follow the process designed for disconnected operation during implementation.

Model inference runs on open-weight models served inside the boundary, because a disconnected environment cannot call a commercial API. The trade for total isolation is the model catalog available inside it.

Perimeter
A disconnected environment; the entire platform inside the boundary
Operations
You operate the environment; updates follow the disconnected process
Run traces
Never leave the boundary — run history deploys inside it
Model endpoints
Open-weight models served inside the boundary

Mode comparison

The same platform in each column — what changes is where the perimeter sits, who operates the infrastructure inside it, and which model endpoints are reachable from it.

ManagedYour VPCOn-premisesAir-gapped
PerimeterA deployment Context operates for your workspaceYour own AWS, Azure, or GCP accountYour data center, on infrastructure you operateA disconnected environment; the entire platform inside the boundary
Who operates whatContext operates the platform; you govern policy and accessRuns under your cloud controls; operations shared per the deployment designYou operate the infrastructure; updates and support configured for itYou operate the environment; updates follow the disconnected process
Run tracesStay inside your deploymentStay inside your VPCStay inside your perimeterNever leave the boundary — run history deploys inside it
Model endpointsCommercial or open-weight endpoints, configured per policyPrivate endpoints; traffic need not leave your networkEndpoints available inside your perimeter, per the deployment designOpen-weight models served inside the boundary

Controls in every mode

The control set is the same in every mode. Engine runs each agent in an isolated environment and gives it only the tools configured for its runbook. Read access, write access, and human approval are configured separately, so a workflow can be broad in what it reads and narrow in what it may change.

The execution environments enforce network policy: default-deny egress with allowlists per workflow, so an agent reaches exactly what its task requires and nothing else.

In every mode: agents get identity from your IdP, every action is authorized against policy and recorded in an append-only trail, and credentials are brokered to connectors at runtime — never written into runbooks or prompts.

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