Business Model

DevAgents OS adapts to your team's reality, regardless of technical maturity or security requirements. The platform is available in three deployment models so every organization can find the fit that matches their context.

Managed Workspace

DevAgents OS runs as a managed workspace where teams activate agents, connect tools, track workflows, and measure engineering outcomes without managing the underlying orchestration infrastructure.

This format is designed for teams that want to capture value quickly without investing in AI infrastructure or internal training to operate agents. The platform handles model selection, agent provisioning, and the governance layer, delivering measurable results from the first workflow.

Private Deployment

DevAgents OS can be deployed in your own environment, respecting security, data residency, governance, model provider, and infrastructure requirements. Ideal for companies with compliance, data security, or regulatory requirements that call for running the platform on their own infrastructure.

In this format, your team operates the platform internally with full control over data, models, and integrations. DevAgents OS provides the license, deployment documentation, and technical support to keep the platform operational and integrated with your existing stack.

Enterprise Hybrid

An enterprise deployment model that combines managed workspace, private deployment, support, updates, and governance according to technical maturity and security requirements. Teams transition between formats as they evolve.

This format recognizes that most organizations don't start and end in the same place. A team can begin with the managed workspace to validate impact, move to private deployment as they internalize operations, and keep the managed workspace for workflows where complexity justifies it. Enterprise Hybrid enables this evolution without switching platforms.

How adoption starts

DevAgents OS starts with one real engineering workflow, activates the first agent, establishes baseline metrics, and expands based on measured impact.

The platform captures the baseline first: where the team loses time, where rework occurs, where decisions get lost, where quality breaks down, and where security arrives too late. From there, we define the most suitable deployment model, connect the first agent to your stack, and expand as impact is measured.