self-hosted coding agent
Self-host a coding agent on infrastructure you control
A self-hosted model endpoint is only one layer. A useful coding agent also needs safe tools, session state, repository context, and an operator workflow.
OpenJet combines a local model runtime with the tools required for repository work. Run it on the developer workstation or keep inference on an internal GPU machine while maintaining control of the model and data path.
Why local wins
- Own the inference host. Use local hardware rather than a managed model API.
- Separate model and execution. Run the model on one controlled machine while repository execution remains on another target when required.
- Reuse one runtime. The agent, benchmarking tools, workflows, and Python SDK share the configured backend.
- Control state-changing work. Human approval stays in the shell execution loop.
Common deployment paths
- Provide an internal coding agent from a shared GPU workstation.
- Keep source code processing inside a company network.
- Standardize a tested model profile for a development team.
- Benchmark local models before wider internal rollout.
Frequently asked questions
- What is a self-hosted coding agent?
- It is a coding agent whose model runtime and supporting services run on infrastructure controlled by the user or organization.
- Can the GPU be on another machine?
- OpenJet supports remote execution patterns, allowing teams to design the model and tool topology around controlled infrastructure.
- Does self-hosting remove all costs?
- It removes hosted token charges, but the organization still pays for hardware, power, storage, maintenance, and operations.
Explore OpenJet
Embed a local model with the Python SDK, run Qwen3.8-27B as a local coding agent, compare local coding-agent alternatives, read the docs, or go back to the homepage.