Compare Coder Agents to Cursor Agents
Cursor’s Self-Hosted Cloud Agents let teams run execution environments on infrastructure they control while relying on Cursor’s cloud for orchestration, model access, and the overall agent experience. For many teams, that balance offers a practical way to adopt agent workflows without managing the full system themselves.
Coder Agents is built for environments with stricter requirements. It keeps the control plane, agent loop, and model routing on infrastructure you control, giving teams full visibility into how agents run, how data flows, and whether anything operates outside their network perimeter. This makes it a better fit for regulated industries, air-gapped deployments, and organizations that need complete control over their development and AI systems.
Why look for Cursor Agents alternatives?
The agent loop runs in Cursor's cloud. Cursor's hybrid model executes tools on your machines, but planning, state management, and model inference stay in Cursor's hosted control plane. Per Cursor's docs, file chunks the model reads leave your network. For teams with strict perimeter or residency requirements, that's a meaningful architectural difference.
LLM credentials and routing are Cursor-managed. API keys for cloud agents are held by Cursor. Customers cannot bring their own keys, route to self-hosted models, or point inference at providers like Bedrock or Ollama. Teams that need full control over inference paths typically need a different platform.
Scaling agent workflows is constrained by the system design. Cursor’s architecture relies on a pool of agent workers running on customer infrastructure, with documented limits on how many can run concurrently (currently up to 50 workers per team and up to 10 per user). For organizations with high volumes of parallel agent activity, these caps can introduce bottlenecks and make it harder to scale agent usage consistently across teams.
None of this makes Cursor a worse product. Cursor optimizes for a managed experience and fast onboarding. Coder optimizes for environments where infrastructure boundaries are non-negotiable.
Architectural differences at a glance
| Dimension | Coder Agents | Cursor Agents (self-hosted) |
|---|---|---|
| Execution environment | Customer-controlled infrastructure | Customer-controlled infrastructure |
| Agent loop location | Customer-controlled infrastructure | Cursor's hosted cloud service |
| Model inference | Direct to customer-configured provider, no Coder intermediary | Routed through Cursor |
| Agent tool execution | Customer-controlled infrastructure | Customer-controlled infrastructure |
| Code and prompt data path | Sent only to the configured provider, or kept fully in-network with self-hosted models | Code context is sent to Cursor for inference |
| Internet dependency | Not required for fully self-contained deployments | Required for orchestration and inference |
| Air-gap capable | Supported with self-hosted models | Not supported |
| Model choice and control | Customer chooses and manages providers and models | Limited to Cursor-managed options |
| Control plane location | Customer-controlled infrastructure | Cursor's hosted cloud service |
| Open source | No |
Why teams choose Coder Agents
When “self-hosted” means the entire stack: source code never leaves, model routing is under your control, and there's no mandatory connectivity to a vendor's cloud.
Fully self-hosted architecture, not partial
The entire system runs on your infrastructure, including the control plane, agent loop, model routing, and execution environments. There’s no dependency on an external service for orchestration.
Air-gap and restricted-network ready
Coder Agents can run without internet access using self-hosted models. This makes it viable for environments where outbound connectivity is limited or prohibited.
Direct control over models and providers
Teams choose which models to use and connect directly to providers like Anthropic, OpenAI, or self-hosted endpoints. There’s no intermediary managing routing or credentials.
Built for enterprise scale
Agent workflows run through centralized infrastructure that can support large developer populations without relying on fixed pools of workers or per-task environments.
Unified infrastructure for devs and agents
Agent environments use the same infrastructure and provisioning model as developer workspaces. Platform teams manage one system, not separate stacks.
Open and inspectable
The platform is fully open source, allowing teams to audit, extend, and integrate it into their existing systems without relying on a black box.
