Radar

Open-source Kubernetes interface with UI and MCP server for troubleshooting and visibility

Productivity & Office Productivity ToolsFreemium

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$149/moStarting price

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About Radar

Radar provides a unified interface for observing and managing Kubernetes clusters through two components: a web UI optimized for human operators and an MCP server designed for AI agents. The open-source version runs locally or in-cluster and offers live topology views, event timelines, Helm chart visibility, GitOps integration with Argo CD and Flux, and cluster audits with 44 best-practice checks. It supports cross-cluster search, resource health tracking, and detailed inspection of workloads, environments, and image contents. Radar Cloud extends these capabilities with hosted alerts, AI-driven root cause analysis, shared team views, and long-term event history up to 30 days. The tool is distributed as a single binary with multiple installation options including Homebrew, direct download, and Helm charts. It is released under the Apache 2.0 license and emphasizes data privacy by keeping cluster information within the user’s infrastructure.

Key features

Live resource graph with SSE updates
Event timeline beyond default 1-hour TTL
Helm chart versioning and rollback
MCP server for AI agent integration
Cluster audit with 44 best-practice checks
Cross-cluster search and comparison
GitOps drift detection for Argo CD and Flux
RBAC-scoped shared views in Radar Cloud

Use cases

Troubleshooting multi-cluster incidents without kubectl
Managing fleet-wide visibility across regions and clouds
GitOps drift detection and application health monitoring

Pros

Open-source Apache 2.0 license with no feature restrictions
Dual interfaces: UI for humans and MCP server for AI agents
Cross-cluster search and comparison with live topology visualization
GitOps visibility with Argo CD and Flux integration
Cluster audits with 44 best-practice checks

Cons

Radar Cloud requires a paid plan for alerts, AI investigations, and team features
Self-Managed option limited to Enterprise and Custom plans
No native mobile or mobile-optimized interface

Frequently asked questions about Radar

What is Radar and what does it do?

Radar is an open-source Kubernetes interface designed for both human operators and AI agents. It provides a unified view of Kubernetes clusters through a web UI and an MCP server, offering live topology visualization, event timelines, Helm chart visibility, GitOps integration, and cluster audits with best-practice checks.

Who should use Radar?

Radar is built for Kubernetes operators, platform teams, and DevOps engineers who need to troubleshoot, monitor, and manage multiple clusters efficiently. It is also useful for AI agent developers who require cluster context for debugging or automation tasks.

How does Radar differ from other Kubernetes tools like Lens or k9s?

Unlike traditional tools, Radar combines a modern UI optimized for humans with an MCP server for AI agents, offering features like cross-cluster search, AI-driven root cause analysis, and long-term event history. It also emphasizes data privacy by keeping cluster information within the user’s infrastructure.

What is Radar Cloud and how does it relate to the open-source version?

Radar Cloud is the hosted version of Radar, adding features like alerts, AI investigations, shared team views, and long-term event history. The open-source version (Radar OSS) remains free and fully functional, while Radar Cloud extends its capabilities for teams needing additional features.

How do I get started with Radar?

Radar can be installed as a single binary using methods like Homebrew, direct download, or Helm charts. No account is required for the open-source version, and it can be run locally or in-cluster. Documentation and community resources are available for setup and usage.

Does Radar support integrations with other tools?

Yes, Radar integrates with GitOps tools like Argo CD and Flux, providing visibility into sync states and drift. It also supports Kubernetes RBAC for team collaboration and can be used with MCP-compatible AI agents for enhanced debugging and automation.