Yes, 0PTIKUBE is completely free to use.

0PTIKUBE

Open-source Kubernetes dashboard for pod metrics and AI-assisted resource optimization.

free

Last Updated 7/30/2026

About 0PTIKUBE

0PTIKUBE is a self-hosted, open-source web application for visualizing Kubernetes infrastructure, especially pod and cluster CPU and memory usage. The official site positions it as an intuitive Kubernetes visualization tool, while the GitHub README explains a more practical setup: a Next.js and TypeScript application backed by Prometheus metrics, Minikube test workloads, GitHub OAuth, a database connection, and OpenAI API configuration for AI-assisted resource recommendations. It fits platform engineers, SREs, and Kubernetes learners who want an inspectable project for dashboarding recent resource usage and experimenting with AI guidance before buying a commercial platform. It is a poor fit for teams that need turnkey SaaS onboarding, enterprise support, mature alerting, multi-cluster operations, role-based governance, or broad observability across logs, traces, incidents, and cost. Pricing is favorable because the repository is MIT licensed and no paid plan was found, but adoption is not zero-effort: teams should check cluster prerequisites, Prometheus setup, secret handling, OAuth configuration, OpenAI usage costs, release maturity, and whether the project activity is sufficient for production use.

Best for

Platform engineers and SREs who want a small open-source dashboard for Kubernetes CPU and memory visibility before adopting a larger observability suite.

Developer teams learning Kubernetes operations with Minikube, Prometheus, and pod-level metrics in a visual interface.

Infrastructure teams evaluating AI-assisted resource recommendations while keeping the app self-hosted and inspectable.

Best use cases

Use it in a lab or internal evaluation cluster to visualize pod and cluster resource usage over recent time windows without relying only on terminal commands.

Use it to explore whether AI-generated resource allocation suggestions are useful enough to justify deeper integration with existing Prometheus and Kubernetes workflows.

Use it as a teaching or prototype environment for Kubernetes observability patterns, PromQL-backed metrics, GitHub OAuth, and OpenAI-powered operations features.

Compare before choosing

Compare with K8sGPT when the priority is command-line cluster diagnostics and issue explanations rather than a web dashboard for CPU and memory charts.

Compare with Lens Prism or Lens K8S IDE when the team needs a mature desktop Kubernetes management environment with AI assistance and broader cluster operations.

Compare with CAST AI, Botkube, or a full observability platform when the requirement is production-grade automation, notifications, multi-cluster management, cost optimization, support, or enterprise controls.

Features

Kubernetes resource dashboard

Visualizes Kubernetes pod and cluster CPU and memory usage in a web interface instead of forcing users to inspect every metric through terminal commands.

Prometheus-backed metrics

The setup flow relies on Prometheus and port forwarding so the app can display current and recent resource data from a local Kubernetes environment.

Historical metric windows

The README describes near real-time historical monitoring with metric views for the last hour, 12 hours, 24 hours, and 7 days.

Pod and cluster display modes

The homepage highlights display modes for viewing resource usage of individual pods or switching to a broader cluster overview.

AI resource recommendations

The project includes an AI path intended to generate tailored resource reallocation insights, with OpenAI API configuration required by the README.

Self-hosted open-source stack

The public repository is MIT licensed and built with Next.js, React, TypeScript, D3.js, Node.js, PostgreSQL, Docker, Minikube, Kubernetes, and Prometheus.

Pros

Free MIT-licensed source code makes the project inspectable, forkable, and suitable for internal experiments.
Focused Kubernetes CPU and memory visualization can reduce the friction of jumping between kubectl, Prometheus, and ad hoc terminal views.
AI recommendation support gives teams a concrete way to test resource optimization workflows before committing to a commercial AIOps platform.
The documented Minikube and Prometheus setup is useful for learning, demos, and controlled lab environments.

Cons

No public hosted service, pricing page, release page, or enterprise support path was found, so production adoption requires extra diligence.
Setup is hands-on and depends on local Kubernetes tooling, Prometheus port forwarding, database configuration, GitHub OAuth, and OpenAI API keys.
The official evidence focuses on CPU and memory metrics; broader logs, traces, incidents, alerting, and cost controls are not clearly covered.
The repository has limited public traction and no published releases, which increases maintenance and roadmap risk for production buyers.

Pricing Plans

MIT open-source self-hosted

FreeNo software billing period

MIT-licensed public repository
Self-hosted Kubernetes visualization application
Prometheus-based CPU and memory metrics
AI recommendation workflow with user-provided OpenAI API configuration

* Pricing information is for reference only. Please refer to the latest announcement on 0PTIKUBE's official website.

FAQ

0PTIKUBE is an open-source web application for visualizing Kubernetes pod and cluster resource usage, with a focus on CPU, memory, and AI-assisted optimization insights.

Yes for the software itself: the official repository is MIT licensed and no official paid plan was found. Users still pay for their own infrastructure, database, Kubernetes environment, and OpenAI API usage.

The README lists Node.js plus a setup flow involving Docker Desktop, Minikube, kubectl, Prometheus, GitHub OAuth, a database connection, and OpenAI API keys for AI features.

Not by itself based on public evidence. It looks better suited to labs, prototypes, learning environments, and internal evaluations unless a team is ready to own deployment, security, monitoring, and maintenance.

The README describes AI integration for tailored insights into reallocating pod or cluster resources and requires OpenAI API configuration. Buyers should verify model behavior, prompt controls, data exposure, and cost before using it on sensitive clusters.

Compare K8sGPT for command-line diagnostics, Lens Prism for mature Kubernetes IDE workflows, Botkube for chat-based monitoring and troubleshooting, and CAST AI for production-grade Kubernetes optimization and cost automation.