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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
System of Record for Kubernetes cost accounting: per-namespace CPU, memory and GPU usage, with the 30% non-allocatable overhead made visible. Connects to AI assistants via MCP (Claude, Gemini, Mistral, Cursor) for plain-language analysis. Formerly Kube-Opex-Analytics.
| Date | Stars |
|---|---|
| 2026-07-31 | 484 |
| 2026-08-06 | 484 |
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growth rate 0.00%/day
# KubeLedger  [](https://github.com/realopslabs/kubeledger/releases)  [](https://github.com/realopslabs/kubeledger/pkgs/container/kubeledger) --- **KubeLedger** is the System of Record that tracks the full picture of Kubernetes costs, revealing the 30% hidden in non-allocatable overhead for precise, per-namespace accounting. > **Note:** KubeLedger was formerly known as **Kubernetes Opex Analytics** aka `kube-opex-analytics`. > Read more about this change in our [announcement blog post](https://kubeledger.io/blog/2025/01/01/kubeledger-announcement/). To handle the migration in a straightforward way, we have provided a [migration procedure](https://kubeledger.io/docs/migration-from-kube-opex-analytics-to-kubeledger/). ## Table of Contents - [Overview](#overview) - [Key Features](#key-features) - [Quick Start](#quick-start) - [MCP Integration (AI Assistant) / Requires v26.05.0+](#mcp-integration-ai-assistant--requires-v26050) - [Architecture](#architecture) - [Documentation](#documentation) - [Configuration](#configuration) - [Troubleshooting](#troubleshooting) - [License](#license) - [Support & Contributions](#support--contributions) ## Overview **KubeLedger** is a usage accounting tool that helps organizations track, analyze, and optimize **CPU, Memory, and GPU** resources on Kubernetes clusters over time (hourly, daily, monthly). It acts as a **System of Record** for your cluster resources, providing insightful usage analytics and charts that engineering and financial teams can use as key indicators for cost optimization decisions. > **New in v26.05: ask your AI assistant.** KubeLedger ships an optional [Model Context Protocol (MCP) server](#mcp-integration-ai-assistant--requires-v26050) that exposes the same analytics to AI tools such as Claude, Gemini, Mistral, Cursor, Windsurf, MCP Inspector and other MCP-aware clients. Ten read-only tools let an assistant rank consumers, group namespaces, assess efficiency or compare periods in plain language, unlocking investigations beyond what predefined dashboards can show. ### Tracked Resources - **CPU** - Core usage and requests per namespace - **Memory** - RAM consumption and requests per namespace - **GPU** - NVIDIA GPU utilization via DCGM integration  > **Multi-cluster Integration:** KubeLedger tracks usage for a single Kubernetes cluster. For centralized multi-cluster analytics, see [Krossboard Kubernetes Operator](https://github.com/2-alchemists/krossboard) ([demo video](https://youtu.be/lfkUIREDYDY)). ## Key Features | Feature | Description | |---------|-------------| | **Hourly/Daily/Monthly Trends** | Tracks actual usage and requested capacities per namespace, collected every 5 minutes and consolidated hourly | | **Non-allocatable Capacity Tracking** | Highlights system overhead (OS, kubelets) vs. usable application capacity at node and cluster levels | | **Cluster Capacity Planning** | Visualize consumed capacity globally, instantly, and over time | | **Usage Efficiency Analysis** | Compare resource requests against actual usage to identify over/under-provisioning | | **Cost Allocation & Chargeback** | Automatic resource usage accounting per namespace for billing and showback | | **Prometheus Integration** | Native exporter at `/metrics` for Grafana dashboards and alerting | | **AI Assistant Integration (MCP)** | **New in v26.05.** Read-only Model Context Protocol server: AI tools such as Claude, Gemini, Mistral and Cursor query usage, efficiency, trends and rankings in plain language through ten read-o
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Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:bd6b2c25296a685c, topic:observability, topic:monitoring
matched fp:bd6b2c25296a685c, topic:gpu, topic:kubernetes