Best AI tools for< Manage Kubernetes Cluster >

unSkript is an Agentic Gen AI platform designed for IT support, offering proactive health checks, issue diagnosis, and resolution. The platform leverages AI to detect and resolve customer issues before they escalate, reducing MTTR, increasing first-call resolution rates, and minimizing ticket resolution time. With features like proactive health checks, automated RCA, and generative AI-based remediation, unSkript aims to streamline IT operations and minimize downtime for software teams. The platform is trusted by top companies worldwide and covered by Forbes.

KubeHelper is an AI-powered tool designed to reduce Kubernetes downtime by providing troubleshooting solutions and command searches. It seamlessly integrates with Slack, allowing users to interact with their Kubernetes cluster in plain English without the need to remember complex commands. With features like troubleshooting steps, command search, infrastructure management, scaling capabilities, and service disruption detection, KubeHelper aims to simplify Kubernetes operations and enhance system reliability.

Rafay is an AI-powered platform that accelerates cloud-native and AI/ML initiatives for enterprises. It provides automation for Kubernetes clusters, cloud cost optimization, and AI workbenches as a service. Rafay enables platform teams to focus on innovation by automating self-service cloud infrastructure workflows.

Nebius AI is an AI-centric cloud platform designed to handle intensive workloads efficiently. It offers a range of advanced features to support various AI applications and projects. The platform ensures high performance and security for users, enabling them to leverage AI technology effectively in their work. With Nebius AI, users can access cutting-edge AI tools and resources to enhance their projects and streamline their workflows.

Mystic.ai is an AI tool designed to deploy and scale Machine Learning models with ease. It offers a fully managed Kubernetes platform that runs in your own cloud, allowing users to deploy ML models in their own Azure/AWS/GCP account or in a shared GPU cluster. Mystic.ai provides cost optimizations, fast inference, simpler developer experience, and performance optimizations to ensure high-performance AI model serving. With features like pay-as-you-go API, cloud integration with AWS/Azure/GCP, and a beautiful dashboard, Mystic.ai simplifies the deployment and management of ML models for data scientists and AI engineers.

Webb.ai is an AI-powered platform that offers automated troubleshooting for Kubernetes. It is designed to assist users in identifying and resolving issues within their Kubernetes environment efficiently. By leveraging AI technology, Webb.ai provides insights and recommendations to streamline the troubleshooting process, ultimately improving system reliability and performance. The platform is user-friendly and caters to both beginners and experienced users in the field of Kubernetes management.

Kubeflow is an open-source machine learning (ML) toolkit that makes deploying ML workflows on Kubernetes simple, portable, and scalable. It provides a unified interface for model training, serving, and hyperparameter tuning, and supports a variety of popular ML frameworks including PyTorch, TensorFlow, and XGBoost. Kubeflow is designed to be used with Kubernetes, a container orchestration system that automates the deployment, management, and scaling of containerized applications.

UbiOps is an AI infrastructure platform that helps teams quickly run their AI & ML workloads as reliable and secure microservices. It offers powerful AI model serving and orchestration with unmatched simplicity, speed, and scale. UbiOps allows users to deploy models and functions in minutes, manage AI workloads from a single control plane, integrate easily with tools like PyTorch and TensorFlow, and ensure security and compliance by design. The platform supports hybrid and multi-cloud workload orchestration, rapid adaptive scaling, and modular applications with unique workflow management system.

Roboweb is an AI assistant designed for exploratory programming. It integrates OpenAI's ChatGPT into JupyterLab to provide users with an optimal environment for exploratory programming tasks. Users can easily deploy the application on Kubernetes and benefit from features like error detection and code fixing assistance. Roboweb also allows users to sign in, create accounts, and manage their chats efficiently. With a focus on enhancing the programming experience, Roboweb is a valuable tool for developers and programmers.

Operant is a cloud-native runtime protection platform that offers instant visibility and control from infrastructure to APIs. It provides AI security shield for applications, API threat protection, Kubernetes security, automatic microsegmentation, and DevSecOps solutions. Operant helps defend APIs, protect Kubernetes, and shield AI applications by detecting and blocking various attacks in real-time. It simplifies security for cloud-native environments with zero instrumentation, application code changes, or integrations.

Union.ai is an infrastructure platform designed for AI, ML, and data workloads. It offers a scalable MLOps platform that optimizes resources, reduces costs, and fosters collaboration among team members. Union.ai provides features such as declarative infrastructure, data lineage tracking, accelerated datasets, and more to streamline AI orchestration on Kubernetes. It aims to simplify the management of AI, ML, and data workflows in production environments by addressing complexities and offering cost-effective strategies.

The website is Microsoft Azure, a cloud computing service offering a wide range of products and solutions for businesses and developers. Azure provides services such as virtual machines, AI services, Kubernetes, DevOps, SQL, and more. It aims to help organizations innovate, migrate to the cloud, and build intelligent applications with AI capabilities. Azure offers a secure and scalable platform for various workloads, including data analytics, application development, and hybrid cloud solutions.

PrimeOrbit is an AI-driven cloud cost optimization platform designed to empower operations and boost ROI for enterprises. The platform focuses on streamlining operations and simplifying cost management by delivering quality-centric solutions. It offers AI-driven optimization recommendations, automated cost allocation, and tailored FinOps for optimal efficiency and control. PrimeOrbit stands out by providing user-centric approach, superior AI recommendations, customization, and flexible enterprise workflow. It supports major cloud providers including AWS, Azure, and GCP, with full support for GCP and Kubernetes coming soon. The platform ensures complete cost allocation across cloud resources, empowering decision-makers to optimize cloud spending efficiently and effectively.

Lacework is a cloud security platform that provides comprehensive security solutions for DevOps, Containers, and Cloud Environments. It offers features such as Code Security, Workload Protection, Identities and Entitlements management, Posture Management, Kubernetes Security, Data Posture Management, Infrastructure as Code security, Software Composition Analysis, Application Security Testing, Edge Security, and Platform Overview. Lacework empowers users to secure their entire cloud infrastructure, prioritize risks, protect workloads, and stay compliant by leveraging AI-driven technologies and behavior-based threat detection. The platform helps automate compliance reporting, fix vulnerabilities, and reduce alerts, ultimately enhancing cloud security and operational efficiency.

Height is an autonomous project management tool designed for teams involved in designing and building projects. It automates manual tasks to provide space for collaborative work, focusing on backlog upkeep, spec updates, and bug triage. With project intelligence and collaboration features, Height offers a customizable workspace with autonomous capabilities to streamline project management. Users can discuss projects in context and benefit from an AI assistant for creating better stories. The tool aims to revolutionize project management by offloading routine tasks to an intelligent system.

SocialBee is an AI-powered social media management tool that helps businesses and individuals manage their social media accounts efficiently. It offers a range of features, including content creation, scheduling, analytics, and collaboration, to help users plan, create, and publish engaging social media content. SocialBee also provides insights into social media performance, allowing users to track their progress and make data-driven decisions.

Moning is a platform designed to help users manage and boost their wealth easily. It provides tools for a global view of wealth, making better investment decisions, avoiding costly mistakes, and increasing performance. With features like AI Analysis, Dividends calendar, and Dividend and Growth Safety Scores, Moning offers a mix of Human & Artificial Intelligence to enhance investment knowledge and decision-making. Users can track and manage their wealth through a comprehensive dashboard, access detailed information on stocks, ETFs, and cryptos, and benefit from quick screeners to find the best investment opportunities.

Legitt AI is an AI-powered Contract Lifecycle Management platform that offers a comprehensive solution for managing contracts at scale. It combines automation and intelligence to revolutionize contract management, ensuring efficiency, accuracy, and compliance with legal standards. The platform streamlines contract creation, signing, tracking, and management processes by embedding intelligence in every step. Legitt AI enhances contract review processes, contract tracking, and contract intelligence at scale, providing users with insights, recommendations, and automated workflows. With robust security measures, scalable infrastructure, and integrations with popular business tools, Legitt AI empowers businesses to manage contracts with precision and efficiency.

Social Places is a leading franchise marketing agency that provides a suite of tools to help businesses with multiple locations manage their online presence. The platform includes tools for managing listings, reputation, social media, ads, and bookings. Social Places also offers a conversational AI chatbot and a custom feedback form builder.

CommodityAI is a web-based platform that uses AI, automation, and collaboration tools to help businesses manage their commodity shipments and supply chains more efficiently. The platform offers a range of features, including shipment management automation, intelligent document processing, stakeholder collaboration, and supply-chain automation. CommodityAI can help businesses improve data accuracy, eliminate manual processes, and streamline communication and collaboration. The platform is designed for the commodities industry and offers commodity-specific automations, ERP integration, and AI-powered insights.

k8m is an AI-driven Mini Kubernetes AI Dashboard lightweight console tool designed to simplify cluster management. It is built on AMIS and uses 'kom' as the Kubernetes API client. k8m has built-in Qwen2.5-Coder-7B model interaction capabilities and supports integration with your own private large models. Its key features include miniaturized design for easy deployment, user-friendly interface for intuitive operation, efficient performance with backend in Golang and frontend based on Baidu AMIS, pod file management for browsing, editing, uploading, downloading, and deleting files, pod runtime management for real-time log viewing, log downloading, and executing shell commands within pods, CRD management for automatic discovery and management of CRD resources, and intelligent translation and diagnosis based on ChatGPT for YAML property translation, Describe information interpretation, AI log diagnosis, and command recommendations, providing intelligent support for managing k8s. It is cross-platform compatible with Linux, macOS, and Windows, supporting multiple architectures like x86 and ARM for seamless operation. k8m's design philosophy is 'AI-driven, lightweight and efficient, simplifying complexity,' helping developers and operators quickly get started and easily manage Kubernetes clusters.

KubeSphere is a distributed operating system for cloud-native application management, using Kubernetes as its kernel. It provides a plug-and-play architecture, allowing third-party applications to be seamlessly integrated into its ecosystem. KubeSphere is also a multi-tenant container platform with full-stack automated IT operation and streamlined DevOps workflows. It provides developer-friendly wizard web UI, helping enterprises to build out a more robust and feature-rich platform, which includes most common functionalities needed for enterprise Kubernetes strategy.

OpenMeter is a real-time and scalable usage metering tool for AI, usage-based billing, infrastructure, and IoT use cases. It provides a REST API for integrations and offers client SDKs in Node.js, Python, Go, and Web. OpenMeter is licensed under the Apache 2.0 License.

Helios is a powerful open-source tool for managing and monitoring your Kubernetes clusters. It provides a user-friendly interface to easily visualize and control your cluster resources, including pods, deployments, services, and more. With Helios, you can efficiently manage your containerized applications and ensure high availability and performance of your Kubernetes infrastructure.

HAMi is a Heterogeneous AI Computing Virtualization Middleware designed to manage Heterogeneous AI Computing Devices in a Kubernetes cluster. It allows for device sharing, device memory control, device type specification, and device UUID specification. The tool is easy to use and does not require modifying task YAML files. It includes features like hard limits on device memory, partial device allocation, streaming multiprocessor limits, and core usage specification. HAMi consists of components like a mutating webhook, scheduler extender, device plugins, and in-container virtualization techniques. It is suitable for scenarios requiring device sharing, specific device memory allocation, GPU balancing, low utilization optimization, and scenarios needing multiple small GPUs. The tool requires prerequisites like NVIDIA drivers, CUDA version, nvidia-docker, Kubernetes version, glibc version, and helm. Users can install, upgrade, and uninstall HAMi, submit tasks, and monitor cluster information. The tool's roadmap includes supporting additional AI computing devices, video codec processing, and Multi-Instance GPUs (MIG).

This Helm chart bootstraps an Airflow deployment on a Kubernetes cluster using the Helm package manager. The version of this chart does not correlate to any other component. Users should not expect feature parity between OSS airflow chart and the Astronomer airflow-chart for identical version numbers. To install this helm chart remotely (using helm 3) kubectl create namespace airflow helm repo add astronomer https://helm.astronomer.io helm install airflow --namespace airflow astronomer/airflow To install this repository from source sh kubectl create namespace airflow helm install --namespace airflow . Prerequisites: Kubernetes 1.12+ Helm 3.6+ PV provisioner support in the underlying infrastructure Installing the Chart: sh helm install --name my-release . The command deploys Airflow on the Kubernetes cluster in the default configuration. The Parameters section lists the parameters that can be configured during installation. Upgrading the Chart: First, look at the updating documentation to identify any backwards-incompatible changes. To upgrade the chart with the release name `my-release`: sh helm upgrade --name my-release . Uninstalling the Chart: To uninstall/delete the `my-release` deployment: sh helm delete my-release The command removes all the Kubernetes components associated with the chart and deletes the release. Updating DAGs: Bake DAGs in Docker image The recommended way to update your DAGs with this chart is to build a new docker image with the latest code (`docker build -t my-company/airflow:8a0da78 .`), push it to an accessible registry (`docker push my-company/airflow:8a0da78`), then update the Airflow pods with that image: sh helm upgrade my-release . --set images.airflow.repository=my-company/airflow --set images.airflow.tag=8a0da78 Docker Images: The Airflow image that are referenced as the default values in this chart are generated from this repository: https://github.com/astronomer/ap-airflow. Other non-airflow images used in this chart are generated from this repository: https://github.com/astronomer/ap-vendor. Parameters: The complete list of parameters supported by the community chart can be found on the Parameteres Reference page, and can be set under the `airflow` key in this chart. The following tables lists the configurable parameters of the Astronomer chart and their default values. | Parameter | Description | Default | | :----------------------------- | :-------------------------------------------------------------------------------------------------------- | :---------------------------- | | `ingress.enabled` | Enable Kubernetes Ingress support | `false` | | `ingress.acme` | Add acme annotations to Ingress object | `false` | | `ingress.tlsSecretName` | Name of secret that contains a TLS secret | `~` | | `ingress.webserverAnnotations` | Annotations added to Webserver Ingress object | `{}` | | `ingress.flowerAnnotations` | Annotations added to Flower Ingress object | `{}` | | `ingress.baseDomain` | Base domain for VHOSTs | `~` | | `ingress.auth.enabled` | Enable auth with Astronomer Platform | `true` | | `extraObjects` | Extra K8s Objects to deploy (these are passed through `tpl`). More about Extra Objects. | `[]` | | `sccEnabled` | Enable security context constraints required for OpenShift | `false` | | `authSidecar.enabled` | Enable authSidecar | `false` | | `authSidecar.repository` | The image for the auth sidecar proxy | `nginxinc/nginx-unprivileged` | | `authSidecar.tag` | The image tag for the auth sidecar proxy | `stable` | | `authSidecar.pullPolicy` | The K8s pullPolicy for the the auth sidecar proxy image | `IfNotPresent` | | `authSidecar.port` | The port the auth sidecar exposes | `8084` | | `gitSyncRelay.enabled` | Enables git sync relay feature. | `False` | | `gitSyncRelay.repo.url` | Upstream URL to the git repo to clone. | `~` | | `gitSyncRelay.repo.branch` | Branch of the upstream git repo to checkout. | `main` | | `gitSyncRelay.repo.depth` | How many revisions to check out. Leave as default `1` except in dev where history is needed. | `1` | | `gitSyncRelay.repo.wait` | Seconds to wait before pulling from the upstream remote. | `60` | | `gitSyncRelay.repo.subPath` | Path to the dags directory within the git repository. | `~` | Specify each parameter using the `--set key=value[,key=value]` argument to `helm install`. For example, sh helm install --name my-release --set executor=CeleryExecutor --set enablePodLaunching=false . Walkthrough using kind: Install kind, and create a cluster We recommend testing with Kubernetes 1.25+, example: sh kind create cluster --image kindest/node:v1.25.11 Confirm it's up: sh kubectl cluster-info --context kind-kind Add Astronomer's Helm repo sh helm repo add astronomer https://helm.astronomer.io helm repo update Create namespace + install the chart sh kubectl create namespace airflow helm install airflow -n airflow astronomer/airflow It may take a few minutes. Confirm the pods are up: sh kubectl get pods --all-namespaces helm list -n airflow Run `kubectl port-forward svc/airflow-webserver 8080:8080 -n airflow` to port-forward the Airflow UI to http://localhost:8080/ to confirm Airflow is working. Login as _admin_ and password _admin_. Build a Docker image from your DAGs: 1. Start a project using astro-cli, which will generate a Dockerfile, and load your DAGs in. You can test locally before pushing to kind with `astro airflow start`. `sh mkdir my-airflow-project && cd my-airflow-project astro dev init` 2. Then build the image: `sh docker build -t my-dags:0.0.1 .` 3. Load the image into kind: `sh kind load docker-image my-dags:0.0.1` 4. Upgrade Helm deployment: sh helm upgrade airflow -n airflow --set images.airflow.repository=my-dags --set images.airflow.tag=0.0.1 astronomer/airflow Extra Objects: This chart can deploy extra Kubernetes objects (assuming the role used by Helm can manage them). For Astronomer Cloud and Enterprise, the role permissions can be found in the Commander role. yaml extraObjects: - apiVersion: batch/v1beta1 kind: CronJob metadata: name: "{{ .Release.Name }}-somejob" spec: schedule: "*/10 * * * *" concurrencyPolicy: Forbid jobTemplate: spec: template: spec: containers: - name: myjob image: ubuntu command: - echo args: - hello restartPolicy: OnFailure Contributing: Check out our contributing guide! License: Apache 2.0 with Commons Clause

LangStream is a tool for natural language processing tasks, providing a CLI for easy installation and usage. Users can try sample applications like Chat Completions and create their own applications using the developer documentation. It supports running on Kubernetes for production-ready deployment, with support for various Kubernetes distributions and external components like Apache Kafka or Apache Pulsar cluster. Users can deploy LangStream locally using minikube and manage the cluster with mini-langstream. Development requirements include Docker, Java 17, Git, Python 3.11+, and PIP, with the option to test local code changes using mini-langstream.

Kaito is an operator that automates the AI/ML inference model deployment in a Kubernetes cluster. It manages large model files using container images, avoids tuning deployment parameters to fit GPU hardware by providing preset configurations, auto-provisions GPU nodes based on model requirements, and hosts large model images in the public Microsoft Container Registry (MCR) if the license allows. Using Kaito, the workflow of onboarding large AI inference models in Kubernetes is largely simplified.

K8sGPT is a tool for scanning your Kubernetes clusters, diagnosing, and triaging issues in simple English. It has SRE experience codified into its analyzers and helps to pull out the most relevant information to enrich it with AI.

LLMariner is an extensible open source platform built on Kubernetes to simplify the management of generative AI workloads. It enables efficient handling of training and inference data within clusters, with OpenAI-compatible APIs for seamless integration with a wide range of AI-driven applications.

Kafka-ML is a framework designed to manage the pipeline of Tensorflow/Keras and PyTorch machine learning models on Kubernetes. It enables the design, training, and inference of ML models with datasets fed through Apache Kafka, connecting them directly to data streams like those from IoT devices. The Web UI allows easy definition of ML models without external libraries, catering to both experts and non-experts in ML/AI.

Langfuse is a powerful tool that helps you develop, monitor, and test your LLM applications. With Langfuse, you can: * **Develop:** Instrument your app and start ingesting traces to Langfuse, inspect and debug complex logs, and manage, version, and deploy prompts from within Langfuse. * **Monitor:** Track metrics (cost, latency, quality) and gain insights from dashboards & data exports, collect and calculate scores for your LLM completions, run model-based evaluations, collect user feedback, and manually score observations in Langfuse. * **Test:** Track and test app behaviour before deploying a new version, test expected in and output pairs and benchmark performance before deploying, and track versions and releases in your application. Langfuse is easy to get started with and offers a generous free tier. You can sign up for Langfuse Cloud or deploy Langfuse locally or on your own infrastructure. Langfuse also offers a variety of integrations to make it easy to connect to your LLM applications.

Omnia is a deployment tool designed to turn servers with RPM-based Linux images into functioning Slurm/Kubernetes clusters. It provides an Ansible playbook-based deployment for Slurm and Kubernetes on servers running an RPM-based Linux OS. The tool simplifies the process of setting up and managing clusters, making it easier for users to deploy and maintain their infrastructure.

Ollama Operator is a Kubernetes operator designed to facilitate running large language models on Kubernetes clusters. It simplifies the process of deploying and managing multiple models on the same cluster, providing an easy-to-use interface for users. With support for various Kubernetes environments and seamless integration with Ollama models, APIs, and CLI, Ollama Operator streamlines the deployment and management of language models. By leveraging the capabilities of lama.cpp, Ollama Operator eliminates the need to worry about Python environments and CUDA drivers, making it a reliable tool for running large language models on Kubernetes.

ChatOpsLLM is a project designed to empower chatbots with effortless DevOps capabilities. It provides an intuitive interface and streamlined workflows for managing and scaling language models. The project incorporates robust MLOps practices, including CI/CD pipelines with Jenkins and Ansible, monitoring with Prometheus and Grafana, and centralized logging with the ELK stack. Developers can find detailed documentation and instructions on the project's website.

AirbyteServerless is a lightweight tool designed to simplify the management of Airbyte connectors. It offers a serverless mode for running connectors, allowing users to easily move data from any source to their data warehouse. Unlike the full Airbyte-Open-Source-Platform, AirbyteServerless focuses solely on the Extract-Load process without a UI, database, or transform layer. It provides a CLI tool, 'abs', for managing connectors, creating connections, running jobs, selecting specific data streams, handling secrets securely, and scheduling remote runs. The tool is scalable, allowing independent deployment of multiple connectors. It aims to streamline the connector management process and provide a more agile alternative to the comprehensive Airbyte platform.

A curated list of awesome Model Context Protocol (MCP) servers that enable AI models to securely interact with local and remote resources through standardized server implementations. The list focuses on production-ready and experimental servers extending AI capabilities through file access, database connections, API integrations, and other contextual services.

Awesome MCP Servers is a curated list of Model Context Protocol (MCP) servers that enable AI models to securely interact with local and remote resources through standardized server implementations. The list includes production-ready and experimental servers that extend AI capabilities through file access, database connections, API integrations, and other contextual services.

A massive list including a huge amount of products and services that are completely free! ⭐ Star on GitHub • 🤝 Contribute # Table of Contents * APIs, Data & ML * Artificial Intelligence * BaaS * Code Editors * Code Generation * DNS * Databases * Design & UI * Domains * Email * Font * For Students * Forms * Linux Distributions * Messaging & Streaming * PaaS * Payments & Billing * SSL

Beta9 is an open-source platform for running scalable serverless GPU workloads across cloud providers. It allows users to scale out workloads to thousands of GPU or CPU containers, achieve ultrafast cold-start for custom ML models, automatically scale to zero to pay for only what is used, utilize flexible distributed storage, distribute workloads across multiple cloud providers, and easily deploy task queues and functions using simple Python abstractions. The platform is designed for launching remote serverless containers quickly, featuring a custom, lazy loading image format backed by S3/FUSE, a fast redis-based container scheduling engine, content-addressed storage for caching images and files, and a custom runc container runtime.

Learn Kubernetes by Hands-on Labs and AI

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Adaptive and clear Bash guide with command execution. Learn by poking around in the code interpreter's isolated Kubernetes container!

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