Best AI tools for< Manage Kubernetes >

unSkript is an AI-powered infrastructure health intelligence tool designed to ensure the health of your application infrastructure. It uses Generative AI and Intelligent Health Checks to proactively find, diagnose, and fix issues in your application infrastructure. With features like Proactive Health Checks, Generative AI based RCA, and Continuous Learning, unSkript helps streamline processes for cloud-operations teams and software teams. By leveraging AI technology, unSkript aims to minimize downtime, deliver real-time troubleshooting, and allow users to focus on strategic tasks.

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.

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.

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.

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.

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.

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.

Microsoft Azure is a cloud computing service that offers a wide range of products and services for businesses and developers. It provides global infrastructure, FinOps capabilities, customer stories, and innovation insights. Azure features include virtual machines, AI services, Kubernetes service, Cosmos DB, and more. The platform supports hybrid and multicloud solutions, analytics, application development, and modernization. Azure also offers resources, pricing tools, and partner programs. With a focus on AI and machine learning, Azure enables responsible AI development and secure cloud solutions. The platform caters to IT professionals, developers, data analysts, business leaders, startups, and students, offering a comprehensive suite of tools and services.

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.

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.

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.

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.

Gideon Legal is an automated intake and document automation software designed to help law firms manage client journeys from contact to contract and intake to eSign. It uses bots, built-in integrations, and no-code technology to maximize revenue and streamline operations by automating client workflows.

Robopost is a social media scheduling and automation tool designed to help freelancers, entrepreneurs, small businesses, and social media teams create, schedule, publish, and automate content daily. With over 20,000 users, Robopost offers essential tools such as social media post scheduling, AI-powered content creation, team management, multi-image and video posts scheduling, AI assistance for generating captions, automations, calendar view, post ideas generation with AI, posts collection organization, and comprehensive support for numerous social media platforms.

Deploy langgenius/dify, an LLM based chat bot app on kubernetes with helm chart.

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.

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).

E.D.D.I (Enhanced Dialog Driven Interface) is an enterprise-certified chatbot middleware that offers advanced prompt and conversation management for Conversational AI APIs. Developed in Java using Quarkus, it is lean, RESTful, scalable, and cloud-native. E.D.D.I is highly scalable and designed to efficiently manage conversations in AI-driven applications, with seamless API integration capabilities. Notable features include configurable NLP and Behavior rules, support for multiple chatbots running concurrently, and integration with MongoDB, OAuth 2.0, and HTML/CSS/JavaScript for UI. The project requires Java 21, Maven 3.8.4, and MongoDB >= 5.0 to run. It can be built as a Docker image and deployed using Docker or Kubernetes, with additional support for integration testing and monitoring through Prometheus and Kubernetes endpoints.

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

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.

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.

Airbroke is an open-source error catcher tool designed for modern web applications. It provides a PostgreSQL-based backend with an Airbrake-compatible HTTP collector endpoint and a React-based frontend for error management. The tool focuses on simplicity, maintaining a small database footprint even under heavy data ingestion. Users can ask AI about issues, replay HTTP exceptions, and save/manage bookmarks for important occurrences. Airbroke supports multiple OAuth providers for secure user authentication and offers occurrence charts for better insights into error occurrences. The tool can be deployed in various ways, including building from source, using Docker images, deploying on Vercel, Render.com, Kubernetes with Helm, or Docker Compose. It requires Node.js, PostgreSQL, and specific system resources for deployment.

Comet Opik is a repository containing two main services: a frontend and a backend. It provides a Python SDK for easy installation. Users can run the full application locally with minikube, following specific installation prerequisites. The repository structure includes directories for applications like Opik backend, with detailed instructions available in the README files. Users can manage the installation using simple k8s commands and interact with the application via URLs for checking the running application and API documentation. The repository aims to facilitate local development and testing of Opik using Kubernetes technology.

Arcadia is an all-in-one enterprise-grade LLMOps platform that provides a unified interface for developers and operators to build, debug, deploy, and manage AI agents. It supports various LLMs, embedding models, reranking models, and more. Built on langchaingo (golang) for better performance and maintainability. The platform follows the operator pattern that extends Kubernetes APIs, ensuring secure and efficient operations.

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.

AIStore on Kubernetes is a toolkit for deploying a lightweight, scalable object storage solution designed for AI applications in a Kubernetes environment. It includes documentation, Ansible playbooks, Kubernetes operator, Helm charts, and Terraform definitions for deployment on public cloud platforms. The system overview shows deployment across nodes with proxy and target pods utilizing Persistent Volumes. The AIStore Operator automates cluster management tasks. The repository focuses on production deployments but offers different deployment options. Thorough planning and configuration decisions are essential for successful multi-node deployment. The AIStore Operator simplifies tasks like starting, deploying, adjusting size, and updating AIStore resources within Kubernetes.

HolmesGPT is an open-source DevOps assistant powered by OpenAI or any tool-calling LLM of your choice. It helps in troubleshooting Kubernetes, incident response, ticket management, automated investigation, and runbook automation in plain English. The tool connects to existing observability data, is compliance-friendly, provides transparent results, supports extensible data sources, runbook automation, and integrates with existing workflows. Users can install HolmesGPT using Brew, prebuilt Docker container, Python Poetry, or Docker. The tool requires an API key for functioning and supports OpenAI, Azure AI, and self-hosted LLMs.

ClearML Server is a backend service infrastructure for ClearML, facilitating collaboration and experiment management. It includes a web app, RESTful API, and file server for storing images and models. Users can deploy ClearML Server using Docker, AWS EC2 AMI, or Kubernetes. The system design supports single IP or sub-domain configurations with specific open ports. ClearML-Agent Services container allows launching long-lasting jobs and various use cases like auto-scaler service, controllers, optimizer, and applications. Advanced functionality includes web login authentication and non-responsive experiments watchdog. Upgrading ClearML Server involves stopping containers, backing up data, downloading the latest docker-compose.yml file, configuring ClearML-Agent Services, and spinning up docker containers. Community support is available through ClearML FAQ, Stack Overflow, GitHub issues, and email contact.

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.

AzureOpenAI-with-APIM is a repository that provides a one-button deploy solution for Azure API Management (APIM), Key Vault, and Log Analytics to work seamlessly with Azure OpenAI endpoints. It enables organizations to scale and manage their Azure OpenAI service efficiently by issuing subscription keys via APIM, delivering usage metrics, and implementing policies for access control and cost management. The repository offers detailed guidance on implementing APIM to enhance Azure OpenAI resiliency, scalability, performance, monitoring, and chargeback capabilities.

KubeAI is a highly scalable AI platform that runs on Kubernetes, serving as a drop-in replacement for OpenAI with API compatibility. It can operate OSS model servers like vLLM and Ollama, with zero dependencies and additional OSS addons included. Users can configure models via Kubernetes Custom Resources and interact with models through a chat UI. KubeAI supports serving various models like Llama v3.1, Gemma2, and Qwen2, and has plans for model caching, LoRA finetuning, and image generation.

This repository serves as a hub for Microsoft Official Build & Modernize AI Applications reference solutions and content. It provides access to projects demonstrating how to build Generative AI applications using Azure services like Azure OpenAI, Azure Container Apps, Azure Kubernetes, and Azure Cosmos DB. The solutions include Vector Search & AI Assistant, Real-Time Payment and Transaction Processing, and Medical Claims Processing. Additionally, there are workshops like the Intelligent App Workshop for Microsoft Copilot Stack, focusing on infusing intelligence into traditional software systems using foundation models and design thinking.

AIStore is a lightweight object storage system designed for AI applications. It is highly scalable, reliable, and easy to use. AIStore can be deployed on any commodity hardware, and it can be used to store and manage large datasets for deep learning and other AI applications.

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

Dietician that helps those with IBS manage their symptoms via FODMAPs. FODMAP stands for fermentable oligosaccharides, disaccharides, monosaccharides and polyols. These are the chemical names of 5 naturally occurring sugars that are not well absorbed by your small intestine.

Provides cognitive-behavioral and emotional therapy guidance, helping users understand and manage their thoughts, behaviors, and emotions.

Cross-cultural management. Useful for those who relocate to another country or manage cross-cultural teams.

I manage finances securely with encryption and user authentication.

Expert project manager, I can help you manage IT projects

Helps developers manage state using StateAdapt

Recognize, manage, cope. Works well with GPT-Voice.

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I manage your grocery lists to help you stay organized. *1/ Tell me what to add to a list. 2/ Ask me to add all ingredients for a receipe. 3/ Upload a receipt to remove items from your lists 4/ Add an item by simply uploading a picture. 5/ Ask me what items I would recommend you add to your lists.*

Helps create and manage local clubs based on shared interests.

Helps users manage and preserve family heirlooms with empathy and practical advice.

Use me to manage your contents.

Give me your syptoms and I will provide instructions for how to manage your illness.

Your AI Google Slides assistant! Effortlessly locate, manage, and summarize your presentations!