
kubesphere
The container platform tailored for Kubernetes multi-cloud, datacenter, and edge management โ ๐ฅ โ๏ธ
Stars: 15078

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.
README:
The container platform tailored for Kubernetes multi-cloud, datacenter, and edge management
English | ไธญๆ
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, see Feature List for details.
The following screenshots give a close insight into KubeSphere. Please check What is KubeSphere for further information.
Workbench | Project Resources |
![]() |
![]() |
CI/CD Pipeline | App Store |
![]() |
![]() |
๐ฎ KubeSphere Lite provides you with free, stable, and out-of-the-box managed cluster service. After registration and login, you can easily create a K8s cluster with KubeSphere installed in only 5 seconds and experience feature-rich KubeSphere.
๐ฅ You can view the Demo Video to get started with KubeSphere.
๐ธ Provisioning Kubernetes Cluster
Support deploy Kubernetes on any infrastructure, support online and air-gapped installation. Learn more.๐ Kubernetes Multi-cluster Management
Provide a centralized control plane to manage multiple Kubernetes clusters, and support the ability to propagate an app to multiple K8s clusters across different cloud providers.๐ค Kubernetes DevOps
Provide GitOps-based CD solutions and use Argo CD to provide the underlying support, collecting CD status information in real time. With the mainstream CI engine Jenkins integrated, DevOps has never been easier. Learn more.๐ Cloud Native Observability
Multi-dimensional monitoring, events and auditing logs are supported; multi-tenant log query and collection, alerting and notification are built-in. Learn more.๐งฉ Service Mesh (Istio-based)
Provide fine-grained traffic management, observability and tracing for distributed microservice applications, provides visualization for traffic topology. Learn more.๐ป App Store
Provide an App Store for Helm-based applications, and offer application lifecycle management on Kubernetes platform. Learn more.๐ก Edge Computing Platform
KubeSphere integrates KubeEdge to enable users to deploy applications on the edge devices and view logs and monitoring metrics of them on the console. Learn more.๐ Metering and Billing
Track resource consumption at different levels on a unified dashboard, which helps you make better-informed decisions on planning and reduce the cost. Learn more.๐ Support Multiple Storage and Networking Solutions
๐ Multi-tenancy
Provide unified authentication with fine-grained roles and three-tier authorization system, and support AD/LDAP authentication.๐ง GPU Workloads Scheduling and Monitoring
Create GPU workloads on the GUI, schedule GPU resources, and manage GPU resource quotas by tenant.KubeSphere uses a loosely-coupled architecture that separates the frontend from the backend. External systems can access the components of the backend through the REST APIs.
๐ KubeSphere v3.4.0 was released! It brings enhancements and better user experience, see the Release Notes For 3.4.0 for the updates.
Component | Version | K8s supported version |
---|---|---|
Alerting | N/A | 1.21,1.22,1.23,1.24,1.25,1.26 |
Auditing | v0.2.0 | 1.21,1.22,1.23,1.24,1.25,1.26 |
Monitoring | N/A | 1.21,1.22,1.23,1.24,1.25,1.26 |
DevOps | v3.4.0 | 1.21,1.22,1.23,1.24,1.25,1.26 |
EdgeRuntime | v1.13.0 | 1.21,1.22,1.23 |
Events | N/A | 1.21,1.22,1.23,1.24,1.25,1.26 |
Logging | opensearch๏ผv2.6.0 fluentbit-operator: v0.14.0 fluent-bit-tag: v1.9.4 |
1.21,1.22,1.23,1.24,1.25,1.26 |
Metrics Server | v0.4.2 | 1.21,1.22,1.23,1.24,1.25,1.26 |
Network | N/A | 1.21,1.22,1.23,1.24,1.25,1.26 |
Notification | v2.3.0 | 1.21,1.22,1.23,1.24,1.25,1.26 |
AppStore | N/A | 1.21,1.22,1.23,1.24,1.25,1.26 |
Storage | pvc-autoresizer: v0.3.0 storageclass-accessor: v0.2.2 |
1.21,1.22,1.23,1.24,1.25,1.26 |
ServiceMesh | Istio: v1.14.6 | 1.21,1.22,1.23,1.24 |
Gateway | Ingress NGINX Controller: v1.3.1 | 1.21,1.22,1.23,1.24 |
KubeSphere can run anywhere from on-premise datacenter to any cloud to edge. In addition, it can be deployed on any version-compatible Kubernetes cluster. The installer will start a minimal installation by default, you can enable other pluggable components before or after installation.
Ensure that your cluster has installed Kubernetes v1.21.x, v1.22.x, v1.23.x, * v1.24.x, * v1.25.x, or * v1.26.x. For Kubernetes versions with an asterisk, some features may be unavailable due to incompatibility.
Run the following commands to install KubeSphere on an existing Kubernetes cluster:
kubectl apply -f https://github.com/kubesphere/ks-installer/releases/download/v3.4.0/kubesphere-installer.yaml
kubectl apply -f https://github.com/kubesphere/ks-installer/releases/download/v3.4.0/cluster-configuration.yaml
๐จโ๐ป No Kubernetes? You can use KubeKey to install both KubeSphere and Kubernetes/K3s in single-node mode on your Linux machine. Let's take K3s as an example:
# Download KubeKey
curl -sfL https://get-kk.kubesphere.io | VERSION=v3.0.10 sh -
# Make kk executable
chmod +x kk
# Create a cluster
./kk create cluster --with-kubernetes v1.24.14 --container-manager containerd --with-kubesphere v3.4.0
You can run the following command to view the installation logs. After KubeSphere is successfully installed, you can
access the KubeSphere web console at http://IP:30880
and log in using the default administrator account (
admin/P@88w0rd).
kubectl logs -n kubesphere-system $(kubectl get pod -n kubesphere-system -l 'app in (ks-install, ks-installer)' -o jsonpath='{.items[0].metadata.name}') -f
KubeSphere is hosted on the following cloud providers, and you can try KubeSphere by one-click installation on their hosted Kubernetes services.
- KubeSphere for Amazon EKS
- KubeSphere for Azure AKS
- KubeSphere for DigitalOcean Kubernetes
- KubeSphere on QingCloud AppCenter(QKE)
You can also install KubeSphere on other hosted Kubernetes services within minutes, see the step-by-step guides to get started.
๐จโ๐ป No internet access? Refer to
the Air-gapped Installation on Kubernetes
or Air-gapped Installation on Linux
for instructions on how to use private registry to install KubeSphere.
We โค๏ธ your contribution. The community walks you through how to get started contributing KubeSphere. The development guide explains how to set up development environment.
๐ค Please submit any KubeSphere bugs, issues, and feature requests to KubeSphere GitHub Issue.
๐ The KubeSphere team also provides efficient official ticket support to respond in hours. For more information, click KubeSphere Online Support.
The user case studies page includes the user list of the project. You can leave a comment to let us know your use case.
ย ย
ย ย
KubeSphere is a member of CNCF and a Kubernetes Conformance Certified platform
, which enriches the CNCF CLOUD NATIVE Landscape.
For Tasks:
Click tags to check more tools for each tasksFor Jobs:
Alternative AI tools for kubesphere
Similar Open Source Tools

kubesphere
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.

dify
Dify is an open-source LLM app development platform that combines AI workflow, RAG pipeline, agent capabilities, model management, observability features, and more. It allows users to quickly go from prototype to production. Key features include: 1. Workflow: Build and test powerful AI workflows on a visual canvas. 2. Comprehensive model support: Seamless integration with hundreds of proprietary / open-source LLMs from dozens of inference providers and self-hosted solutions. 3. Prompt IDE: Intuitive interface for crafting prompts, comparing model performance, and adding additional features. 4. RAG Pipeline: Extensive RAG capabilities that cover everything from document ingestion to retrieval. 5. Agent capabilities: Define agents based on LLM Function Calling or ReAct, and add pre-built or custom tools. 6. LLMOps: Monitor and analyze application logs and performance over time. 7. Backend-as-a-Service: All of Dify's offerings come with corresponding APIs for easy integration into your own business logic.

OmAgent
OmAgent is an open-source agent framework designed to streamline the development of on-device multimodal agents. It enables agents to empower various hardware devices, integrates speed-optimized SOTA multimodal models, provides SOTA multimodal agent algorithms, and focuses on optimizing the end-to-end computing pipeline for real-time user interaction experience. Key features include easy connection to diverse devices, scalability, flexibility, and workflow orchestration. The architecture emphasizes graph-based workflow orchestration, native multimodality, and device-centricity, allowing developers to create bespoke intelligent agent programs.

swirl-search
Swirl is an open-source software that allows users to simultaneously search multiple content sources and receive AI-ranked results. It connects to various data sources, including databases, public data services, and enterprise sources, and utilizes AI and LLMs to generate insights and answers based on the user's data. Swirl is easy to use, requiring only the download of a YML file, starting in Docker, and searching with Swirl. Users can add credentials to preloaded SearchProviders to access more sources. Swirl also offers integration with ChatGPT as a configured AI model. It adapts and distributes user queries to anything with a search API, re-ranking the unified results using Large Language Models without extracting or indexing anything. Swirl includes five Google Programmable Search Engines (PSEs) to get users up and running quickly. Key features of Swirl include Microsoft 365 integration, SearchProvider configurations, query adaptation, synchronous or asynchronous search federation, optional subscribe feature, pipelining of Processor stages, results stored in SQLite3 or PostgreSQL, built-in Query Transformation support, matching on word stems and handling of stopwords, duplicate detection, re-ranking of unified results using Cosine Vector Similarity, result mixers, page through all results requested, sample data sets, optional spell correction, optional search/result expiration service, easily extensible Connector and Mixer objects, and a welcoming community for collaboration and support.

refly
Refly.AI is an open-source AI-native creation engine that empowers users to transform ideas into production-ready content. It features a free-form canvas interface with multi-threaded conversations, knowledge base integration, contextual memory, intelligent search, WYSIWYG AI editor, and more. Users can leverage AI-powered capabilities, context memory, knowledge base integration, quotes, and AI document editing to enhance their content creation process. Refly offers both cloud and self-hosting options, making it suitable for individuals, enterprises, and organizations. The tool is designed to facilitate human-AI collaboration and streamline content creation workflows.

synmetrix
Synmetrix is an open source data engineering platform and semantic layer for centralized metrics management. It provides a complete framework for modeling, integrating, transforming, aggregating, and distributing metrics data at scale. Key features include data modeling and transformations, semantic layer for unified data model, scheduled reports and alerts, versioning, role-based access control, data exploration, caching, and collaboration on metrics modeling. Synmetrix leverages Cube.js to consolidate metrics from various sources and distribute them downstream via a SQL API. Use cases include data democratization, business intelligence and reporting, embedded analytics, and enhancing accuracy in data handling and queries. The tool speeds up data-driven workflows from metrics definition to consumption by combining data engineering best practices with self-service analytics capabilities.

mlcraft
Synmetrix (prev. MLCraft) is an open source data engineering platform and semantic layer for centralized metrics management. It provides a complete framework for modeling, integrating, transforming, aggregating, and distributing metrics data at scale. Key features include data modeling and transformations, semantic layer for unified data model, scheduled reports and alerts, versioning, role-based access control, data exploration, caching, and collaboration on metrics modeling. Synmetrix leverages Cube (Cube.js) for flexible data models that consolidate metrics from various sources, enabling downstream distribution via a SQL API for integration into BI tools, reporting, dashboards, and data science. Use cases include data democratization, business intelligence, embedded analytics, and enhancing accuracy in data handling and queries. The tool speeds up data-driven workflows from metrics definition to consumption by combining data engineering best practices with self-service analytics capabilities.

ludwig
Ludwig is a declarative deep learning framework designed for scale and efficiency. It is a low-code framework that allows users to build custom AI models like LLMs and other deep neural networks with ease. Ludwig offers features such as optimized scale and efficiency, expert level control, modularity, and extensibility. It is engineered for production with prebuilt Docker containers, support for running with Ray on Kubernetes, and the ability to export models to Torchscript and Triton. Ludwig is hosted by the Linux Foundation AI & Data.

ChopperBot
A multifunctional, intelligent, personalized, scalable, easy to build, and fully automated multi platform intelligent live video editing and publishing robot. ChopperBot is a comprehensive AI tool that automatically analyzes and slices the most interesting clips from popular live streaming platforms, generates and publishes content, and manages accounts. It supports plugin DIY development and hot swapping functionality, making it easy to customize and expand. With ChopperBot, users can quickly build their own live video editing platform without the need to install any software, thanks to its visual management interface.

stockbot-on-groq
StockBot Powered by Groq is an AI-powered chatbot that provides lightning-fast responses with live interactive stock charts, financial data, news, screeners, and more. Leveraging Groq's speed and Vercel's AI SDK, StockBot offers real-time conversation with natural language processing, interactive TradingView charts, adaptive interfaces, and multi-asset market coverage. It is designed for entertainment and instructional use, not for investment advice.

lm.rs
lm.rs is a tool that allows users to run inference on Language Models locally on the CPU using Rust. It supports LLama3.2 1B and 3B models, with a WebUI also available. The tool provides benchmarks and download links for models and tokenizers, with recommendations for quantization options. Users can convert models from Google/Meta on huggingface using provided scripts. The tool can be compiled with cargo and run with various arguments for model weights, tokenizer, temperature, and more. Additionally, a backend for the WebUI can be compiled and run to connect via the web interface.

HAMi
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).

fAIr
fAIr is an open AI-assisted mapping service developed by the Humanitarian OpenStreetMap Team (HOT) to improve mapping efficiency and accuracy for humanitarian purposes. It uses AI models, specifically computer vision techniques, to detect objects like buildings, roads, waterways, and trees from satellite and UAV imagery. The service allows OSM community members to create and train their own AI models for mapping in their region of interest and ensures models are relevant to local communities. Constant feedback loop with local communities helps eliminate model biases and improve model accuracy.

rai
RAI is a framework designed to bring general multi-agent system capabilities to robots, enhancing human interactivity, flexibility in problem-solving, and out-of-the-box AI features. It supports multi-modalities, incorporates an advanced database for agent memory, provides ROS 2-oriented tooling, and offers a comprehensive task/mission orchestrator. The framework includes features such as voice interaction, customizable robot identity, camera sensor access, reasoning through ROS logs, and integration with LangChain for AI tools. RAI aims to support various AI vendors, improve human-robot interaction, provide an SDK for developers, and offer a user interface for configuration.

PhiCookBook
Phi Cookbook is a repository containing hands-on examples with Microsoft's Phi models, which are a series of open source AI models developed by Microsoft. Phi is currently the most powerful and cost-effective small language model with benchmarks in various scenarios like multi-language, reasoning, text/chat generation, coding, images, audio, and more. Users can deploy Phi to the cloud or edge devices to build generative AI applications with limited computing power.

Kiln
Kiln is an intuitive tool for fine-tuning LLM models, generating synthetic data, and collaborating on datasets. It offers desktop apps for Windows, MacOS, and Linux, zero-code fine-tuning for various models, interactive data generation, and Git-based version control. Users can easily collaborate with QA, PM, and subject matter experts, generate auto-prompts, and work with a wide range of models and providers. The tool is open-source, privacy-first, and supports structured data tasks in JSON format. Kiln is free to use and helps build high-quality AI products with datasets, facilitates collaboration between technical and non-technical teams, allows comparison of models and techniques without code, ensures structured data integrity, and prioritizes user privacy.
For similar tasks

kubesphere
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.
For similar jobs

AirGo
AirGo is a front and rear end separation, multi user, multi protocol proxy service management system, simple and easy to use. It supports vless, vmess, shadowsocks, and hysteria2.

mosec
Mosec is a high-performance and flexible model serving framework for building ML model-enabled backend and microservices. It bridges the gap between any machine learning models you just trained and the efficient online service API. * **Highly performant** : web layer and task coordination built with Rust ๐ฆ, which offers blazing speed in addition to efficient CPU utilization powered by async I/O * **Ease of use** : user interface purely in Python ๐, by which users can serve their models in an ML framework-agnostic manner using the same code as they do for offline testing * **Dynamic batching** : aggregate requests from different users for batched inference and distribute results back * **Pipelined stages** : spawn multiple processes for pipelined stages to handle CPU/GPU/IO mixed workloads * **Cloud friendly** : designed to run in the cloud, with the model warmup, graceful shutdown, and Prometheus monitoring metrics, easily managed by Kubernetes or any container orchestration systems * **Do one thing well** : focus on the online serving part, users can pay attention to the model optimization and business logic

llm-code-interpreter
The 'llm-code-interpreter' repository is a deprecated plugin that provides a code interpreter on steroids for ChatGPT by E2B. It gives ChatGPT access to a sandboxed cloud environment with capabilities like running any code, accessing Linux OS, installing programs, using filesystem, running processes, and accessing the internet. The plugin exposes commands to run shell commands, read files, and write files, enabling various possibilities such as running different languages, installing programs, starting servers, deploying websites, and more. It is powered by the E2B API and is designed for agents to freely experiment within a sandboxed environment.

pezzo
Pezzo is a fully cloud-native and open-source LLMOps platform that allows users to observe and monitor AI operations, troubleshoot issues, save costs and latency, collaborate, manage prompts, and deliver AI changes instantly. It supports various clients for prompt management, observability, and caching. Users can run the full Pezzo stack locally using Docker Compose, with prerequisites including Node.js 18+, Docker, and a GraphQL Language Feature Support VSCode Extension. Contributions are welcome, and the source code is available under the Apache 2.0 License.

learn-generative-ai
Learn Cloud Applied Generative AI Engineering (GenEng) is a course focusing on the application of generative AI technologies in various industries. The course covers topics such as the economic impact of generative AI, the role of developers in adopting and integrating generative AI technologies, and the future trends in generative AI. Students will learn about tools like OpenAI API, LangChain, and Pinecone, and how to build and deploy Large Language Models (LLMs) for different applications. The course also explores the convergence of generative AI with Web 3.0 and its potential implications for decentralized intelligence.

gcloud-aio
This repository contains shared codebase for two projects: gcloud-aio and gcloud-rest. gcloud-aio is built for Python 3's asyncio, while gcloud-rest is a threadsafe requests-based implementation. It provides clients for Google Cloud services like Auth, BigQuery, Datastore, KMS, PubSub, Storage, and Task Queue. Users can install the library using pip and refer to the documentation for usage details. Developers can contribute to the project by following the contribution guide.

fluid
Fluid is an open source Kubernetes-native Distributed Dataset Orchestrator and Accelerator for data-intensive applications, such as big data and AI applications. It implements dataset abstraction, scalable cache runtime, automated data operations, elasticity and scheduling, and is runtime platform agnostic. Key concepts include Dataset and Runtime. Prerequisites include Kubernetes version > 1.16, Golang 1.18+, and Helm 3. The tool offers features like accelerating remote file accessing, machine learning, accelerating PVC, preloading dataset, and on-the-fly dataset cache scaling. Contributions are welcomed, and the project is under the Apache 2.0 license with a vendor-neutral approach.

aiges
AIGES is a core component of the Athena Serving Framework, designed as a universal encapsulation tool for AI developers to deploy AI algorithm models and engines quickly. By integrating AIGES, you can deploy AI algorithm models and engines rapidly and host them on the Athena Serving Framework, utilizing supporting auxiliary systems for networking, distribution strategies, data processing, etc. The Athena Serving Framework aims to accelerate the cloud service of AI algorithm models and engines, providing multiple guarantees for cloud service stability through cloud-native architecture. You can efficiently and securely deploy, upgrade, scale, operate, and monitor models and engines without focusing on underlying infrastructure and service-related development, governance, and operations.