
skypilot
Run, manage, and scale AI workloads on any AI infrastructure. Use one system to access & manage all AI compute (Kubernetes, 17+ clouds, or on-prem).
Stars: 8755

SkyPilot is a framework for running LLMs, AI, and batch jobs on any cloud, offering maximum cost savings, highest GPU availability, and managed execution. SkyPilot abstracts away cloud infra burdens: - Launch jobs & clusters on any cloud - Easy scale-out: queue and run many jobs, automatically managed - Easy access to object stores (S3, GCS, R2) SkyPilot maximizes GPU availability for your jobs: * Provision in all zones/regions/clouds you have access to (the _Sky_), with automatic failover SkyPilot cuts your cloud costs: * Managed Spot: 3-6x cost savings using spot VMs, with auto-recovery from preemptions * Optimizer: 2x cost savings by auto-picking the cheapest VM/zone/region/cloud * Autostop: hands-free cleanup of idle clusters SkyPilot supports your existing GPU, TPU, and CPU workloads, with no code changes.
README:
🔥 News 🔥
- [Aug 2025] Serve and finetune OpenAI GPT-OSS models (gpt-oss-120b, gpt-oss-20b) with one command on any infra: serve + LoRA and full finetuning
- [Jul 2025] Run large-scale LLM training with TorchTitan on any cloud: example
- [Jul 2025] Run distributed RL training for LLMs with Verl (PPO, GRPO) on any cloud: example
- [Jul 2025] 🎉 SkyPilot v0.10.0 released! blog post, release notes
- [Jul 2025] Finetune Llama4 on any distributed cluster/cloud: example
- [Jul 2025] Two-part blog series,
The Evolution of AI Job Orchestration
: (1) Running AI jobs on GPU Neoclouds, (2) The AI-Native Control Plane & Orchestration that Finally Works for ML - [Apr 2025] Spin up Qwen3 on your cluster/cloud: example
- [Feb 2025] Prepare and serve Retrieval Augmented Generation (RAG) with DeepSeek-R1: blog post, example
LLM Finetuning Cookbooks: Finetuning Llama 2 / Llama 3.1 in your own cloud environment, privately: Llama 2 example and blog; Llama 3.1 example and blog
SkyPilot is a system to run, manage, and scale AI workloads on any AI infrastructure.
SkyPilot gives AI teams a simple interface to run jobs on any infra. Infra teams get a unified control plane to manage any AI compute — with advanced scheduling, scaling, and orchestration.

SkyPilot is easy to use for AI teams:
- Quickly spin up compute on your own infra
- Environment and job as code — simple and portable
- Easy job management: queue, run, and auto-recover many jobs
SkyPilot makes Kubernetes easy for AI & Infra teams:
- Slurm-like ease of use, cloud-native robustness
- Local dev experience on K8s: SSH into pods, sync code, or connect IDE
- Turbocharge your clusters: gang scheduling, multi-cluster, and scaling
SkyPilot unifies multiple clusters, clouds, and hardware:
- One interface to use reserved GPUs, Kubernetes clusters, or 16+ clouds
- Flexible provisioning of GPUs, TPUs, CPUs, with auto-retry
- Team deployment and resource sharing
SkyPilot cuts your cloud costs & maximizes GPU availability:
- Autostop: automatic cleanup of idle resources
- Spot instance support: 3-6x cost savings, with preemption auto-recovery
- Intelligent scheduling: automatically run on the cheapest & most available infra
SkyPilot supports your existing GPU, TPU, and CPU workloads, with no code changes.
Install with pip:
# Choose your clouds:
pip install -U "skypilot[kubernetes,aws,gcp,azure,oci,nebius,lambda,runpod,fluidstack,paperspace,cudo,ibm,scp,seeweb]"
To get the latest features and fixes, use the nightly build or install from source:
# Choose your clouds:
pip install "skypilot-nightly[kubernetes,aws,gcp,azure,oci,nebius,lambda,runpod,fluidstack,paperspace,cudo,ibm,scp,seeweb]"
Current supported infra: Kubernetes, AWS, GCP, Azure, OCI, Nebius, Lambda Cloud, RunPod, Fluidstack, Cudo, Digital Ocean, Paperspace, Cloudflare, Samsung, IBM, Vast.ai, VMware vSphere, Seeweb.
You can find our documentation here.
A SkyPilot task specifies: resource requirements, data to be synced, setup commands, and the task commands.
Once written in this unified interface (YAML or Python API), the task can be launched on any available infra (Kubernetes, cloud, etc.). This avoids vendor lock-in, and allows easily moving jobs to a different provider.
Paste the following into a file my_task.yaml
:
resources:
accelerators: A100:8 # 8x NVIDIA A100 GPU
num_nodes: 1 # Number of VMs to launch
# Working directory (optional) containing the project codebase.
# Its contents are synced to ~/sky_workdir/ on the cluster.
workdir: ~/torch_examples
# Commands to be run before executing the job.
# Typical use: pip install -r requirements.txt, git clone, etc.
setup: |
cd mnist
pip install -r requirements.txt
# Commands to run as a job.
# Typical use: launch the main program.
run: |
cd mnist
python main.py --epochs 1
Prepare the workdir by cloning:
git clone https://github.com/pytorch/examples.git ~/torch_examples
Launch with sky launch
(note: access to GPU instances is needed for this example):
sky launch my_task.yaml
SkyPilot then performs the heavy-lifting for you, including:
- Find the cheapest & available infra across your clusters or clouds
- Provision the GPUs (pods or VMs), with auto-failover if the infra returned capacity errors
- Sync your local
workdir
to the provisioned cluster - Auto-install dependencies by running the task's
setup
commands - Run the task's
run
commands, and stream logs
See Quickstart to get started with SkyPilot.
See SkyPilot examples that cover: development, training, serving, LLM models, AI apps, and common frameworks.
Latest featured examples:
Task | Examples |
---|---|
Training | Verl, Finetune Llama 4, TorchTitan, PyTorch, DeepSpeed, NeMo, Ray, Unsloth, Jax/TPU |
Serving | vLLM, SGLang, Ollama |
Models | DeepSeek-R1, Llama 4, Llama 3, CodeLlama, Qwen, Kimi-K2, Mixtral |
AI apps | RAG, vector databases (ChromaDB, CLIP) |
Common frameworks | Airflow, Jupyter |
Source files can be found in llm/
and examples/
.
To learn more, see SkyPilot Overview, SkyPilot docs, and SkyPilot blog.
SkyPilot adopters: Testimonials and Case Studies
Partners and integrations: Community Spotlights
Follow updates:
Read the research:
- SkyPilot paper and talk (NSDI 2023)
- Sky Computing whitepaper
- Sky Computing vision paper (HotOS 2021)
- SkyServe: AI serving across regions and clouds (EuroSys 2025)
- Managed jobs spot instance policy (NSDI 2024)
SkyPilot was initially started at the Sky Computing Lab at UC Berkeley and has since gained many industry contributors. To read about the project's origin and vision, see Concept: Sky Computing.
We are excited to hear your feedback:
- For issues and feature requests, please open a GitHub issue.
- For questions, please use GitHub Discussions.
For general discussions, join us on the SkyPilot Slack.
We welcome all contributions to the project! See CONTRIBUTING for how to get involved.
For Tasks:
Click tags to check more tools for each tasksFor Jobs:
Alternative AI tools for skypilot
Similar Open Source Tools

skypilot
SkyPilot is a framework for running LLMs, AI, and batch jobs on any cloud, offering maximum cost savings, highest GPU availability, and managed execution. SkyPilot abstracts away cloud infra burdens: - Launch jobs & clusters on any cloud - Easy scale-out: queue and run many jobs, automatically managed - Easy access to object stores (S3, GCS, R2) SkyPilot maximizes GPU availability for your jobs: * Provision in all zones/regions/clouds you have access to (the _Sky_), with automatic failover SkyPilot cuts your cloud costs: * Managed Spot: 3-6x cost savings using spot VMs, with auto-recovery from preemptions * Optimizer: 2x cost savings by auto-picking the cheapest VM/zone/region/cloud * Autostop: hands-free cleanup of idle clusters SkyPilot supports your existing GPU, TPU, and CPU workloads, with no code changes.

ApeRAG
ApeRAG is a production-ready platform for Retrieval-Augmented Generation (RAG) that combines Graph RAG, vector search, and full-text search with advanced AI agents. It is ideal for building Knowledge Graphs, Context Engineering, and deploying intelligent AI agents for autonomous search and reasoning across knowledge bases. The platform offers features like advanced index types, intelligent AI agents with MCP support, enhanced Graph RAG with entity normalization, multimodal processing, hybrid retrieval engine, MinerU integration for document parsing, production-grade deployment with Kubernetes, enterprise management features, MCP integration, and developer-friendly tools for customization and contribution.

promptfoo
Promptfoo is a tool for testing and evaluating LLM output quality. With promptfoo, you can build reliable prompts, models, and RAGs with benchmarks specific to your use-case, speed up evaluations with caching, concurrency, and live reloading, score outputs automatically by defining metrics, use as a CLI, library, or in CI/CD, and use OpenAI, Anthropic, Azure, Google, HuggingFace, open-source models like Llama, or integrate custom API providers for any LLM API.

instill-core
Instill Core is an open-source orchestrator comprising a collection of source-available projects designed to streamline every aspect of building versatile AI features with unstructured data. It includes Instill VDP (Versatile Data Pipeline) for unstructured data, AI, and pipeline orchestration, Instill Model for scalable MLOps and LLMOps for open-source or custom AI models, and Instill Artifact for unified unstructured data management. Instill Core can be used for tasks such as building, testing, and sharing pipelines, importing, serving, fine-tuning, and monitoring ML models, and transforming documents, images, audio, and video into a unified AI-ready format.

midscene
Midscene.js is an AI-powered automation SDK that allows users to control web pages, perform assertions, and extract data in JSON format using natural language. It offers features such as natural language interaction, understanding UI and providing responses in JSON, intuitive assertion based on AI understanding, compatibility with public multimodal LLMs like GPT-4o, visualization tool for easy debugging, and a brand new experience in automation development.

kornia
Kornia is a differentiable computer vision library for PyTorch. It consists of a set of routines and differentiable modules to solve generic computer vision problems. At its core, the package uses PyTorch as its main backend both for efficiency and to take advantage of the reverse-mode auto-differentiation to define and compute the gradient of complex functions.

Mira
Mira is an agentic AI library designed for automating company research by gathering information from various sources like company websites, LinkedIn profiles, and Google Search. It utilizes a multi-agent architecture to collect and merge data points into a structured profile with confidence scores and clear source attribution. The core library is framework-agnostic and can be integrated into applications, pipelines, or custom workflows. Mira offers features such as real-time progress events, confidence scoring, company criteria matching, and built-in services for data gathering. The tool is suitable for users looking to streamline company research processes and enhance data collection efficiency.

TaskingAI
TaskingAI brings Firebase's simplicity to **AI-native app development**. The platform enables the creation of GPTs-like multi-tenant applications using a wide range of LLMs from various providers. It features distinct, modular functions such as Inference, Retrieval, Assistant, and Tool, seamlessly integrated to enhance the development process. TaskingAI’s cohesive design ensures an efficient, intelligent, and user-friendly experience in AI application development.

ai-flow
AI Flow is an open-source, user-friendly UI application that empowers you to seamlessly connect multiple AI models together, specifically leveraging the capabilities of multiples AI APIs such as OpenAI, StabilityAI and Replicate. In a nutshell, AI Flow provides a visual platform for crafting and managing AI-driven workflows, thereby facilitating diverse and dynamic AI interactions.

ClaudeSync
ClaudeSync is a powerful tool designed to seamlessly synchronize local files with Claude.ai projects. It bridges the gap between local development environment and Claude.ai's knowledge base, offering real-time synchronization, CLI for easy management, support for multiple organizations and projects, intelligent file filtering, configurable sync interval, two-way synchronization, and more. It ensures data privacy, open source transparency, and comes with disclaimers for use at own risk. Users can quickly start syncing by installing, logging in, selecting organization and project, and running sync. Advanced features include API, organization, project, file, chat management, configuration, synchronization modes, scheduled sync, providers, custom ignore file, and troubleshooting. Contributions are welcome, and communication channels include GitHub Issues and Discord. Licensed under MIT License.

chatnio
Chat Nio is a next-generation AIGC one-stop business solution that combines the advantages of frontend-oriented lightweight deployment projects with powerful API distribution systems. It offers rich model support, beautiful UI design, complete Markdown support, multi-theme support, internationalization support, text-to-image support, powerful conversation sync, model market & preset system, rich file parsing, full model internet search, Progressive Web App (PWA) support, comprehensive backend management, multiple billing methods, innovative model caching, and additional features. The project aims to address limitations in conversation synchronization, billing, file parsing, conversation URL sharing, channel management, and API call support found in existing AIGC commercial sites, while also providing a user-friendly interface design and C-end features.

LibreChat
LibreChat is an all-in-one AI conversation platform that integrates multiple AI models, including ChatGPT, into a user-friendly interface. It offers a wide range of features, including multimodal chat, multilingual UI, AI model selection, custom presets, conversation branching, message export, search, plugins, multi-user support, and extensive configuration options. LibreChat is open-source and community-driven, with a focus on providing a free and accessible alternative to ChatGPT Plus. It is designed to enhance productivity, creativity, and communication through advanced AI capabilities.

ai-optimizer
The Oracle AI Optimizer and Toolkit provides a streamlined environment for developers and data scientists to explore Generative Artificial Intelligence (GenAI) and Retrieval-Augmented Generation (RAG) capabilities. It integrates Oracle Database 23ai AI VectorSearch and SelectAI to enhance Large Language Models (LLMs) through RAG.

Learn_Prompting
Learn Prompting is a platform offering free resources, courses, and webinars to master prompt engineering and generative AI. It provides a Prompt Engineering Guide, courses on Generative AI, workshops, and the HackAPrompt competition. The platform also offers AI Red Teaming and AI Safety courses, research reports on prompting techniques, and welcomes contributions in various forms such as content suggestions, translations, artwork, and typo fixes. Users can locally develop the website using Visual Studio Code, Git, and Node.js, and run it in development mode to preview changes.

logto
Logto is a modern, open-source authentication infrastructure designed for SaaS and AI applications. It simplifies OIDC and OAuth 2.1 implementation, enabling secure, production-ready authentication with features like multi-tenancy, enterprise SSO, and RBAC. Logto offers pre-built sign-in flows, customizable UIs, and SDKs for various frameworks, supporting protocols like OIDC, OAuth 2.1, and SAML. It is suitable for teams scaling SaaS, AI, and agent-based platforms without authentication complexities.

griptape
Griptape is a modular Python framework for building AI-powered applications that securely connect to your enterprise data and APIs. It offers developers the ability to maintain control and flexibility at every step. Griptape's core components include Structures (Agents, Pipelines, and Workflows), Tasks, Tools, Memory (Conversation Memory, Task Memory, and Meta Memory), Drivers (Prompt and Embedding Drivers, Vector Store Drivers, Image Generation Drivers, Image Query Drivers, SQL Drivers, Web Scraper Drivers, and Conversation Memory Drivers), Engines (Query Engines, Extraction Engines, Summary Engines, Image Generation Engines, and Image Query Engines), and additional components (Rulesets, Loaders, Artifacts, Chunkers, and Tokenizers). Griptape enables developers to create AI-powered applications with ease and efficiency.
For similar tasks

agent-os
The Agent OS is an experimental framework and runtime to build sophisticated, long running, and self-coding AI agents. We believe that the most important super-power of AI agents is to write and execute their own code to interact with the world. But for that to work, they need to run in a suitable environment—a place designed to be inhabited by agents. The Agent OS is designed from the ground up to function as a long-term computing substrate for these kinds of self-evolving agents.

AISystem
This open-source project, also known as **Deep Learning System** or **AI System (AISys)**, aims to explore and learn about the system design of artificial intelligence and deep learning. The project is centered around the full-stack content of AI systems that ZOMI has accumulated,整理, and built during his work. The goal is to collaborate with all friends who are interested in AI open-source projects to jointly promote learning and discussion.

skypilot
SkyPilot is a framework for running LLMs, AI, and batch jobs on any cloud, offering maximum cost savings, highest GPU availability, and managed execution. SkyPilot abstracts away cloud infra burdens: - Launch jobs & clusters on any cloud - Easy scale-out: queue and run many jobs, automatically managed - Easy access to object stores (S3, GCS, R2) SkyPilot maximizes GPU availability for your jobs: * Provision in all zones/regions/clouds you have access to (the _Sky_), with automatic failover SkyPilot cuts your cloud costs: * Managed Spot: 3-6x cost savings using spot VMs, with auto-recovery from preemptions * Optimizer: 2x cost savings by auto-picking the cheapest VM/zone/region/cloud * Autostop: hands-free cleanup of idle clusters SkyPilot supports your existing GPU, TPU, and CPU workloads, with no code changes.

BentoML
BentoML is an open-source model serving library for building performant and scalable AI applications with Python. It comes with everything you need for serving optimization, model packaging, and production deployment.

council
Council is an open-source platform designed for the rapid development and deployment of customized generative AI applications using teams of agents. It extends the LLM tool ecosystem by providing advanced control flow and scalable oversight for AI agents. Users can create sophisticated agents with predictable behavior by leveraging Council's powerful approach to control flow using Controllers, Filters, Evaluators, and Budgets. The framework allows for automated routing between agents, comparing, evaluating, and selecting the best results for a task. Council aims to facilitate packaging and deploying agents at scale on multiple platforms while enabling enterprise-grade monitoring and quality control.

LazyLLM
LazyLLM is a low-code development tool for building complex AI applications with multiple agents. It assists developers in building AI applications at a low cost and continuously optimizing their performance. The tool provides a convenient workflow for application development and offers standard processes and tools for various stages of application development. Users can quickly prototype applications with LazyLLM, analyze bad cases with scenario task data, and iteratively optimize key components to enhance the overall application performance. LazyLLM aims to simplify the AI application development process and provide flexibility for both beginners and experts to create high-quality applications.

spring-ai-alibaba
Spring AI Alibaba is an AI application framework for Java developers that seamlessly integrates with Alibaba Cloud QWen LLM services and cloud-native infrastructures. It provides features like support for various AI models, high-level AI agent abstraction, function calling, and RAG support. The framework aims to simplify the development, evaluation, deployment, and observability of AI native Java applications. It offers open-source framework and ecosystem integrations to support features like prompt template management, event-driven AI applications, and more.

AimRT
AimRT is a basic runtime framework for modern robotics, developed in modern C++ with lightweight and easy deployment. It integrates research and development for robot applications in various deployment scenarios, providing debugging tools and observability support. AimRT offers a plug-in development interface compatible with ROS2, HTTP, Grpc, and other ecosystems for progressive system upgrades.
For similar jobs

weave
Weave is a toolkit for developing Generative AI applications, built by Weights & Biases. With Weave, you can log and debug language model inputs, outputs, and traces; build rigorous, apples-to-apples evaluations for language model use cases; and organize all the information generated across the LLM workflow, from experimentation to evaluations to production. Weave aims to bring rigor, best-practices, and composability to the inherently experimental process of developing Generative AI software, without introducing cognitive overhead.

LLMStack
LLMStack is a no-code platform for building generative AI agents, workflows, and chatbots. It allows users to connect their own data, internal tools, and GPT-powered models without any coding experience. LLMStack can be deployed to the cloud or on-premise and can be accessed via HTTP API or triggered from Slack or Discord.

VisionCraft
The VisionCraft API is a free API for using over 100 different AI models. From images to sound.

kaito
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.

PyRIT
PyRIT is an open access automation framework designed to empower security professionals and ML engineers to red team foundation models and their applications. It automates AI Red Teaming tasks to allow operators to focus on more complicated and time-consuming tasks and can also identify security harms such as misuse (e.g., malware generation, jailbreaking), and privacy harms (e.g., identity theft). The goal is to allow researchers to have a baseline of how well their model and entire inference pipeline is doing against different harm categories and to be able to compare that baseline to future iterations of their model. This allows them to have empirical data on how well their model is doing today, and detect any degradation of performance based on future improvements.

tabby
Tabby is a self-hosted AI coding assistant, offering an open-source and on-premises alternative to GitHub Copilot. It boasts several key features: * Self-contained, with no need for a DBMS or cloud service. * OpenAPI interface, easy to integrate with existing infrastructure (e.g Cloud IDE). * Supports consumer-grade GPUs.

spear
SPEAR (Simulator for Photorealistic Embodied AI Research) is a powerful tool for training embodied agents. It features 300 unique virtual indoor environments with 2,566 unique rooms and 17,234 unique objects that can be manipulated individually. Each environment is designed by a professional artist and features detailed geometry, photorealistic materials, and a unique floor plan and object layout. SPEAR is implemented as Unreal Engine assets and provides an OpenAI Gym interface for interacting with the environments via Python.

Magick
Magick is a groundbreaking visual AIDE (Artificial Intelligence Development Environment) for no-code data pipelines and multimodal agents. Magick can connect to other services and comes with nodes and templates well-suited for intelligent agents, chatbots, complex reasoning systems and realistic characters.