What is Ray?
Ray is the open-source framework that simplifies and optimizes AI and machine learning workloads. Built for developers, it’s designed to handle the growing complexity of AI—whether you’re training large models, processing multi-modal data, or deploying production-ready solutions. With Ray, you can scale seamlessly from your laptop to thousands of GPUs, all while maximizing resource utilization and minimizing costs.
Why Ray?
AI is evolving faster than ever, and managing its complexity is a challenge. Teams often struggle with slow production timelines, underutilized resources, and skyrocketing costs. Ray solves these problems by acting as your AI Compute Engine, unifying infrastructure for any workload—AI, ML, or Gen AI.
Key Features
🌟 Parallel Python Code
Scale and distribute Python applications effortlessly. Whether you’re running simulations, backtesting, or other compute-heavy tasks, Ray makes it easy to parallelize your code with minimal changes.
🌟 Multi-Modal Data Processing
Handle structured and unstructured data—images, videos, audio, and more—with ease. Ray’s framework-agnostic approach ensures compatibility with your existing tools.
🌟 Distributed Model Training
Train models at scale, from traditional ML models like XGBoost to Gen AI foundation models. Ray supports distributed training with just one line of code, integrating seamlessly with your preferred frameworks.
🌟 Model Serving
Deploy models efficiently with Ray Serve. Its independent scaling and fractional resource allocation ensure optimal performance for any ML model, from LLMs to stable diffusion models.
🌟 Batch Inference
Optimize offline batch inference workflows by leveraging heterogeneous compute. Use CPUs and GPUs in the same pipeline to maximize utilization and reduce costs.
🌟 Reinforcement Learning
Run production-level reinforcement learning workflows with Ray RLlib. Its unified APIs simplify complex RL tasks for a wide range of applications.
🌟 Gen AI Workflows
Build end-to-end Gen AI applications, including multimodal models and RAG (Retrieval-Augmented Generation) pipelines, with Ray’s flexible infrastructure.
🌟 LLM Inference & Fine-Tuning
Scale Large Language Model (LLM) inference seamlessly and fine-tune models efficiently, even for the most demanding workloads.
Who is Ray For?
🔹 Data Scientists & ML Practitioners
Scale ML workloads without needing deep infrastructure expertise. Ray lets you focus on building models while it handles the complexities of distributed computing.
🔹 ML Platform Builders & Engineers
Create scalable, robust ML platforms with Ray’s unified API. Simplify onboarding and integration with the broader ML ecosystem, reducing friction between development and production.
🔹 Distributed Systems Engineers
Automate orchestration, scheduling, fault tolerance, and auto-scaling with Ray’s distributed computing primitives.
Real-World Results
Ray delivers measurable impact for teams tackling AI at scale:
10-100x more model training dataprocessed.
1M+ CPU coresdeployed for online model serving.
300B+ parameterstrained for foundation models.
82% lower data processing costs, saving $120M annually.
30x cost reductionswitching from Spark to Ray for batch inference.
4x improvement in GPU utilizationand 7x lower costs.
How It Works
Ray’s unified compute framework consists of three layers:
Ray AI Libraries: Scalable, domain-specific libraries for ML tasks like data processing, training, and serving.
Ray Core: General-purpose distributed computing primitives for scaling Python applications.
Ray Clusters: Flexible, auto-scaling clusters that run on any infrastructure—cloud, on-premise, or Kubernetes.
More information on Ray
Launched
2013-01
Pricing Model
Free
Starting Price
Global Rank
221971
Follow
Month Visit
190.9K
Tech used
Google Tag Manager,HubSpot Analytics,Next.js,Gzip,OpenGraph,Progressive Web App,Webpack,Cowboy
Top 5 Countries
31.63%
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3.7%
3.68%
3.02%
China
United States
Canada
India
Hong Kong
Traffic Sources
51.34%
39.08%
7.76%
1.47%
0.29%
0.07%
Search
Direct
Referrals
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Paid Referrals
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Ray was manually vetted by our editorial team and was first featured on September 4th 2025.
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