Ray AI

Overview

Ray AI is an open-source framework for building and running distributed applications. It was introduced in 2019 and is developed by Anyscale. The framework provides a set of tools and libraries that enable developers to scale Python applications from a single machine to a large cluster, making it suitable for complex AI and machine learning workloads. Ray's design abstracts away the complexities of distributed computing, offering a unified API for various tasks such as data processing, model training, hyperparameter tuning, and real-time model serving.

Ray is primarily designed for developers and technical buyers who need to execute large-scale machine learning tasks efficiently. It supports distributed deep learning training, allowing models to be trained across multiple GPUs and machines. For example, Ray Train facilitates distributed training with popular frameworks like PyTorch and TensorFlow. Hyperparameter tuning at scale is addressed by Ray Tune, which can orchestrate thousands of trials concurrently. Real-time model serving is managed by Ray Serve, which can deploy and scale models as microservices.

The framework's core strength lies in its ability to unify the machine learning lifecycle by providing a consistent programming model across different stages. This approach aims to reduce the overhead associated with integrating disparate tools for distributed data processing, model training, and deployment. Ray's Python-native interface is intended to streamline the developer experience, allowing ML engineers to transition from local development to distributed environments with minimal code changes. Its architecture supports fault tolerance and dynamic cluster scaling, which are critical for robust production AI systems. The framework's modular nature, with components like Ray Data for distributed data processing and Ray RLlib for reinforcement learning, allows users to adopt specific functionalities as needed for their specific use cases.

Key features

• Ray Core: Provides the foundational distributed computing primitives, including tasks for stateless functions and actors for stateful computations, enabling parallel execution across a cluster.
• Ray Data: A library for scalable data loading and processing, designed to handle large datasets and integrate with various data sources for machine learning pipelines.
• Ray Train: Facilitates distributed training of machine learning models using popular frameworks such as PyTorch, TensorFlow, and Hugging Face Transformers, enabling multi-GPU and multi-node training.
• Ray Tune: A scalable library for hyperparameter optimization, supporting various search algorithms and integrating with machine learning frameworks to efficiently find optimal model configurations.
• Ray Serve: Enables the deployment and serving of machine learning models as scalable, production-ready microservices, supporting online inference and model composition.
• Ray RLlib: A library for reinforcement learning, offering a unified API for a range of algorithms and environments, designed for scalable experimentation and training.
• Python-Native API: Offers a consistent and intuitive Python API that minimizes the learning curve for developers accustomed to single-node Python programming.
• Fault Tolerance: Includes mechanisms to handle node failures and task retries, contributing to the reliability of distributed applications.
• Dynamic Scaling: Supports elastic scaling of clusters, allowing resources to be added or removed based on workload demands.

Pricing

Ray AI is an open-source framework that can be self-hosted without direct cost for the software itself. Anyscale, the company behind Ray, offers a managed service called Anyscale Cloud for deploying and managing Ray clusters. The pricing for Anyscale Cloud is usage-based, scaling with compute consumption and data egress. As of May 2026, Anyscale Cloud provides a free tier, with paid plans based on resource consumption.

For detailed and current pricing information, refer to the Anyscale Cloud pricing page.

Common integrations

• PyTorch: Ray Train integrates with PyTorch for distributed deep learning training, as detailed in the Ray PyTorch example.
• TensorFlow: Ray supports distributed training with TensorFlow, allowing users to scale TensorFlow models across clusters.
• Hugging Face Transformers: Ray Train can be used to distribute the training of models from the Hugging Face Transformers library.
• Scikit-learn: Ray can parallelize Scikit-learn workloads, such as hyperparameter tuning with Ray Tune.
• MLflow: Integration with MLflow for experiment tracking and model management, enabling logging of Ray Tune experiments.
• Data Sources (e.g., S3, GCS, HDFS): Ray Data can read from and write to various distributed storage systems.
• Kubernetes: Ray clusters can be deployed and managed on Kubernetes for containerized orchestration, as outlined in the Ray Kubernetes documentation.

Alternatives

• Apache Spark: An open-source unified analytics engine for large-scale data processing, often used for ETL, streaming, and machine learning workloads.
• Dask: A flexible library for parallel computing in Python, providing parallel collections like Dask DataFrames and Dask Arrays.
• Kubeflow: A machine learning toolkit for Kubernetes, providing components for deploying, managing, and scaling ML workflows on Kubernetes.

Getting started

To begin using Ray, you can install it via pip and run a simple distributed task. The following Python example demonstrates how to define a remote function and execute it in parallel using Ray Core.

import ray

# Initialize Ray
# If running on a cluster, ray.init() connects to the existing cluster.
# For local development, it starts a local Ray instance.
ray.init()

# Define a remote function using the @ray.remote decorator
@ray.remote
def multiply(a, b):
    return a * b

# Call the remote function. It returns an ObjectRef immediately.
# The actual computation runs in the background on a Ray worker.
future_result = multiply.remote(10, 20)

# Retrieve the actual result from the ObjectRef
result = ray.get(future_result)

print(f"The result of 10 * 20 is: {result}")

# Define another remote function and call it multiple times in parallel
@ray.remote
def increment(x):
    return x + 1

# Create a list of ObjectRefs for parallel execution
results_futures = [increment.remote(i) for i in range(5)]

# Retrieve all results in order
results = ray.get(results_futures)

print(f"Results of parallel increments: {results}")

# Shutdown Ray (optional, typically done when the script exits)
ray.shutdown()

This example initializes Ray, defines two simple functions as remote Ray tasks, executes them, and retrieves their results. The @ray.remote decorator transforms a regular Python function into a remote function that can be executed as a task on a Ray worker. The .remote() call immediately returns an ObjectRef, which is a future representing the result. ray.get() is then used to block until the result is available. This demonstrates the basic mechanism for distributing computations with Ray Core. For more complex use cases involving data processing, training, or serving, refer to the official Ray documentation.