Anyscale
PaidScale your AI applications effortlessly with Ray.
A fully managed platform built on Ray, enabling developers to scale AI applications effortlessly.
What is it
Anyscale is a fully managed platform built by the creators of Ray, the world-leading framework for scaling AI and Python applications. It focuses on 'Distributed Computing,' providing the infrastructure to easily scale any AI workload from a single laptop to a massive cloud cluster.
What it can do
It simplifies the process of building, training, and serving large-scale machine learning models and LLMs. Anyscale handles all the complexities of cluster management, auto-scaling, and fault tolerance, allowing developers to focus purely on their application logic while effortlessly utilizing hundreds of GPUs for distributed tasks. It supports multimodal data curation, distributed model training, batch embedding generation, post-training workflows, and AI agent deployment.
Who is it for
It is the go-to platform for engineering teams and data scientists at large-scale technology companies and AI startups who need a robust, scalable infrastructure to manage their most demanding and complex distributed AI workloads.
Key Features
Ray Distributed Computing Engine
Anyscale provides a professional platform to build, train, and scale any Ray application from a laptop to the cloud. Powered by Ray with over 500 million downloads, 41,000+ GitHub stars, and 1,200+ contributors, it eliminates manual infrastructure management and ensures distributed machine learning projects scale horizontally with precision and efficiency.
Workspaces for Interactive Development
Build, debug, and deploy AI on scalable Ray clusters with cluster-backed VS Code, JupyterLab, and web terminals. Workspaces start in under one minute with fast dependency syncing, providing a coding agent-ready environment where developers can iterate on distributed code without changing how they write Python.
Jobs & Services for Production Workloads
Run production-grade managed Ray clusters for data processing, training, and model serving with head node resilience, automatic scaling, and A/B rollouts. Jobs handle batch workloads like model training and batch inference, while Services provide zero-downtime upgrades and high availability for online serving powered by Ray Serve.
Workload Observability & Monitoring
Monitor and debug Ray Data, Train, and Serve workloads through workload-specific dashboards backed by persistent logs. Access hardware metrics, task-level breakdowns, and profiling tools including CPU flame graphs and memory profiling, with integrations to Grafana and third-party monitoring tools of your choice.
Use Cases
Distributed AI model training at scale
ML engineers orchestrate model training across GPU clusters with elastic scaling, last-mile data preprocessing, and GPU observability, training large language models and vision models on hundreds of GPUs with frameworks like PyTorch and Ray Train.
Production ML model serving and inference
Engineering teams deploy and scale large language models and other AI models to production serving endpoints using Ray Serve on Anyscale, with zero-downtime upgrades, high availability, and dynamic autoscaling to meet serving demands.
Multimodal data curation pipelines
Data teams build large-scale pipelines for curating and preparing multimodal data across videos, images, text, and audio, using Ray Data to process and filter datasets before training with unified CPU and GPU pipelines.
Batch embedding generation
Developers process and generate embeddings at scale for downstream search, retrieval, or training use cases, distributing sentence transformer workloads across dozens of GPU workers for efficient vector pipeline processing.
Pricing plans
Frequently Asked Questions
Discussion
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