Anyscale / anyscale.com
Managed platform for Ray, the open source distributed computing framework for AI/ML, enabling scalable model training, inference serving, and data processing across cloud compute clusters.
Pricing
Free
Free plan
Yes
Category
Developer Tools
Platforms
4
Free plan
Yes
API access
Yes
Open source
Yes
Platforms
4
Anyscale is the company behind Ray, one of the most widely used open source frameworks for distributed AI/ML computation. Ray abstracts the complexity of distributing Python code across multiple machines — training large models across GPU clusters, running parallel hyperparameter searches, serving models with auto-scaling, and processing large datasets — behind a simple Python API.
Ray Core provides the fundamental distributed computing primitives — remote functions, actors, and object stores — that allow any Python code to run distributed across a cluster. For ML teams who need to scale computation beyond a single machine, Ray Core is the layer that makes distribution practical without learning Spark or Kubernetes internals.
Ray Train handles distributed ML training, providing integrations with PyTorch, TensorFlow, Hugging Face, and XGBoost that distribute training across GPU clusters. Teams training large models or running many parallel training jobs use Ray Train to utilise multiple machines without custom distributed training code.
Ray Serve is the model serving component — a framework for deploying and scaling ML models as APIs with support for batching, replicas, resource allocation, and request routing. Large-scale production inference workloads use Ray Serve for its ability to compose pipelines and scale individual components independently.
Anyscale provides managed Ray — the cloud platform that handles cluster provisioning, scaling, monitoring, and job management for Ray workloads. Used by OpenAI, Spotify, Instacart, and many others for large-scale distributed AI.
Anyscale runs as ml platform software built around code and data workflows. Users typically start with a prompt, upload, or connected data source, and the underlying model handles the heavy lifting before returning a result you can refine or export. It's available on python, cli, and kubernetes, with API access for teams that want to embed it into their own products.
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The capabilities that matter most for teams evaluating Anyscale.
Distributed computing primitives (remote functions, actors, object stores) that distribute any Python code across multiple machines behind a simple decorator API.
Distributed ML training integrations for PyTorch, TensorFlow, Hugging Face, and XGBoost that distribute training across GPU clusters without custom distributed training code.
Distributed model serving framework for deploying ML models as scalable APIs with batching, replicas, and pipeline composition at production scale.
Ray open source is free. Anyscale cloud platform usage-based from $0.001/CPU-hour. Enterprise custom.
Model
Usage-based
Starting price
Free
Free trial
No
Apache Spark is the alternative for large-scale data processing. Kubeflow provides ML pipelines on Kubernetes. SageMaker provides managed ML infrastructure on AWS. Horovod is a distributed training alternative for PyTorch and TensorFlow.
A side-by-side look at the closest alternative in this category.
Key facts about model providers, platforms, and team support.
Model Provider
Anyscale
Platforms
Python, CLI, Kubernetes, Cloud
Deployment
Open Source, SaaS, Cloud
Integrations
PyTorch, TensorFlow, Hugging Face, XGBoost, Spark, AWS, GCP, Azure, API
Team Collaboration
No
Launch Year
2023
Compliance signals and data-handling notes as reported by the vendor.
SOC 2 Type II. ISO 27001. GDPR compliant. Anyscale cloud enterprise data handling agreements. Self-hosted Ray keeps data on customer infrastructure.
Ray open source self-hosted keeps all computation data on customer infrastructure. Anyscale cloud processes job data on Anyscale-managed infrastructure.
Editorial Verdict
Anyscale/Ray is the essential distributed AI/ML platform for teams training large models across GPU clusters or serving AI at scale, with open source Ray providing infrastructure independence and Anyscale providing managed convenience.
Last verified July 24, 2026.
Ray open source is free. Anyscale cloud platform usage-based from $0.001/CPU-hour. Enterprise custom.
Open source and free. Tecton provides managed enterprise Feast from custom pricing. Cloud-provider managed versions available.
SOC 2 Type II. ISO 27001. GDPR compliant. Anyscale cloud enterprise data handling agreements. Self-hosted Ray keeps data on customer infrastructure.
Open source self-hosted keeps feature data on customer infrastructure. Tecton managed enterprise subject to Tecton's data handling. Enterprise agreements available.
Ray open source self-hosted keeps all computation data on customer infrastructure. Anyscale cloud processes job data on Anyscale-managed infrastructure.
Feature data stored in customer-controlled backends (Redis, DynamoDB, BigQuery). Feast does not access underlying data — customers control storage and access. Tecton managed has separate data handling.
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