Buying snapshot
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| Decision point | What the Ray project states |
|---|---|
License | Open source under the Apache-2.0 license |
How it is installed | pip install ray; libraries come as extras, such as "ray[serve]" and "ray[train]" |
Where it runs | Any machine, cluster, cloud provider and Kubernetes; the cluster launcher covers major public clouds, K8s and bare-metal on-premise machines, and a Kubernetes Operator deploys Serve apps to K8s |
Managed option | Anyscale, a fully managed AI platform for Ray, built by the minds behind it; what it adds is under Pricing |
Security default | Token authentication on by default for local clusters from Ray 2.59, and for all clusters from Ray 2.61 |
Community support | Estimated response: Discourse Forum < 1 day; GitHub Issues < 2 days (Ray OSS Team); Slack < 2 days; StackOverflow 3-5 days |
Our take
Choose Ray if your team writes Python and needs training, serving or batch work spread across many machines, and you want one open-source runtime on infrastructure you control. If you would rather not run the clusters yourself, look at Anyscale, the managed option.
Evaluate Ray
Comparisons
Where it stands out
One runtime, several libraries. Data, Train, Tune, RLlib and Serve sit on Ray Core, whose tasks, actors and objects are the building blocks for your own distributed Python code. The project says these libraries scale data processing, training and serving.
Same code, laptop to cluster. The project says you can scale the same Python code from a laptop to a cluster with no other infrastructure required, so you can test locally before you deploy.
Wide framework support for training. Ray Train works with PyTorch, TensorFlow, PyTorch Lightning, Keras, Hugging Face Transformers and Accelerate, Horovod, XGBoost, DeepSpeed and LightGBM, with guides for JAX.
Serving built for composition. Ray Serve is framework-agnostic and combines several models and business logic in Python. For LLMs it offers response streaming, dynamic request batching and multi-node/multi-GPU serving; fractional GPUs let many models share hardware.
Per-step scaling. Serve scales a model up and down by replica count and batches requests, and each application step can autoscale on its own.
Mixed CPU and GPU pipelines. Ray can use heterogeneous GPUs and CPUs with independent scaling, and batch inference can use CPUs and GPUs in the same pipeline.
Built-in tools for operators. The Ray Dashboard monitors apps and clusters, and the Ray Distributed Debugger helps debug Ray apps.
Product screenshot
Trade-offs to weigh
Serving, not a full ML platform. By the project's own account, Ray Serve lacks functions for managing the lifecycle of your models and visualizing their performance. Plan for a model registry and monitoring tool beside it.
No model-specific speed-ups. Unlike framework-specific servers, Ray Serve does not optimize individual models. You optimize them yourself, for example with PyTorch JIT or ONNXRuntime, and then run them in Serve.
Plan the authentication change. Teams with existing clusters should read the project's token-authentication rollout details before upgrading.
Expert support comes through Anyscale. The open-source channels are mostly community-run. Hands-on training and expert support from the team that created Ray come through Anyscale.
Pricing and plan conditions
The billing boundary is who runs the clusters. With open-source Ray you supply and run the infrastructure yourself. Anyscale moves that work to a fully managed platform. Ray's docs also show a "Try Ray with $100 credit" banner next to a "Try Managed Ray" link.
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| Option | Cost basis | What it adds |
|---|---|---|
Ray (open source) | Runs on your own infrastructure | Ray Core and the AI libraries; community support channels |
Anyscale (managed Ray) | Try for Free; Book a Demo | Fully managed platform to run Ray, plus enterprise governance, advanced developer tooling and expert support |
Public signals
About these signals
Verified factual signals from public sources. They indicate observable activity or interest, not total adoption, product quality, or cost.
1.2k GitHub commits 90d44.2k GitHub stars0 vulnerabilities across 1 package
See all signals from 7 sources
Three-month score against stable baseline terms—not search volume or adoption.
PyPI · ray@2.60.0
Alternatives
AWS SageMaker, Azure ML, Google Vertex AI. Ray's Serve docs say Serve brings the scalability and reliability of these hosted offerings to your own infrastructure.
TFServing, TorchServe, ONNXRuntime. Ray's docs call these framework-specific solutions; Serve is framework-agnostic.
Seldon, KServe, Cortex. Ray's docs say Serve lets you develop on a laptop and scale to multiple machines or a Kubernetes cluster with minimal or no code changes, without first provisioning a K8s cluster.
BentoML, Comet.ml, MLflow. Ray's docs say these focus on serving and scaling models independently, while Serve focuses on model composition and works with any model packaging and registry format.
Alternatives to Ray
The reviewed substitutes for Ray among the data processing engines, and what would make each one the better answer.
Other approaches
A different approach to the same problem. Each substitutes only for the workload named beside it.
- Kubeflow
- Kubeflow and Ray can both run distributed machine learning training and the pipelines around it. Kubeflow is a set of Kubernetes-native tools covering the ML workflow from notebooks to serving, which fits teams already on Kubernetes, while Ray is a Python framework for scaling compute across many machines and accelerators, with managed hosting from Anyscale.Applies to: Deciding how the stack is shaped, where both products can be part of the answer.
- Amazon SageMaker
- The decision is whether distributed ML compute is run directly or consumed as a managed platform. Anyscale benchmarks Ray Data against SageMaker Batch Transform, and SageMaker also hosts Ray through built-in containers and HyperPod, so the pair is an architecture choice rather than a straight substitution.Applies to: Running distributed training and batch inference, with or without a managed ML platform.
Related technologies
Normally used together rather than chosen between, so these are not alternatives.
- MLflow
- A distributed compute framework runs the training or tuning; a tracker records what each run produced. The documented pattern logs from inside distributed jobs, so the two are used together rather than chosen between.Applies to: Whether distributed training needs a separate tool to record results.
- Weights & Biases
- A distributed compute framework runs the training or tuning; a tracker records what each run produced. The documented pattern logs from inside distributed jobs, so the two are used together rather than chosen between.Applies to: Whether distributed training needs a separate tool to record results.