Query engines & federation · Anyscale (Ray open-source project)

Ray

Open-source Python framework for distributing machine-learning training, tuning and general compute across a cluster.

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Ray is an open-source Python framework for scaling compute-heavy workloads, most notably machine-learning training, hyperparameter tuning, reinforcement learning and batch inference, from a single laptop to a large cluster with minimal code changes. It provides a low-level task and actor API for general distributed Python, plus higher-level libraries (Ray Train, Ray Tune, Ray Serve, Ray Data) purpose-built for ML pipelines, distinguishing it from data-engineering-first engines like Spark or Flink. Ray handles scheduling, fault tolerance and resource management (CPUs, GPUs, memory) across a dynamically scaling cluster of nodes. It is self-hosted by default, commonly deployed on Kubernetes via KubeRay, while Anyscale, founded by Ray's original creators, offers a managed platform for running Ray at scale with usage-based compute billing. Ray is typically shortlisted by ML engineering teams rather than by BI or classic ETL teams.

At a glance

Vendor Anyscale (Ray open-source project)
Pricing model Open source + paid options
Free tier Yes
Deployment Self-hosted
Open source Yes (Apache-2.0)
Best for ML engineering teams scaling model training, tuning or inference beyond a single machine.

Pricing

Free and open source; a managed platform for running Ray at scale is sold separately by Anyscale on a usage-based basis.

Pricing has not been verified yet — see the vendor's site.

Features

  • Distributed task and actor execution for Python
  • Ray Train for distributed model training
  • Ray Tune for distributed hyperparameter search
  • Ray Serve for model serving
  • Ray Data for distributed data loading/preprocessing
  • Dynamic autoscaling across CPU/GPU clusters

Integrations

Profile last reviewed September 21, 2026

Alternatives

Dask Open-source Python library that parallelizes NumPy, pandas and scikit-learn workflows across cores or a cluster. Apache Spark Open-source distributed processing engine for large-scale batch, SQL, streaming and machine-learning workloads. Apache Flink Open-source stream-processing engine for stateful, low-latency event processing at scale, with batch support.

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Terms to know

Machine learning Building models that learn patterns from data to make predictions or decisions, rather than following hand-coded rules. Hyperparameter tuning The process of finding the settings that control how a model learns, such as learning rate or tree depth, rather than what it learns. Reinforcement learning A learning approach where an agent learns a strategy by taking actions in an environment and receiving rewards or penalties over time.

Related guides

How to choose a distributed query engine This category bundles two different jobs — federating SQL across existing systems, and distributed compute for pipelines and ML — start by telling them apart.