Dask alternatives

2 tools to consider instead of Dask, shown against it.

Dask Ray Apache Spark
Vendor Dask (NumFOCUS-sponsored open-source project) Anyscale (Ray open-source project) Apache Software Foundation
Pricing model Open source + paid options Open source + paid options Open source + paid options
Free tier Yes Yes Yes
Deployment Self-hosted Self-hosted Self-hosted
Open source Yes (BSD-3-Clause) Yes (Apache-2.0) Yes (Apache-2.0)
Best for Python/pandas teams scaling existing analysis or ML code beyond a single machine without switching ecosystems. ML engineering teams scaling model training, tuning or inference beyond a single machine. Data engineering teams building large-scale batch ETL, streaming or machine-learning pipelines.
Pricing

Free and open source; managed cluster hosting for Dask is sold separately by Coiled on a usage-based basis.

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

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.

Free and open source under the Apache Software Foundation; managed lakehouse platforms built on Spark, such as Databricks, are priced separately.

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

Features
  • Parallelizes pandas, NumPy and scikit-learn code with minimal rewrites
  • Dynamic task-graph scheduling
  • Scales from a laptop to a distributed cluster
  • Dask-ML for distributed model training
  • Integrates with Kubernetes, YARN and HPC job schedulers
  • Lazy evaluation for large-than-memory datasets
  • 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
  • Unified batch, SQL, streaming and ML APIs
  • In-memory distributed execution (RDDs/DataFrames)
  • Structured Streaming for near-real-time pipelines
  • MLlib for distributed machine learning
  • Runs on Kubernetes, YARN or standalone
  • Broad connector ecosystem for storage and lakehouse formats

In the index now