dask
Dask is an open-source Python parallel computing library that scales NumPy, Pandas, and scikit-learn workflows from a single machine to distributed clusters, serving data scientists, ML practitioners, and scientific researchers across enterprise and academia.
- Company typePrivate
- Founded-
- Headquarters—
- Headcount—
- GTM typeB2B
- OfferingSoftware
What dask does
Dask is an open-source Python library for parallel and distributed computing, maintained by a community of "Dask core developers" and licensed under the New BSD License. The product scales the Python data science toolchain (NumPy, Pandas, scikit-learn) from a single laptop to clusters of thousands of machines using lazy evaluation and a dynamic task scheduler. Core components include Dask Arrays (blocked NumPy), Dask DataFrames (parallel Pandas), Dask Bags (unstructured data), Dask Delayed (custom task graphs), Dask Futures (real-time execution), and the Dask Dashboard (diagnostics). The ecosystem extends this core with Dask Gateway (multi-tenant cluster management), the Dask Kubernetes Operator and Helm chart (Kubernetes-native deployments), Dask-Jobqueue (SLURM/PBS/SGE/LSF HPC integration), Dask-MPI, and dask-ml for scaled machine learning.
dask firmographics
Firmographics- Name
- dask
- Legal name
- Dask
- Website
- https://dask.org
- Company type
- Private
- Operating status
- Operating
- Short description
- Dask is an open-source Python parallel computing library that scales NumPy, Pandas, and scikit-learn workflows from a single machine to distributed clusters, serving data scientists, ML practitioners, and scientific researchers across enterprise and academia.
- Ownership category
- akta.pro rank
dask industry classification
Industry- Product category
- Distributed Computing Framework
- NAICS
- Computing Infrastructure Providers, Data Processing, Web Hosting, and Related Services (5182), Computer Systems Design and Related Services (54151), Computer Systems Design and Related Services (5415)
- SIC
- Services-Prepackaged Software (7372), Services-Computer Programming Services (7371)
- akta.pro primary industry
- Developer Tools & DevOps Platform Services (CI/CD, Artifacts, IaC) (HDABAAAI)
- akta.pro secondary industry
- Hybrid Cloud Management, Monitoring & FinOps (HDABAMAD)
Keywords
dask business model
Business model- GTM type
- B2B
- Offering type
- Software
- Cost components
- Personnel, Technology or R&D, Operations, Marketing or Sales, Infrastructure
Revenue model
- Open Source Core (No Direct Revenue): Dask is a free, open-source library under the New BSD License. The core library generates no direct revenue. The project is sustained by volunteer contributors and companies that use Dask in production.
- Coiled (Related Commercial Service): Coiled is a separate commercial company that provides a managed cloud service built on top of Dask. While not directly Dask's revenue, it represents the commercial ecosystem around Dask. Coiled offers a free tier for individuals and paid tiers for teams.
Pricing tiers
| Model | Billing | Price |
|---|---|---|
| Freemium | Others | Open Source - Free |
Go-to-market motion2 records
Distribution channels5 records
Marketing channels9 records
dask product offering
Product offeringCore offering
Dask is an open-source Python library for parallel and distributed computing that scales familiar Python interfaces (NumPy, pandas, scikit-learn) from single laptops to thousands-node clusters using task scheduling and blocked algorithms. The core offering is a free, New-BSD-licensed library; the surrounding commercial ecosystem is delivered through Coiled, which provides a managed cloud platform built on Dask with free and paid tiers.
Product overview
Dask is an open-source Python parallel computing library that scales the Python tools users already know (NumPy, pandas, scikit-learn) to clusters of machines through lazy evaluation and task scheduling. The core product consists of multiple high-level collections: Dask Arrays (blocked NumPy interface for large multi-dimensional data), Dask DataFrames (pandas-compatible interface for large tabular data), Dask Bags (unstructured data processing), Dask Delayed (custom task graphs), and Dask Futures (real-time task execution). The ecosystem includes Dask Gateway (multi-tenant cluster management via Kubernetes/Helm), Dask Kubernetes Operator, Dask-Jobqueue (HPC scheduler integration), Dask-MPI, and dask-ml (machine learning at scale). Coiled is a commercial managed cloud SaaS built on Dask.
Differentiator
Problem solved
Functional benefit
Products and services
- Dask Open-source Python library for parallel and distributed computing that scales Python code from a single machine to clusters of thousands of machines using lazy evaluation and task scheduling; intended for data scientists, ML engineers, and researchers working with large datasets.
- Dask Arrays Parallel NumPy-like array interface that processes arrays larger than memory by coordinating many small NumPy arrays into a grid, supporting arithmetic, reductions, slicing, tensor contractions, and selected linear algebra.
- Dask DataFrames Pandas-like DataFrame interface that scales tabular pandas workflows to large datasets, using pandas under the hood so existing pandas code typically works with minimal changes.
- Dask Bags High-level collection for processing unstructured or semi-structured data in parallel, suitable for log files, JSON records, and other arbitrary Python objects.
- Dask Futures Real-time task framework that extends Python's concurrent.futures interface to enable immediate, non-lazy parallel execution of arbitrary Python code across Dask clusters.
- Dask Delayed Mechanism to parallelize existing Python code by wrapping functions and building custom task graphs with lazy evaluation and fine-grained dependencies.
- Dask Gateway Multi-tenant, secure cluster manager for Dask that provides a gateway API server, Traefik proxy, and Kubernetes controller for launching Dask clusters without direct access to backend infrastructure.
- Dask Kubernetes Operator Kubernetes-native operator for deploying and managing Dask clusters on Kubernetes, recommended for fast-moving or ephemeral deployments.
- Dask-Jobqueue Interface to HPC job schedulers (SLURM, PBS, SGE, LSF) that launches Dask workers as batch jobs, enabling interactive and adaptive use on large HPC systems without dedicated IT support.
- Dask-MPI Deploys Dask on top of MPI systems using mpirun/mpiexec, suitable for batch processing jobs that require a fixed and stable number of workers.
- Dask Gateway Helm Chart Helm chart for deploying single Dask clusters and optionally Jupyter on Kubernetes via Helm.
- dask-ml Machine learning library providing common ML functions scaled with Dask, including scikit-learn estimator wrappers and distributed hyperparameter optimization.
Quantifiable outcome
- 91% reduction in model training times at Capital One
- +3 more outcomes
Companies that use dask
Customer profileNamed customers8 records
Segments4 records
Ideal customer profiles3 records
dask technology and API
TechnologyTechnology focussed Yes
API detail
- Has API
- Yes
- API docs
- API detail
Core technology
AI maturity
App detail
Integration30 records
Feature8 records
dask partnerships and signals
Strategic signalPartnerships
Eight partnerships are on record, tiered core.
- CoiledcoreCoiled is the primary commercial ecosystem partner for Dask, providing a managed cloud service built on top of Dask. They offer free hosting for individuals and paid plans for teams. Coiled's documentation is integrated with Dask's official docs and benchmarks. The partnership is co-branded with Dask's homepage promoting Coiled as the recommended cloud deployment option.
- XGBoostcoreNative Dask integration with XGBoost for distributed gradient boosted tree training. Dask DataFrames can be directly converted to XGBoost DaskDMatrix for model training at scale.
- PyTorchcoreDask provides integration for PyTorch workloads, enabling distributed batch prediction and training workflows.
- scikit-learncoreIntegration with scikit-learn for parallelizing machine learning workflows using Dask's parallel backends.
- OptunacoreOptuna integration with Dask via DaskStorage, enabling distributed hyperparameter optimization with parallel trial evaluation.
- XarraycoreXarray integration enables Dask to process multi-dimensional array data formats like HDF, NetCDF, TIFF, and Zarr for scientific computing.
- KubernetescoreDask runs natively on Kubernetes via Dask Kubernetes Operator, enabling containerized deployments. Dask Gateway provides multi-tenant cluster management on Kubernetes.
- HPC Resource Managers (SLURM, PBS, LSF)coreDask-Jobqueue interfaces directly with HPC resource managers SLURM, PBS, SGE, and LSF to launch Dask workers as batch jobs on supercomputers.
Scale indicators5 records
Recent moves6 records
Expansion highlights5 records
dask competitors and assessment
Company assessmentBroad incumbents
- Databricks: Commercial data and AI platform built around Apache Spark, Delta Lake, and MLflow. A broad incumbent offering an end-to-end alternative to Dask-based stacks for enterprise data engineering and ML workloads.
- Anaconda: Distributor of Python and R for data science, ships Dask by default in its distribution. An adjacent incumbent in the Python data science ecosystem rather than a direct distributed-compute competitor.
- AWS EMR: Managed cloud service for running Spark, Hadoop, and other distributed frameworks on AWS. Competes with Dask (and Coiled) by offering a fully managed, enterprise-supported alternative for distributed data processing on cloud infrastructure.
Direct peers
- Ray: Open-source framework for scaling Python and AI workloads, offering distributed task scheduling, actors, and ML libraries. Most credible emerging alternative to Dask for Python-native distributed compute, especially in ML/AI contexts.
- Coiled: Commercial SaaS offering managed Dask on cloud infrastructure. Effectively the commercial monetization layer for the Dask open-source project, providing paid cluster provisioning, environments, and support.
- Apache Spark: The dominant open-source distributed data processing engine. Direct competitor to Dask for large-scale parallel computation, with a broader enterprise ecosystem but a JVM/Scala-centric stack versus Dask's Python-native approach.
- Apache Beam: Open-source unified programming model for batch and streaming data processing that runs on multiple execution backends (Spark, Flink, Google Cloud Dataflow). Competes with Dask for large-scale ETL and pipeline workloads.
Emerging players
- Polars: Fast DataFrame library built on Apache Arrow with its own multi-threaded and streaming engines. Competes for single-machine pandas-replacement use cases that might otherwise graduate to Dask.
- Modin: Open-source library that accelerates pandas by dropping in a Dask or Ray backend. Directly competes with Dask DataFrames for the 'scale pandas without rewriting' use case, and overlaps with Dask's pandas compatibility story.
Others
- NumFOCUS: Non-profit fiscal sponsor for many open-source data science projects including NumPy, pandas, and Jupyter. Operates as a peer organization in the open-source scientific Python ecosystem in which Dask sits.
Market position
Strengths4 records
Weaknesses4 records
Competitive moat5 records
Key risks5 records
Key highlights6 records
Customer concentration
dask social profiles
Digital presencedask financial estimates
Financial estimateRevenue estimate
Valuation estimate
dask leadership team
Management profileNumber of profiles
dask funding detail
Funding detailFunding overview
Funding rounds
Investors
Funding detail is available on the Subscription and Enterprise plan.Contact sales →
dask M&A and investment
M&A and investmentM&A
Investments
M&A and investment is available on the Subscription and Enterprise plan.Contact sales →
Frequently asked questions about dask
What does dask do?
Dask is an open-source Python library for parallel and distributed computing that scales familiar Python interfaces (NumPy, pandas, scikit-learn) from single laptops to thousands-node clusters using task scheduling and blocked algorithms. The core offering is a free, New-BSD-licensed library; the surrounding commercial ecosystem is delivered through Coiled, which provides a managed cloud platform built on Dask with free and paid tiers.
Is dask a public or private company?
dask is a private company. It is classified as unknown and is currently operating.
When was dask founded?
dask was founded in -1.
How does dask make money?
Two revenue lines are on record. Open Source Core (No Direct Revenue) is the primary driver. The others are coiled (Related Commercial Service).
Who are dask's main competitors?
Broad incumbents on record are Databricks, Anaconda and AWS EMR. Direct peers are Ray, Coiled, Apache Spark and Apache Beam. Emerging players are Polars and Modin. NumFOCUS is listed as an others.
Does dask have an API?
Yes. Dask provides a Python API for distributed task scheduling and parallel computing. The primary interface is the dask.distributed.Client class, which connects to a Dask cluster and provides methods for submitting tasks (Client.submit, Client.map), gathering results (Client.gather), scattering data (Client.scatter), and managing futures. Additional APIs include dask.array for large NumPy-like arrays, dask.dataframe for pandas-like DataFrames, dask.bag for unstructured data, dask.delayed for custom task graphs, and dask.futures for real-time task execution. The dask-gateway package provides a Gateway API for multi-tenant cluster management.
What industry is dask in?
dask's product category is Distributed Computing Framework. Its primary akta.pro industry code is HDABAAAI, Developer Tools & DevOps Platform Services (CI/CD, Artifacts, IaC), with a secondary code of HDABAMAD, Hybrid Cloud Management, Monitoring & FinOps. Its NAICS code is 5182 and its SIC code is 7372.