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Coiled

Freemium
pythondaskbig datadata sciencemachine learningparallel computingcloud computingserverlessdata engineering

A managed cloud platform for scaling Python and Dask computations, enabling data scientists to run complex analytics and machine learning workloads on-demand without managing complex cloud infrastructure.

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Coiled provides a serverless platform designed to scale Python data science and machine learning workflows effortlessly. It primarily serves data scientists, ML engineers, and Python developers who work with datasets that exceed the memory capacity of a single machine. The platform dramatically simplifies the deployment and management of Dask clusters, an open-source parallel computing library, on cloud providers like AWS and GCP. Its core value proposition is abstracting away the complex DevOps of cloud infrastructure, allowing users to spin up and tear down scalable compute clusters with just a few lines of Python code. This significantly reduces the time and expertise needed to parallelize computations, enabling faster iteration on data-intensive projects.

Pros

  • Simplifies Dask cluster deployment and management in the cloud.
  • Seamlessly integrates with the existing PyData ecosystem (Pandas, NumPy, Scikit-learn).
  • Serverless, on-demand resource provisioning reduces idle costs.
  • Provides robust tools for monitoring, diagnostics, and debugging distributed computations.
  • Offers GPU support for accelerating machine learning and deep learning workloads.
  • Enterprise-grade security features and team management capabilities.

Cons

  • Primarily focused on the Dask ecosystem, which is a limitation for users invested in other frameworks like Apache Spark.
  • Usage-based pricing can be unpredictable for teams with highly variable workloads.
  • Requires a foundational understanding of parallel computing concepts to use Dask effectively, despite simplified deployment.
  • Introduces a dependency on a third-party managed service.

Key features

  • Managed Dask clusters
  • Serverless scaling of compute resources
  • GPU acceleration support
  • Customizable software environment management
  • Team collaboration and security features (SSO, VPC peering)
  • Performance monitoring and diagnostics dashboard
  • Integration with cloud storage (S3, GCS)
  • Notebook and IDE integration (Jupyter, VS Code, PyCharm)

Integrations

DaskPandasNumPyScikit-learnXGBoostPyTorchJupyterVS CodeAWSGoogle Cloud

Target audience

Data scientists, machine learning engineers, and Python developers working with large-scale datasets and requiring parallel computing capabilities without extensive DevOps overhead.

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Key Metrics

Founded

2020

Headquarters

Newark, USA

Pricing Tiers

Individual

Includes 100 free core-hours per month, after which it's pay-as-you-go per core-hour. Designed for individual developers, researchers, and students.

Free

Teams

Base price per user plus usage-based compute costs. Includes team management, user roles, shared software environments, and standard support.

$30/user/mo

Enterprise

Custom pricing for large organizations. Includes all Teams features plus VPC peering, SSO, private networking, audit logs, and premium support SLAs.

Custom

Frequently Asked Questions

Top Alternatives to Coiled

Databricks

A unified platform built around Apache Spark, offering a broader suite for ETL and BI, but requires migrating from a Pandas/Dask workflow to Spark.

Saturn Cloud

Another Dask-focused platform that offers a similar value proposition but with a different user experience and pricing, often appealing to users seeking more direct control over hosted Jupyter environments.

AWS SageMaker

A native AWS service offering a vast array of ML tools, but can involve more complex configuration and lead to vendor lock-in compared to Coiled's Python-native, multi-cloud approach.

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