PyRun vs. Coiled: Choosing the Right Cloud Computing Platform ⚔️

Both PyRun and Coiled aim to simplify scaling Python workloads in the cloud, particularly with Dask. Coiled is a valuable tool for scaling Dask, offering managed cluster deployments. However, PyRun provides a distinct, integrated, and often more efficient approach, especially when considering versatility, ease of use, and overall performance across different types of workloads.

This page outlines the key differences to help you choose the platform that best fits your needs.

Core Philosophy and Approach

• PyRun: Focuses on providing a unified, simplified, and highly integrated platform for running various Python workloads (including Dask and FaaS/Lithops) in the cloud. It prioritizes automating infrastructure management, runtime configuration, and providing a seamless user experience directly within the platform.
• Coiled: Primarily focuses on managed Dask cluster deployment and scaling. It aims to make provisioning and managing Dask clusters easier for data scientists and engineers already familiar with Dask.

Performance & Cost Benchmarks

Direct comparisons highlight significant performance and cost advantages for PyRun in specific scenarios:

FaaS (Function-as-a-Service / Lithops) Execution

For serverless function execution, PyRun demonstrates substantial benefits. The benchmark below represents generating Kerchunk references for GOES satellite data, executing 24 parallel functions, each allocated 1 CPU and 256 MB of memory:

• Cost: Benchmarks show PyRun can be dramatically more cost-effective, achieving up to 14.9x lower costs compared to Coiled for equivalent FaaS workloads.
• Speed: PyRun executes FaaS tasks significantly faster, showing up to 5.4x speed improvements in our tests.

Dask Execution

When running Dask workloads, PyRun also shows performance advantages. This benchmark represents generating IPCC plots entirely in the cloud (for the AMS2023 Meeting) using a Dask cluster of 10 workers, each with 4 CPUs, 16GB memory, and 4GB ephemeral storage:

• Cluster Creation: PyRun achieves significantly faster Dask cluster creation times, up to 2.5x faster than Coiled in benchmarks. This translates to quicker iteration cycles.
• Execution Time: PyRun demonstrates faster Dask task execution, up to 1.4x faster.
• Total Time: Combining faster cluster creation and execution, PyRun delivers up to 1.6x faster total time for completing Dask workloads.

User Experience & Interface

• PyRun: Offers an integrated, VS Code-like web interface. You can write code, manage files, configure runtimes (environment.yml/Dockerfile), monitor jobs, and configure frameworks (like Lithops) all within a single, unified environment. This seamless workflow minimizes context switching.

  

• Coiled: Primarily managed through its web dashboard, Python API, and CLI. While powerful, it may require users to integrate with separate development environments (like a local VS Code or Jupyter) and manage configurations potentially across different interfaces.

Runtime & Configuration Management

• PyRun: Features automatic runtime management. Simply edit your .pyrun/environment.yml or replace it with a Dockerfile, and PyRun automatically detects changes, rebuilds the runtime, and makes it available for your next execution. Configuration for tools like Lithops is also handled directly within the PyRun dashboard. It's designed to be transparent and effortless.
• Coiled: Requires users to manage software environments for their Dask clusters, often involving building Docker images or using Coiled's environment management features, which might involve more manual steps or configuration outside the primary coding workflow.

Framework Support

• PyRun: Designed for versatility, offering first-class, integrated support for both Dask and Lithops (FaaS) out-of-the-box. This allows users to choose the best tool for the job within the same platform.
• Coiled: Primarily known and optimized for Dask. While other integrations might be possible, its core focus and tightest integration revolve around Dask clusters.

Summary Table

Feature	PyRun	Coiled
Primary Focus	Unified Platform (Dask, FaaS/Lithops, AI Workflow)	Managed Dask Scaling
FaaS Cost (Kerchunk GOES)	Significantly Lower (up to 14.9x cheaper)	Higher
FaaS Speed (Kerchunk GOES)	Significantly Faster (up to 5.4x faster)	Slower
Dask Cluster Creation (IPCC Plot)	Faster (up to 2.5x faster)	Slower
Dask Total Time (IPCC Plot)	Faster (up to 1.6x faster)	Slower
User Interface	Integrated VS Code-like web interface	Web Dashboard, Python API, CLI (Often used with separate IDEs)
Runtime Management	Automatic detection & rebuild (environment.yml/Dockerfile)	Manual/API-driven environment creation/management
Configuration	Integrated within PyRun dashboard (e.g., Lithops config)	Primarily via API, CLI, or dashboard settings
Framework Support	Dask & Lithops (FaaS) seamlessly integrated	Strong focus on Dask, other integrations may require more effort
Ease of Use	High - designed for simplicity and automation	Moderate to High - powerful but potentially steeper learning curve

Why Choose PyRun?

Choose PyRun if you value:

• Significant Cost Savings & Speed: Especially for FaaS/Lithops workloads, but also offering Dask performance benefits.
• Simplicity & Ease of Use: An integrated environment and automated management reduce complexity.
• Seamless Workflow: Code, configure, run, and monitor all within a single platform.
• Versatility: First-class support for both Dask and Lithops (FaaS) provides flexibility.
• Faster Iteration: Quicker cluster startup and automated runtime builds speed up development cycles.

While Coiled is a strong contender for purely Dask-focused teams needing managed cluster deployment, PyRun offers a compelling alternative with broader framework support, a more integrated user experience, and significant performance and cost advantages demonstrated in benchmarks.