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Orchest

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Build, run, and manage data pipelines with a visual interface and powerful orchestration.

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TL;DR - Orchest

  • Visually build and manage data pipelines.
  • Streamline data science and machine learning workflows.
  • Open-source platform for reproducibility and scalability.
Pricing: Free plan available
Best for: Growing teams
4.8/5 across review platforms

Pros & Cons

Pros

  • Simplifies complex data pipeline creation
  • Enhances reproducibility of data science projects
  • Facilitates collaboration among data teams
  • Open-source nature allows for customization and community support

Cons

  • Requires some understanding of containerization (e.g., Docker)
  • Initial setup might require technical expertise
  • Performance can depend on underlying infrastructure

Ratings Across the Web

4.8(67 reviews)

Ratings aggregated from independent review platforms. Learn more

Key Features

Visual pipeline editorContainerized steps for reproducibilityIntegrated development environment (IDE)Version control integrationScheduled pipeline runsMonitoring and loggingCollaboration featuresScalable execution engine

Pricing Plans

Free Trial

Free

Free

  • 1 user
  • 1 project
  • 100 MB storage
  • Basic features

Basic

$10/month

  • 5 users
  • 5 projects
  • 1 GB storage
  • Advanced features

Pro

$25/month

  • Unlimited users
  • Unlimited projects
  • 10 GB storage
  • All features
  • Priority support

What is Orchest?

Editorial review
Orchest is an open-source platform designed for data scientists and engineers to build, run, and manage data pipelines efficiently. It provides a visual interface for defining data workflows, allowing users to connect various steps, from data ingestion and transformation to model training and deployment. The platform aims to simplify complex data operations by offering a structured environment for experimentation, collaboration, and production deployment. It caters to individuals and teams working with data, enabling them to streamline their machine learning and data processing workflows. By providing a unified environment, Orchest helps reduce the overhead associated with managing disparate tools and environments, leading to faster development cycles and more reliable data products. Its focus on reproducibility and scalability makes it suitable for projects ranging from small-scale analyses to large-scale enterprise data initiatives.

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Orchest FAQ

What is Orchest?

Orchest is an open-source platform that helps data scientists and engineers build, run, and manage data pipelines through a visual interface, simplifying complex data workflows and machine learning operations.

How much does Orchest cost?

Orchest offers a freemium model. The core platform is open-source and free to use. There are also paid tiers for additional features, support, and enterprise deployments.

Is Orchest free?

Yes, Orchest has a free and open-source version available for use. They also offer paid plans with additional features and support for larger organizations.

Who is Orchest for?

Orchest is designed for data scientists, machine learning engineers, and data engineers who need to build, manage, and automate data pipelines and machine learning workflows efficiently and collaboratively.

Source: orchest.io

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