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

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Reviews onG2Capterra
67 reviews tracked

The Bottom Line

Entry price

Free plan available, paid tiers above

Biggest pro

Simplifies complex data pipeline creation

Biggest con

Requires some understanding of containerization (e.g., Docker)

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

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.

Available on: Windows, macOS, Linux

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

Pricing checked Jul 11, 2026

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

How Orchest's pricing compares

At $10/mo, Orchest is mid-range of its 6 direct competitors ($0.35 to $450/mo across the set).

Entry paid plan, monthly. Pricing checked Jul 11, 2026.

Reviews

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4.8/5

Across 67 verified user reviews on Capterra, G2

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Orchest FAQ

How does Orchest simplify the creation of data pipelines?

Orchest provides a visual interface for defining data workflows, which allows users to connect various steps from data ingestion and transformation to model training and deployment. This visual approach helps streamline complex data operations by offering a structured environment.

Which teams benefit most from using Orchest?

Orchest is designed for data scientists and engineers, enabling them to streamline machine learning and data processing workflows. It facilitates collaboration among data teams and is suitable for projects ranging from small-scale analyses to large-scale enterprise data initiatives.

Does Orchest include a free tier?

Yes, Orchest is available on a free tier, with paid plans offered for users requiring more usage and additional features. Its open-source nature also allows for customization and community support.

What kind of technical expertise is needed to get started with Orchest?

While Orchest simplifies data pipeline creation, users should have some understanding of containerization, such as Docker. The initial setup of the platform might also require technical expertise.

How does Orchest compare to Apache Airflow for workflow automation?

Orchest provides a visual interface for building, running, and managing data pipelines, aiming to simplify complex data operations. Like Apache Airflow, it focuses on workflow automation but emphasizes a unified environment for experimentation, collaboration, and production deployment.

Can Orchest be used for ETL processes?

Yes, Orchest is categorized under ETL & Data Pipelines, indicating its suitability for Extract, Transform, Load processes. It supports defining data workflows that include data ingestion and transformation steps.

Source: orchest.io

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