Software Alternatives, Accelerators & Startups

Jenkins VS Metaflow

Compare Jenkins VS Metaflow and see what are their differences

Jenkins logo Jenkins

Jenkins is an open-source continuous integration server with 300+ plugins to support all kinds of software development

Metaflow logo Metaflow

Framework for real-life data science; build, improve, and operate end-to-end workflows.
  • Jenkins Landing page
    Landing page //
    2023-04-15
  • Metaflow Landing page
    Landing page //
    2023-03-03

Jenkins features and specs

  • Open Source
    Jenkins is an open-source tool, which means users can modify, share, and use it without licensing fees.
  • Large Plugin Ecosystem
    Jenkins has a robust plugin ecosystem with over 1,500 plugins, allowing extensive customization and functionality to fit various DevOps needs.
  • Active Community
    The active and large community of Jenkins users and developers provides extensive support, documentation, and shared solutions.
  • Platform Independent
    Jenkins can run on various platforms including Windows, macOS, and various Unix-like systems, providing flexibility in deployment.
  • CI/CD Capabilities
    Jenkins is well-suited for implementing Continuous Integration and Continuous Deployment (CI/CD) pipelines, facilitating automated build, test, and deployment processes.
  • Scalability
    It supports distributed builds using Master-Slave architecture, enabling you to scale your build and deployment processes across multiple machines.
  • Extensible
    Thanks to its plugin architecture, Jenkins can be extended to integrate with a variety of tools and services, making it highly adaptable.

Possible disadvantages of Jenkins

  • Complex Setup
    Initial setup and configuration of Jenkins can be complicated, especially for new users or large-scale environments.
  • Resource Intensive
    Jenkins can be resource-intensive, requiring significant memory and CPU, particularly for large projects or high-frequency builds.
  • Maintenance Overhead
    Due to its extensive plugin usage, keeping Jenkins and its plugins updated can be time-consuming and sometimes problematic.
  • Steep Learning Curve
    Learning to use Jenkins effectively can have a steep learning curve, particularly due to the need to understand its various plugins and configuration options.
  • User Interface
    The user interface of Jenkins is sometimes considered outdated and not as intuitive or user-friendly as some of its modern counterparts.
  • Security Vulnerabilities
    As with many open-source tools, Jenkins can have security vulnerabilities that need to be regularly addressed to ensure a secure environment.
  • Poor Plugin Compatibility
    Not all plugins are maintained equally, leading to potential compatibility issues or bugs when using multiple plugins together.

Metaflow features and specs

  • Ease of Use
    Metaflow is designed with a strong focus on user experience, providing users with a simple and user-friendly interface for building and managing workflows. Its Pythonic API makes it easy for data scientists to work with complex data workflows without needing to learn a lot of new concepts.
  • Scalability
    Metaflow supports scalable data workflows, allowing users to run their workflows seamlessly from a laptop to the cloud. It integrates well with AWS, enabling users to utilize Amazon's scalable infrastructure for processing large datasets.
  • Versioning
    Metaflow provides built-in support for data and model versioning, making it easier for teams to track changes and reproduce results. This feature is crucial for maintaining consistency and reliability in machine learning projects.
  • Integration with Popular Tools
    Metaflow integrates well with popular data science and machine learning tools, including Jupyter notebooks and AWS services, enhancing its usability within existing data ecosystems.
  • Error Handling and Monitoring
    Metaflow offers robust error handling and monitoring capabilities, allowing users to track the execution of workflows, identify errors, and debug issues efficiently.

Possible disadvantages of Metaflow

  • AWS Dependency
    While Metaflow supports other infrastructures, it is tightly integrated with AWS. Users who do not use AWS may find it less convenient compared to other tools that are more agnostic in their cloud support.
  • Limited Support for Non-Python Environments
    Metaflow primarily supports Python, which might be a limitation for teams or projects that rely heavily on other programming languages for their workflows.
  • Learning Curve for Advanced Features
    Although Metaflow is designed to be user-friendly, utilizing its advanced features and realizing its full potential can have a steep learning curve, especially for users without prior experience with workflow management systems.
  • Community and Ecosystem Size
    Compared to some of its competitors, Metaflow has a smaller community and ecosystem, which might limit the availability of third-party resources, plugins, and community support.
  • Enterprise Features
    Some advanced enterprise features, while robust, may not be as developed or extensive compared to other dedicated data processing and workflow management platforms.

Jenkins videos

Mick Jenkins - The Circus Album Review | DEHH

More videos:

  • Review - Mick Jenkins - The Water[s] ALBUM REVIEW
  • Review - Mick Jenkins - THE WATERS First REACTION/REVIEW

Metaflow videos

useR! 2020: End-to-end machine learning with Metaflow (S. Goyal, B. Galvin, J. Ge), tutorial

More videos:

  • Review - Screencast: Metaflow Sandbox Example

Category Popularity

0-100% (relative to Jenkins and Metaflow)
DevOps Tools
96 96%
4% 4
Workflow Automation
0 0%
100% 100
Continuous Integration
100 100%
0% 0
Continuous Deployment
100 100%
0% 0

User comments

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Reviews

These are some of the external sources and on-site user reviews we've used to compare Jenkins and Metaflow

Jenkins Reviews

The Best Alternatives to Jenkins for Developers
Jenkins X, a new kind of Jenkins made for cloud environments and modern development practices, tries to make setting up and handling CI/CD pipelines easier. It uses Kubernetes along with GitOps ideas in order to offer teams working on cloud-native apps an automated way that is less complex when it comes to managing their project’s lifecycle.
Source: morninglif.com
Top 5 Jenkins Alternatives in 2024: Automation of IT Infrastructure Written by Uzair Ghalib on the 02nd Jan 2024
If you have searched about Jenkins alternatives and you are reading this article, then there must be one of the three reasons you are here. You are already using Jenkins and are fed up with facing different issues and looking for a change. Or maybe you haven’t faced any issues yet but have heard the stories about Jenkins issues and looking to avoid them by choosing an...
Source: attuneops.io
What Are The Best Alternatives To Ansible? | Attune, Jenkins &, etc.
Jenkin is a popular tool for performing continuous integration of software projects in the market. Plus, it continues the delivery of projects regardless of the platform you’re working on. And it is also responsible for handling any build or continuous integration with various testing and development technologies. As a product, Jenkins is more developer-centric and...
Best 8 Ansible Alternatives & equivalent in 2022
Jenkins is an open-source continuous integration tool. It is written using the Java programming language. It facilitates real-time testing and reporting on isolated changes in a larger code base. This software similar to Ansible helps developers to quickly find and solve defects in their code base & automate testing of their builds.
Source: www.guru99.com
Top 10 Most Popular Jenkins Alternatives for DevOps in 2024
Jenkins may be a de-facto tool for CI/CD, but it’s no longer a shiny newcomer borne directly out of modern DevOps best practices. Although Jenkins is still relevant, newer tools can offer improved ergonomics and expanded functionality. These can be better suited to contemporary software delivery methods.
Source: spacelift.io

Metaflow Reviews

Comparison of Python pipeline packages: Airflow, Luigi, Gokart, Metaflow, Kedro, PipelineX
Metaflow enables you to define your pipeline as a child class of FlowSpec that includes class methods with step decorators in Python code.
Source: medium.com

Social recommendations and mentions

Based on our record, Metaflow should be more popular than Jenkins. It has been mentiond 14 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.

Jenkins mentions (7)

  • CircleCI vs. Jenkins
    Jenkins is an open-source automation server used for software continuous integration and delivery. It automates various tasks, such as building, testing, and deploying applications.  It is easily extendable due to its vast ecosystem of plugins, making it easy to integrate into version control systems like Git, build tools like Maven/Gradle, and deployment platforms like AWS and Docker. - Source: dev.to / 3 months ago
  • Automated delivery React / Vue app for each Pull Request.
    It will give you a possibility to find and solve problems faster, release more stable and higher quality products. Here we will use CircleCI, but you can use whatever you need (Jenkins, Travis CI, GitLab CI). - Source: dev.to / about 1 year ago
  • Is Jenkins dead? v2
    CloudBees Jenkins Platform is a commercial offering from CloudBees, it is not the Jenkins project itself (which is open source). Jenkins is alive and well. See https://jenkins.io. Source: almost 2 years ago
  • ELI5 what is Jenkins?
    Ok. I'm talking about this: https://jenkins.io/. Source: over 2 years ago
  • I wanted a self hosted alternative to Atlassian status page so I build my own application !
    Currently supported : Datadog, Jenkins, DNS, HTTP. Source: over 2 years ago
View more

Metaflow mentions (14)

  • 20 Open Source Tools I Recommend to Build, Share, and Run AI Projects
    Metaflow is an open source framework developed at Netflix for building and managing ML, AI, and data science projects. This tool addresses the issue of deploying large data science applications in production by allowing developers to build workflows using their Python API, explore with notebooks, test, and quickly scale out to the cloud. ML experiments and workflows can also be tracked and stored on the platform. - Source: dev.to / 7 months ago
  • Recapping the AI, Machine Learning and Computer Meetup — August 15, 2024
    As a data scientist/ML practitioner, how would you feel if you can independently iterate on your data science projects without ever worrying about operational overheads like deployment or containerization? Let’s find out by walking you through a sample project that helps you do so! We’ll combine Python, AWS, Metaflow and BentoML into a template/scaffolding project with sample code to train, serve, and deploy ML... - Source: dev.to / 10 months ago
  • What are some open-source ML pipeline managers that are easy to use?
    I would recommend the following: - https://www.mage.ai/ - https://dagster.io/ - https://www.prefect.io/ - https://metaflow.org/ - https://zenml.io/home. Source: about 2 years ago
  • Needs advice for choosing tools for my team. We use AWS.
    1) I've been looking into [Metaflow](https://metaflow.org/), which connects nicely to AWS, does a lot of heavy lifting for you, including scheduling. Source: about 2 years ago
  • Selfhosted chatGPT with local contente
    Even for people who don't have an ML background there's now a lot of very fully-featured model deployment environments that allow self-hosting (kubeflow has a good self-hosting option, as do mlflow and metaflow), handle most of the complicated stuff involved in just deploying an individual model, and work pretty well off the shelf. Source: over 2 years ago
View more

What are some alternatives?

When comparing Jenkins and Metaflow, you can also consider the following products

CircleCI - CircleCI gives web developers powerful Continuous Integration and Deployment with easy setup and maintenance.

Apache Airflow - Airflow is a platform to programmaticaly author, schedule and monitor data pipelines.

Travis CI - Simple, flexible, trustworthy CI/CD tools. Join hundreds of thousands who define tests and deployments in minutes, then scale up simply with parallel or multi-environment builds using Travis CI’s precision syntax—all with the developer in mind.

Luigi - Luigi is a Python module that helps you build complex pipelines of batch jobs.

Codeship - Codeship is a fast and secure hosted Continuous Delivery platform that scales with your needs.

Azkaban - Azkaban is a batch workflow job scheduler created at LinkedIn to run Hadoop jobs.