Alternatives to Metal3 logo

Alternatives to Metal3

Kubernetes, Docker Compose, Helm, Rancher, and Docker Swarm are the most popular alternatives and competitors to Metal3.
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What is Metal3 and what are its top alternatives?

There are a number of great open source tools for bare metal host provisioning, including Ironic. It aims to build on these technologies to provide a Kubernetes native API for managing bare metal hosts via a provisioning stack that is also running on Kubernetes. We believe that Kubernetes Native Infrastructure, or managing your infrastructure just like your applications, is a powerful next step in the evolution of infrastructure management.
Metal3 is a tool in the Container Tools category of a tech stack.
Metal3 is an open source tool with GitHub stars and GitHub forks. Here’s a link to Metal3's open source repository on GitHub

Top Alternatives to Metal3

  • Kubernetes

    Kubernetes

    Kubernetes is an open source orchestration system for Docker containers. It handles scheduling onto nodes in a compute cluster and actively manages workloads to ensure that their state matches the users declared intentions. ...

  • Docker Compose

    Docker Compose

    With Compose, you define a multi-container application in a single file, then spin your application up in a single command which does everything that needs to be done to get it running. ...

  • Helm

    Helm

    Helm is the best way to find, share, and use software built for Kubernetes.

  • Rancher

    Rancher

    Rancher is an open source container management platform that includes full distributions of Kubernetes, Apache Mesos and Docker Swarm, and makes it simple to operate container clusters on any cloud or infrastructure platform. ...

  • Docker Swarm

    Docker Swarm

    Swarm serves the standard Docker API, so any tool which already communicates with a Docker daemon can use Swarm to transparently scale to multiple hosts: Dokku, Compose, Krane, Deis, DockerUI, Shipyard, Drone, Jenkins... and, of course, the Docker client itself. ...

  • Spring Cloud

    Spring Cloud

    It provides tools for developers to quickly build some of the common patterns in distributed systems. ...

  • Docker Machine

    Docker Machine

    Machine lets you create Docker hosts on your computer, on cloud providers, and inside your own data center. It creates servers, installs Docker on them, then configures the Docker client to talk to them. ...

  • Portainer

    Portainer

    It is a universal container management tool. It works with Kubernetes, Docker, Docker Swarm and Azure ACI. It allows you to manage containers without needing to know platform-specific code. ...

Metal3 alternatives & related posts

Kubernetes logo

Kubernetes

33.8K
28.3K
601
Manage a cluster of Linux containers as a single system to accelerate Dev and simplify Ops
33.8K
28.3K
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601
PROS OF KUBERNETES
  • 152
    Leading docker container management solution
  • 121
    Simple and powerful
  • 96
    Open source
  • 73
    Backed by google
  • 56
    The right abstractions
  • 24
    Scale services
  • 17
    Replication controller
  • 9
    Permission managment
  • 6
    Simple
  • 5
    Supports autoscaling
  • 5
    Cheap
  • 3
    Reliable
  • 3
    No cloud platform lock-in
  • 3
    Self-healing
  • 3
    Open, powerful, stable
  • 3
    Scalable
  • 3
    Promotes modern/good infrascture practice
  • 2
    Cloud Agnostic
  • 2
    Backed by Red Hat
  • 2
    Custom and extensibility
  • 2
    Quick cloud setup
  • 2
    Captain of Container Ship
  • 2
    A self healing environment with rich metadata
  • 1
    Everything of CaaS
  • 1
    Easy setup
  • 1
    Expandable
  • 1
    Runs on azure
  • 1
    Sfg
  • 1
    Golang
  • 1
    Gke
CONS OF KUBERNETES
  • 13
    Poor workflow for development
  • 11
    Steep learning curve
  • 5
    Orchestrates only infrastructure
  • 2
    High resource requirements for on-prem clusters

related Kubernetes posts

Conor Myhrvold
Tech Brand Mgr, Office of CTO at Uber · | 38 upvotes · 3.7M views

How Uber developed the open source, end-to-end distributed tracing Jaeger , now a CNCF project:

Distributed tracing is quickly becoming a must-have component in the tools that organizations use to monitor their complex, microservice-based architectures. At Uber, our open source distributed tracing system Jaeger saw large-scale internal adoption throughout 2016, integrated into hundreds of microservices and now recording thousands of traces every second.

Here is the story of how we got here, from investigating off-the-shelf solutions like Zipkin, to why we switched from pull to push architecture, and how distributed tracing will continue to evolve:

https://eng.uber.com/distributed-tracing/

(GitHub Pages : https://www.jaegertracing.io/, GitHub: https://github.com/jaegertracing/jaeger)

Bindings/Operator: Python Java Node.js Go C++ Kubernetes JavaScript OpenShift C# Apache Spark

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Yshay Yaacobi

Our first experience with .NET core was when we developed our OSS feature management platform - Tweek (https://github.com/soluto/tweek). We wanted to create a solution that is able to run anywhere (super important for OSS), has excellent performance characteristics and can fit in a multi-container architecture. We decided to implement our rule engine processor in F# , our main service was implemented in C# and other components were built using JavaScript / TypeScript and Go.

Visual Studio Code worked really well for us as well, it worked well with all our polyglot services and the .Net core integration had great cross-platform developer experience (to be fair, F# was a bit trickier) - actually, each of our team members used a different OS (Ubuntu, macos, windows). Our production deployment ran for a time on Docker Swarm until we've decided to adopt Kubernetes with almost seamless migration process.

After our positive experience of running .Net core workloads in containers and developing Tweek's .Net services on non-windows machines, C# had gained back some of its popularity (originally lost to Node.js), and other teams have been using it for developing microservices, k8s sidecars (like https://github.com/Soluto/airbag), cli tools, serverless functions and other projects...

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Docker Compose logo

Docker Compose

13.2K
9.5K
473
Define and run multi-container applications with Docker
13.2K
9.5K
+ 1
473
PROS OF DOCKER COMPOSE
  • 119
    Multi-container descriptor
  • 108
    Fast development environment setup
  • 75
    Easy linking of containers
  • 65
    Simple yaml configuration
  • 58
    Easy setup
  • 15
    Yml or yaml format
  • 11
    Use Standard Docker API
  • 7
    Open source
  • 4
    Go from template to application in minutes
  • 4
    Can choose Discovery Backend
  • 2
    Kubernetes integration
  • 2
    Easy configuration
  • 2
    Scalable
  • 1
    Quick and easy
CONS OF DOCKER COMPOSE
  • 7
    Tied to single machine
  • 4
    Still very volatile, changing syntax often

related Docker Compose posts

Simon Reymann
Senior Fullstack Developer at QUANTUSflow Software GmbH · | 28 upvotes · 2.6M views

Our whole DevOps stack consists of the following tools:

  • GitHub (incl. GitHub Pages/Markdown for Documentation, GettingStarted and HowTo's) for collaborative review and code management tool
  • Respectively Git as revision control system
  • SourceTree as Git GUI
  • Visual Studio Code as IDE
  • CircleCI for continuous integration (automatize development process)
  • Prettier / TSLint / ESLint as code linter
  • SonarQube as quality gate
  • Docker as container management (incl. Docker Compose for multi-container application management)
  • VirtualBox for operating system simulation tests
  • Kubernetes as cluster management for docker containers
  • Heroku for deploying in test environments
  • nginx as web server (preferably used as facade server in production environment)
  • SSLMate (using OpenSSL) for certificate management
  • Amazon EC2 (incl. Amazon S3) for deploying in stage (production-like) and production environments
  • PostgreSQL as preferred database system
  • Redis as preferred in-memory database/store (great for caching)

The main reason we have chosen Kubernetes over Docker Swarm is related to the following artifacts:

  • Key features: Easy and flexible installation, Clear dashboard, Great scaling operations, Monitoring is an integral part, Great load balancing concepts, Monitors the condition and ensures compensation in the event of failure.
  • Applications: An application can be deployed using a combination of pods, deployments, and services (or micro-services).
  • Functionality: Kubernetes as a complex installation and setup process, but it not as limited as Docker Swarm.
  • Monitoring: It supports multiple versions of logging and monitoring when the services are deployed within the cluster (Elasticsearch/Kibana (ELK), Heapster/Grafana, Sysdig cloud integration).
  • Scalability: All-in-one framework for distributed systems.
  • Other Benefits: Kubernetes is backed by the Cloud Native Computing Foundation (CNCF), huge community among container orchestration tools, it is an open source and modular tool that works with any OS.
See more

Recently I have been working on an open source stack to help people consolidate their personal health data in a single database so that AI and analytics apps can be run against it to find personalized treatments. We chose to go with a #containerized approach leveraging Docker #containers with a local development environment setup with Docker Compose and nginx for container routing. For the production environment we chose to pull code from GitHub and build/push images using Jenkins and using Kubernetes to deploy to Amazon EC2.

We also implemented a dashboard app to handle user authentication/authorization, as well as a custom SSO server that runs on Heroku which allows experts to easily visit more than one instance without having to login repeatedly. The #Backend was implemented using my favorite #Stack which consists of FeathersJS on top of Node.js and ExpressJS with PostgreSQL as the main database. The #Frontend was implemented using React, Redux.js, Semantic UI React and the FeathersJS client. Though testing was light on this project, we chose to use AVA as well as ESLint to keep the codebase clean and consistent.

See more
Helm logo

Helm

843
573
10
The Kubernetes Package Manager
843
573
+ 1
10
PROS OF HELM
  • 4
    Infrastructure as code
  • 3
    Open source
  • 2
    Easy setup
  • 1
    Testa­bil­i­ty and re­pro­ducibil­i­ty
CONS OF HELM
    Be the first to leave a con

    related Helm posts

    Emanuel Evans
    Senior Architect at Rainforest QA · | 14 upvotes · 580.4K views

    We recently moved our main applications from Heroku to Kubernetes . The 3 main driving factors behind the switch were scalability (database size limits), security (the inability to set up PostgreSQL instances in private networks), and costs (GCP is cheaper for raw computing resources).

    We prefer using managed services, so we are using Google Kubernetes Engine with Google Cloud SQL for PostgreSQL for our PostgreSQL databases and Google Cloud Memorystore for Redis . For our CI/CD pipeline, we are using CircleCI and Google Cloud Build to deploy applications managed with Helm . The new infrastructure is managed with Terraform .

    Read the blog post to go more in depth.

    See more
    Robert Zuber

    Our backend consists of two major pools of machines. One pool hosts the systems that run our site, manage jobs, and send notifications. These services are deployed within Docker containers orchestrated in Kubernetes. Due to Kubernetes’ ecosystem and toolchain, it was an obvious choice for our fairly statically-defined processes: the rate of change of job types or how many we may need in our internal stack is relatively low.

    The other pool of machines is for running our users’ jobs. Because we cannot dynamically predict demand, what types of jobs our users need to have run, nor the resources required for each of those jobs, we found that Nomad excelled over Kubernetes in this area.

    We’re also using Helm to make it easier to deploy new services into Kubernetes. We create a chart (i.e. package) for each service. This lets us easily roll back new software and gives us an audit trail of what was installed or upgraded.

    See more
    Rancher logo

    Rancher

    778
    1.2K
    644
    Open Source Platform for Running a Private Container Service
    778
    1.2K
    + 1
    644
    PROS OF RANCHER
    • 103
      Easy to use
    • 79
      Open source and totally free
    • 63
      Multi-host docker-compose support
    • 58
      Load balancing and health check included
    • 58
      Simple
    • 44
      Rolling upgrades, green/blue upgrades feature
    • 42
      Dns and service discovery out-of-the-box
    • 37
      Only requires docker
    • 34
      Multitenant and permission management
    • 29
      Easy to use and feature rich
    • 11
      Cross cloud compatible
    • 11
      Does everything needed for a docker infrastructure
    • 8
      Simple and powerful
    • 8
      Next-gen platform
    • 7
      Very Docker-friendly
    • 6
      Support Kubernetes and Swarm
    • 6
      Application catalogs with stack templates (wizards)
    • 6
      Supports Apache Mesos, Docker Swarm, and Kubernetes
    • 6
      Rolling and blue/green upgrades deployments
    • 6
      High Availability service: keeps your app up 24/7
    • 5
      Easy to use service catalog
    • 4
      Very intuitive UI
    • 4
      IaaS-vendor independent, supports hybrid/multi-cloud
    • 4
      Awesome support
    • 3
      Scalable
    • 2
      Requires less infrastructure requirements
    CONS OF RANCHER
    • 7
      Hosting Rancher can be complicated

    related Rancher posts

    Docker Swarm logo

    Docker Swarm

    679
    806
    267
    Native clustering for Docker. Turn a pool of Docker hosts into a single, virtual host.
    679
    806
    + 1
    267
    PROS OF DOCKER SWARM
    • 54
      Docker friendly
    • 45
      Easy to setup
    • 39
      Standard Docker API
    • 37
      Easy to use
    • 22
      Native
    • 21
      Free
    • 12
      Clustering made easy
    • 11
      Simple usage
    • 10
      Integral part of docker
    • 5
      Cross Platform
    • 4
      Labels and annotations
    • 3
      Performance
    • 2
      Shallow learning curve
    • 2
      Easy Networking
    CONS OF DOCKER SWARM
    • 7
      Low adoption

    related Docker Swarm posts

    Yshay Yaacobi

    Our first experience with .NET core was when we developed our OSS feature management platform - Tweek (https://github.com/soluto/tweek). We wanted to create a solution that is able to run anywhere (super important for OSS), has excellent performance characteristics and can fit in a multi-container architecture. We decided to implement our rule engine processor in F# , our main service was implemented in C# and other components were built using JavaScript / TypeScript and Go.

    Visual Studio Code worked really well for us as well, it worked well with all our polyglot services and the .Net core integration had great cross-platform developer experience (to be fair, F# was a bit trickier) - actually, each of our team members used a different OS (Ubuntu, macos, windows). Our production deployment ran for a time on Docker Swarm until we've decided to adopt Kubernetes with almost seamless migration process.

    After our positive experience of running .Net core workloads in containers and developing Tweek's .Net services on non-windows machines, C# had gained back some of its popularity (originally lost to Node.js), and other teams have been using it for developing microservices, k8s sidecars (like https://github.com/Soluto/airbag), cli tools, serverless functions and other projects...

    See more
    Simon Reymann
    Senior Fullstack Developer at QUANTUSflow Software GmbH · | 28 upvotes · 2.6M views

    Our whole DevOps stack consists of the following tools:

    • GitHub (incl. GitHub Pages/Markdown for Documentation, GettingStarted and HowTo's) for collaborative review and code management tool
    • Respectively Git as revision control system
    • SourceTree as Git GUI
    • Visual Studio Code as IDE
    • CircleCI for continuous integration (automatize development process)
    • Prettier / TSLint / ESLint as code linter
    • SonarQube as quality gate
    • Docker as container management (incl. Docker Compose for multi-container application management)
    • VirtualBox for operating system simulation tests
    • Kubernetes as cluster management for docker containers
    • Heroku for deploying in test environments
    • nginx as web server (preferably used as facade server in production environment)
    • SSLMate (using OpenSSL) for certificate management
    • Amazon EC2 (incl. Amazon S3) for deploying in stage (production-like) and production environments
    • PostgreSQL as preferred database system
    • Redis as preferred in-memory database/store (great for caching)

    The main reason we have chosen Kubernetes over Docker Swarm is related to the following artifacts:

    • Key features: Easy and flexible installation, Clear dashboard, Great scaling operations, Monitoring is an integral part, Great load balancing concepts, Monitors the condition and ensures compensation in the event of failure.
    • Applications: An application can be deployed using a combination of pods, deployments, and services (or micro-services).
    • Functionality: Kubernetes as a complex installation and setup process, but it not as limited as Docker Swarm.
    • Monitoring: It supports multiple versions of logging and monitoring when the services are deployed within the cluster (Elasticsearch/Kibana (ELK), Heapster/Grafana, Sysdig cloud integration).
    • Scalability: All-in-one framework for distributed systems.
    • Other Benefits: Kubernetes is backed by the Cloud Native Computing Foundation (CNCF), huge community among container orchestration tools, it is an open source and modular tool that works with any OS.
    See more
    Spring Cloud logo

    Spring Cloud

    640
    582
    0
    Spring helps development teams everywhere build simple, portable,fast and flexible JVM-based systems and applications.
    640
    582
    + 1
    0
    PROS OF SPRING CLOUD
      Be the first to leave a pro
      CONS OF SPRING CLOUD
        Be the first to leave a con

        related Spring Cloud posts

        Spring-Boot Spring Cloud Elasticsearch MySQL Redis RabbitMQ Kafka MongoDB GitHub Linux IntelliJ IDEA

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        Docker Machine logo

        Docker Machine

        414
        481
        12
        Machine management for a container-centric world
        414
        481
        + 1
        12
        PROS OF DOCKER MACHINE
        • 12
          Easy docker hosts management
        CONS OF DOCKER MACHINE
          Be the first to leave a con

          related Docker Machine posts

          Portainer logo

          Portainer

          314
          598
          134
          Open source tool for managing containerized applications
          314
          598
          + 1
          134
          PROS OF PORTAINER
          • 35
            Simple
          • 25
            Great UI
          • 17
            Friendly
          • 12
            Easy to setup, gives a practical interface for Docker
          • 11
            Fully featured
          • 9
            Because it just works, super simple yet powerful
          • 8
            A must for Docker DevOps
          • 6
            Free and opensource
          • 4
            It's simple, fast and the support is great
          • 4
            API
          • 3
            Template Support
          CONS OF PORTAINER
            Be the first to leave a con

            related Portainer posts

            Charles Coleman
            President/CEO at Rapidfyre · | 2 upvotes · 63.2K views
            Shared insights
            on
            Portainer
            Docker

            I've found Portainer to be a like the 8 tooled jacknife I need for Docker and am loving it. Wasn't hard to get up and going and is well rounded enough to do everything I need. Win win.

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            Wallace Alves
            Cyber Security Analyst · | 1 upvote · 548K views

            Docker Docker Compose Portainer ELK Elasticsearch Kibana Logstash nginx

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