Alternatives to Rancher logo

Alternatives to Rancher

Kubernetes, DC/OS, Portainer, Docker, and Helm are the most popular alternatives and competitors to Rancher.
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What is Rancher and what are its top alternatives?

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.
Rancher is a tool in the Container Tools category of a tech stack.
Rancher is an open source tool with 16.2K GitHub stars and 2K GitHub forks. Here’s a link to Rancher's open source repository on GitHub

Top Alternatives to Rancher

  • 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. ...

  • DC/OS

    DC/OS

    Unlike traditional operating systems, DC/OS spans multiple machines within a network, aggregating their resources to maximize utilization by distributed applications. ...

  • Portainer

    Portainer

    Portainer is an open-source lightweight management UI which allows you to easily manage your Docker environments. Portainer is available on Windows, Linux and Mac. It has never been so easy to manage Docker ! ...

  • Docker

    Docker

    The Docker Platform is the industry-leading container platform for continuous, high-velocity innovation, enabling organizations to seamlessly build and share any application ‚ÄĒ from legacy to what comes next ‚ÄĒ and securely run them anywhere ...

  • Helm

    Helm

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

  • Cowboy

    Cowboy

    Cowboy aims to provide a complete HTTP stack in a small code base. It is optimized for low latency and low memory usage, in part because it uses binary strings. Cowboy provides routing capabilities, selectively dispatching requests to handlers written in Erlang. ...

  • 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. ...

  • 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. ...

Rancher alternatives & related posts

Kubernetes logo

Kubernetes

30.5K
25.1K
595
Manage a cluster of Linux containers as a single system to accelerate Dev and simplify Ops
30.5K
25.1K
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PROS OF KUBERNETES
  • 152
    Leading docker container management solution
  • 121
    Simple and powerful
  • 96
    Open source
  • 71
    Backed by google
  • 55
    The right abstractions
  • 24
    Scale services
  • 17
    Replication controller
  • 9
    Permission managment
  • 6
    Simple
  • 5
    Cheap
  • 5
    Supports autoscaling
  • 3
    Promotes modern/good infrascture practice
  • 3
    Reliable
  • 3
    No cloud platform lock-in
  • 3
    Self-healing
  • 3
    Open, powerful, stable
  • 3
    Scalable
  • 2
    Quick cloud setup
  • 2
    A self healing environment with rich metadata
  • 2
    Captain of Container Ship
  • 1
    Custom and extensibility
  • 1
    Expandable
  • 1
    Easy setup
  • 1
    Gke
  • 1
    Golang
  • 1
    Backed by Red Hat
  • 1
    Everything of CaaS
  • 1
    Runs on azure
  • 1
    Cloud Agnostic
  • 1
    Sfg
CONS OF KUBERNETES
  • 13
    Poor workflow for development
  • 10
    Steep learning curve
  • 4
    Orchestrates only infrastructure
  • 2
    High resource requirements for on-prem clusters

related Kubernetes posts

Conor Myhrvold
Tech Brand Mgr, Office of CTO at Uber · | 37 upvotes · 3.5M 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

See more
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
DC/OS logo

DC/OS

110
158
12
The Datacenter Operating System. The easiest way to run microservices, big data, and containers in production.
110
158
+ 1
12
PROS OF DC/OS
  • 5
    Easy to setup a HA cluster
  • 3
    Open source
  • 2
    Has templates to install via AWS and Azure
  • 1
    Easy Setup
  • 1
    Easy to get services running and operate them
CONS OF DC/OS
    Be the first to leave a con

    related DC/OS posts

    Portainer logo

    Portainer

    295
    561
    133
    Simple management UI for Docker
    295
    561
    + 1
    133
    PROS OF PORTAINER
    • 34
      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 · 51.4K views
      Shared insights
      on
      PortainerPortainerDockerDocker

      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.

      See more
      Wallace Alves
      Cyber Security Analyst · | 1 upvote · 524.3K views

      Docker Docker Compose Portainer ELK Elasticsearch Kibana Logstash nginx

      See more
      Docker logo

      Docker

      91.7K
      71.3K
      3.8K
      Enterprise Container Platform for High-Velocity Innovation.
      91.7K
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      PROS OF DOCKER
      • 817
        Rapid integration and build up
      • 688
        Isolation
      • 515
        Open source
      • 502
        Testa­bil­i­ty and re­pro­ducibil­i­ty
      • 457
        Lightweight
      • 215
        Standardization
      • 180
        Scalable
      • 104
        Upgrading / down­grad­ing / ap­pli­ca­tion versions
      • 86
        Security
      • 83
        Private paas environments
      • 33
        Portability
      • 25
        Limit resource usage
      • 15
        I love the way docker has changed virtualization
      • 15
        Game changer
      • 12
        Fast
      • 11
        Concurrency
      • 7
        Docker's Compose tools
      • 4
        Because its fun
      • 4
        Easy setup
      • 4
        Fast and Portable
      • 3
        Makes shipping to production very simple
      • 2
        It's dope
      • 1
        Open source and highly configurable
      • 1
        Simplicity, isolation, resource effective
      • 1
        Highly useful
      • 1
        MacOS support FAKE
      • 1
        Its cool
      • 1
        Docker hub for the FTW
      • 1
        Package the environment with the application
      • 1
        Very easy to setup integrate and build
      CONS OF DOCKER
      • 7
        New versions == broken features
      • 4
        Documentation not always in sync
      • 3
        Moves quickly
      • 3
        Unreliable networking

      related Docker posts

      Simon Reymann
      Senior Fullstack Developer at QUANTUSflow Software GmbH · | 28 upvotes · 2.2M 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
      Tymoteusz Paul
      Devops guy at X20X Development LTD · | 21 upvotes · 4M views

      Often enough I have to explain my way of going about setting up a CI/CD pipeline with multiple deployment platforms. Since I am a bit tired of yapping the same every single time, I've decided to write it up and share with the world this way, and send people to read it instead ;). I will explain it on "live-example" of how the Rome got built, basing that current methodology exists only of readme.md and wishes of good luck (as it usually is ;)).

      It always starts with an app, whatever it may be and reading the readmes available while Vagrant and VirtualBox is installing and updating. Following that is the first hurdle to go over - convert all the instruction/scripts into Ansible playbook(s), and only stopping when doing a clear vagrant up or vagrant reload we will have a fully working environment. As our Vagrant environment is now functional, it's time to break it! This is the moment to look for how things can be done better (too rigid/too lose versioning? Sloppy environment setup?) and replace them with the right way to do stuff, one that won't bite us in the backside. This is the point, and the best opportunity, to upcycle the existing way of doing dev environment to produce a proper, production-grade product.

      I should probably digress here for a moment and explain why. I firmly believe that the way you deploy production is the same way you should deploy develop, shy of few debugging-friendly setting. This way you avoid the discrepancy between how production work vs how development works, which almost always causes major pains in the back of the neck, and with use of proper tools should mean no more work for the developers. That's why we start with Vagrant as developer boxes should be as easy as vagrant up, but the meat of our product lies in Ansible which will do meat of the work and can be applied to almost anything: AWS, bare metal, docker, LXC, in open net, behind vpn - you name it.

      We must also give proper consideration to monitoring and logging hoovering at this point. My generic answer here is to grab Elasticsearch, Kibana, and Logstash. While for different use cases there may be better solutions, this one is well battle-tested, performs reasonably and is very easy to scale both vertically (within some limits) and horizontally. Logstash rules are easy to write and are well supported in maintenance through Ansible, which as I've mentioned earlier, are at the very core of things, and creating triggers/reports and alerts based on Elastic and Kibana is generally a breeze, including some quite complex aggregations.

      If we are happy with the state of the Ansible it's time to move on and put all those roles and playbooks to work. Namely, we need something to manage our CI/CD pipelines. For me, the choice is obvious: TeamCity. It's modern, robust and unlike most of the light-weight alternatives, it's transparent. What I mean by that is that it doesn't tell you how to do things, doesn't limit your ways to deploy, or test, or package for that matter. Instead, it provides a developer-friendly and rich playground for your pipelines. You can do most the same with Jenkins, but it has a quite dated look and feel to it, while also missing some key functionality that must be brought in via plugins (like quality REST API which comes built-in with TeamCity). It also comes with all the common-handy plugins like Slack or Apache Maven integration.

      The exact flow between CI and CD varies too greatly from one application to another to describe, so I will outline a few rules that guide me in it: 1. Make build steps as small as possible. This way when something breaks, we know exactly where, without needing to dig and root around. 2. All security credentials besides development environment must be sources from individual Vault instances. Keys to those containers should exist only on the CI/CD box and accessible by a few people (the less the better). This is pretty self-explanatory, as anything besides dev may contain sensitive data and, at times, be public-facing. Because of that appropriate security must be present. TeamCity shines in this department with excellent secrets-management. 3. Every part of the build chain shall consume and produce artifacts. If it creates nothing, it likely shouldn't be its own build. This way if any issue shows up with any environment or version, all developer has to do it is grab appropriate artifacts to reproduce the issue locally. 4. Deployment builds should be directly tied to specific Git branches/tags. This enables much easier tracking of what caused an issue, including automated identifying and tagging the author (nothing like automated regression testing!).

      Speaking of deployments, I generally try to keep it simple but also with a close eye on the wallet. Because of that, I am more than happy with AWS or another cloud provider, but also constantly peeking at the loads and do we get the value of what we are paying for. Often enough the pattern of use is not constantly erratic, but rather has a firm baseline which could be migrated away from the cloud and into bare metal boxes. That is another part where this approach strongly triumphs over the common Docker and CircleCI setup, where you are very much tied in to use cloud providers and getting out is expensive. Here to embrace bare-metal hosting all you need is a help of some container-based self-hosting software, my personal preference is with Proxmox and LXC. Following that all you must write are ansible scripts to manage hardware of Proxmox, similar way as you do for Amazon EC2 (ansible supports both greatly) and you are good to go. One does not exclude another, quite the opposite, as they can live in great synergy and cut your costs dramatically (the heavier your base load, the bigger the savings) while providing production-grade resiliency.

      See more
      Helm logo

      Helm

      782
      529
      10
      The Kubernetes Package Manager
      782
      529
      + 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 · | 13 upvotes · 527.6K 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
        Ido Shamun
        at The Elegant Monkeys · | 6 upvotes · 284K views

        Kubernetes powers our #backend services as it is very easy in terms of #devops (the managed version). We deploy everything using @helm charts as it provides us to manage deployments the same way we manage our code on GitHub . On every commit a CircleCI job is triggered to run the tests, build Docker images and deploy them to the registry. Finally on every master commit CircleCI also deploys the relevant service using Helm chart to our Kubernetes cluster

        See more
        Cowboy logo

        Cowboy

        600
        54
        19
        Small, fast, modular HTTP server written in Erlang.
        600
        54
        + 1
        19
        PROS OF COWBOY
        • 8
          Websockets integration
        • 6
          Cool name
        • 3
          Good to use with Erlang
        • 2
          Anime mascot
        CONS OF COWBOY
          Be the first to leave a con

          related Cowboy posts

          Docker Compose logo

          Docker Compose

          12.1K
          8.6K
          471
          Define and run multi-container applications with Docker
          12.1K
          8.6K
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          PROS OF DOCKER COMPOSE
          • 118
            Multi-container descriptor
          • 107
            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
            Can choose Discovery Backend
          • 4
            Go from template to application in minutes
          • 2
            Scalable
          • 2
            Easy configuration
          • 2
            Kubernetes integration
          • 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 · 2.2M 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
          Docker Swarm logo

          Docker Swarm

          649
          784
          252
          Native clustering for Docker. Turn a pool of Docker hosts into a single, virtual host.
          649
          784
          + 1
          252
          PROS OF DOCKER SWARM
          • 53
            Docker friendly
          • 43
            Easy to setup
          • 38
            Standard Docker API
          • 35
            Easy to use
          • 21
            Native
          • 20
            Free
          • 11
            Clustering made easy
          • 10
            Simple usage
          • 9
            Integral part of docker
          • 4
            Cross Platform
          • 2
            Easy Networking
          • 2
            Shallow learning curve
          • 2
            Labels and annotations
          • 2
            Performance
          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 · 2.2M 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