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 18.1K GitHub stars and 2.4K 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

    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. ...

  • 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 — from legacy to what comes next — 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. ...

  • Spring Cloud

    Spring Cloud

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

Rancher alternatives & related posts

Kubernetes logo

Kubernetes

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Manage a cluster of Linux containers as a single system to accelerate Dev and simplify Ops
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PROS OF KUBERNETES
  • 159
    Leading docker container management solution
  • 124
    Simple and powerful
  • 101
    Open source
  • 75
    Backed by google
  • 56
    The right abstractions
  • 24
    Scale services
  • 18
    Replication controller
  • 9
    Permission managment
  • 7
    Simple
  • 7
    Supports autoscaling
  • 6
    Cheap
  • 4
    Self-healing
  • 4
    Reliable
  • 4
    No cloud platform lock-in
  • 3
    Open, powerful, stable
  • 3
    Scalable
  • 3
    Quick cloud setup
  • 3
    Promotes modern/good infrascture practice
  • 2
    Backed by Red Hat
  • 2
    Runs on azure
  • 2
    Cloud Agnostic
  • 2
    Custom and extensibility
  • 2
    Captain of Container Ship
  • 2
    A self healing environment with rich metadata
  • 1
    Golang
  • 1
    Easy setup
  • 1
    Everything of CaaS
  • 1
    Sfg
  • 1
    Expandable
  • 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 · | 39 upvotes · 4.2M 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...

See more
DC/OS logo

DC/OS

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The Datacenter Operating System. The easiest way to run microservices, big data, and containers in production.
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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
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    related DC/OS posts

    Portainer logo

    Portainer

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    Open source tool for managing containerized applications
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    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
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      related Portainer posts

      Charles Coleman
      President/CEO at Rapidfyre · | 2 upvotes · 83K 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 · 585.8K views

      Docker Docker Compose Portainer ELK Elasticsearch Kibana Logstash nginx

      See more
      Docker logo

      Docker

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      Enterprise Container Platform for High-Velocity Innovation.
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      PROS OF DOCKER
      • 821
        Rapid integration and build up
      • 688
        Isolation
      • 517
        Open source
      • 505
        Testa­bil­i­ty and re­pro­ducibil­i­ty
      • 459
        Lightweight
      • 217
        Standardization
      • 182
        Scalable
      • 105
        Upgrading / down­grad­ing / ap­pli­ca­tion versions
      • 86
        Security
      • 84
        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
        Fast and Portable
      • 4
        Easy setup
      • 4
        Because its fun
      • 3
        Makes shipping to production very simple
      • 2
        It's dope
      • 1
        Highly useful
      • 1
        MacOS support FAKE
      • 1
        Its cool
      • 1
        Docker hub for the FTW
      • 1
        Very easy to setup integrate and build
      • 1
        Package the environment with the application
      • 1
        Does a nice job hogging memory
      • 1
        Open source and highly configurable
      • 1
        Simplicity, isolation, resource effective
      CONS OF DOCKER
      • 7
        New versions == broken features
      • 5
        Documentation not always in sync
      • 5
        Unreliable networking
      • 3
        Moves quickly
      • 2
        Not Secure

      related Docker posts

      Simon Reymann
      Senior Fullstack Developer at QUANTUSflow Software GmbH · | 28 upvotes · 3.3M 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 · | 23 upvotes · 4.6M 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

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      The Kubernetes Package Manager
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      PROS OF HELM
      • 5
        Infrastructure as code
      • 3
        Open source
      • 2
        Easy setup
      • 1
        Testa­bil­i­ty and re­pro­ducibil­i­ty
      CONS OF HELM
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        related Helm posts

        Emanuel Evans
        Senior Architect at Rainforest QA · | 16 upvotes · 703K 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
        Cowboy logo

        Cowboy

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        58
        19
        Small, fast, modular HTTP server written in Erlang.
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        58
        + 1
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        PROS OF COWBOY
        • 8
          Websockets integration
        • 6
          Cool name
        • 3
          Good to use with Erlang
        • 2
          Anime mascot
        CONS OF COWBOY
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          related Cowboy posts

          Docker Compose logo

          Docker Compose

          14.9K
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          477
          Define and run multi-container applications with Docker
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          PROS OF DOCKER COMPOSE
          • 121
            Multi-container descriptor
          • 109
            Fast development environment setup
          • 75
            Easy linking of containers
          • 66
            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
          • 8
            Tied to single machine
          • 5
            Still very volatile, changing syntax often

          related Docker Compose posts

          Simon Reymann
          Senior Fullstack Developer at QUANTUSflow Software GmbH · | 28 upvotes · 3.3M 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
          Spring Cloud logo

          Spring Cloud

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          0
          Spring helps development teams everywhere build simple, portable,fast and flexible JVM-based systems and applications.
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          PROS OF SPRING CLOUD
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            CONS OF SPRING CLOUD
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              related Spring Cloud posts

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

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