Alternatives to Mesosphere logo

Alternatives to Mesosphere

Kubernetes, Rancher, Red Hat OpenShift, Apache Mesos, and CoreOS are the most popular alternatives and competitors to Mesosphere.
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What is Mesosphere and what are its top alternatives?

Mesosphere offers a layer of software that organizes your machines, VMs, and cloud instances and lets applications draw from a single pool of intelligently- and dynamically-allocated resources, increasing efficiency and reducing operational complexity.
Mesosphere is a tool in the Cluster Management category of a tech stack.

Top Alternatives to Mesosphere

  • 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. ...

  • 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. ...

  • Red Hat OpenShift

    Red Hat OpenShift

    OpenShift is Red Hat's Cloud Computing Platform as a Service (PaaS) offering. OpenShift is an application platform in the cloud where application developers and teams can build, test, deploy, and run their applications. ...

  • Apache Mesos

    Apache Mesos

    Apache Mesos is a cluster manager that simplifies the complexity of running applications on a shared pool of servers. ...

  • CoreOS

    CoreOS

    It is designed for security, consistency, and reliability. Instead of installing packages via yum or apt, it uses Linux containers to manage your services at a higher level of abstraction. A single service's code and all dependencies are packaged within a container that can be run on one or many machines. ...

  • Cloud Foundry

    Cloud Foundry

    Cloud Foundry is an open platform as a service (PaaS) that provides a choice of clouds, developer frameworks, and application services. Cloud Foundry makes it faster and easier to build, test, deploy, and scale applications. ...

  • 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 ...

  • OpenStack

    OpenStack

    OpenStack is a cloud operating system that controls large pools of compute, storage, and networking resources throughout a datacenter, all managed through a dashboard that gives administrators control while empowering their users to provision resources through a web interface. ...

Mesosphere alternatives & related posts

Kubernetes logo

Kubernetes

30.2K
24.8K
591
Manage a cluster of Linux containers as a single system to accelerate Dev and simplify Ops
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PROS OF KUBERNETES
  • 151
    Leading docker container management solution
  • 121
    Simple and powerful
  • 95
    Open source
  • 70
    Backed by google
  • 55
    The right abstractions
  • 24
    Scale services
  • 16
    Replication controller
  • 9
    Permission managment
  • 6
    Simple
  • 5
    Supports autoscaling
  • 5
    Cheap
  • 3
    Promotes modern/good infrascture practice
  • 3
    No cloud platform lock-in
  • 3
    Self-healing
  • 3
    Open, powerful, stable
  • 3
    Scalable
  • 3
    Reliable
  • 2
    A self healing environment with rich metadata
  • 2
    Captain of Container Ship
  • 2
    Quick cloud setup
  • 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 · 3.4M 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...

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Rancher logo

Rancher

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1.1K
642
Open Source Platform for Running a Private Container Service
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1.1K
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PROS OF RANCHER
  • 102
    Easy to use
  • 79
    Open source and totally free
  • 62
    Multi-host docker-compose support
  • 58
    Simple
  • 58
    Load balancing and health check included
  • 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

Red Hat OpenShift logo

Red Hat OpenShift

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1.1K
475
Red Hat's free Platform as a Service (PaaS) for hosting Java, PHP, Ruby, Python, Node.js, and Perl apps
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PROS OF RED HAT OPENSHIFT
  • 97
    Good free plan
  • 61
    Open Source
  • 45
    Easy setup
  • 41
    Nodejs support
  • 38
    Well documented
  • 31
    Custom domains
  • 27
    Mongodb support
  • 26
    Clean and simple architecture
  • 24
    PHP support
  • 20
    Customizable environments
  • 10
    Ability to run CRON jobs
  • 8
    Easier than Heroku for a WordPress blog
  • 6
    Good balance between Heroku and AWS for flexibility
  • 6
    PostgreSQL support
  • 6
    Easy deployment
  • 5
    Autoscaling
  • 4
    Shell access to gears
  • 4
    Free, Easy Setup, Lot of Gear or D.I.Y Gear
  • 3
    Great Support
  • 2
    Overly complicated and over engineered in majority of e
  • 2
    Golang support
  • 2
    Its free and offer custom domain usage
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    because it is easy to manage
  • 1
    No credit card needed
  • 1
    Autoscaling at a good price point
  • 1
    Easy setup and great customer support
  • 1
    This is the only free one among the three as of today
  • 1
    Meteor support
  • 1
    Great free plan with excellent support
CONS OF RED HAT OPENSHIFT
  • 2
    Decisions are made for you, limiting your options
  • 2
    License cost
  • 1
    Behind, sometimes severely, the upstreams

related Red Hat OpenShift posts

Conor Myhrvold
Tech Brand Mgr, Office of CTO at Uber · | 37 upvotes · 3.4M 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
Michael Ionita

We use Kubernetes because we decided to migrate to a hosted cluster (not AWS) and still be able to scale our clusters up and down depending on load. By wrapping it with OpenShift we are now able to easily adapt to demand but also able to separate concerns into separate Pods depending on use-cases we have.

See more
Apache Mesos logo

Apache Mesos

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Develop and run resource-efficient distributed systems
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PROS OF APACHE MESOS
  • 20
    Easy scaling
  • 6
    Web UI
  • 2
    Fault-Tolerant
  • 1
    Elastic Distributed System
  • 1
    High-Available
CONS OF APACHE MESOS
  • 1
    Not for long term
  • 1
    Depends on Zookeeper

related Apache Mesos posts

Docker containers on Mesos run their microservices with consistent configurations at scale, along with Aurora for long-running services and cron jobs.

See more
CoreOS logo

CoreOS

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Linux for Massive Server Deployments
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PROS OF COREOS
  • 21
    Container management
  • 15
    Lightweight
  • 11
    Systemd
CONS OF COREOS
  • 1
    End-of-lifed

related CoreOS posts

Cloud Foundry logo

Cloud Foundry

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4
Deploy and scale applications in seconds on your choice of private or public cloud
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PROS OF CLOUD FOUNDRY
  • 1
    Perfectly aligned with springboot
  • 1
    Free distributed tracing (zipkin)
  • 1
    Application health management
  • 1
    Free service discovery (Eureka)
CONS OF CLOUD FOUNDRY
    Be the first to leave a con

    related Cloud Foundry posts

    Docker logo

    Docker

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    Enterprise Container Platform for High-Velocity Innovation.
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    PROS OF DOCKER
    • 816
      Rapid integration and build up
    • 687
      Isolation
    • 514
      Open source
    • 501
      Testa­bil­i­ty and re­pro­ducibil­i­ty
    • 456
      Lightweight
    • 214
      Standardization
    • 181
      Scalable
    • 104
      Upgrading / down­grad­ing / ap­pli­ca­tion versions
    • 85
      Security
    • 82
      Private paas environments
    • 32
      Portability
    • 24
      Limit resource usage
    • 14
      I love the way docker has changed virtualization
    • 14
      Game changer
    • 12
      Fast
    • 10
      Concurrency
    • 6
      Docker's Compose tools
    • 3
      Because its fun
    • 3
      Easy setup
    • 3
      Fast and Portable
    • 2
      Makes shipping to production very simple
    • 2
      It's dope
    • 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
      Open source and highly configurable
    • 1
      Simplicity, isolation, resource effective
    • 1
      Highly useful
    • 1
      MacOS support FAKE
    CONS OF DOCKER
    • 7
      New versions == broken features
    • 4
      Documentation not always in sync
    • 3
      Moves quickly
    • 3
      Unreliable networking

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    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
    OpenStack logo

    OpenStack

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    102
    Open source software for building private and public clouds
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    PROS OF OPENSTACK
    • 43
      Private cloud
    • 34
      Avoid vendor lock-in
    • 18
      Flexible in use
    • 4
      Industry leader
    • 2
      Supported by many companies in top500
    • 1
      Robust architecture
    CONS OF OPENSTACK
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