Alternatives to Docker Datacenter logo

Alternatives to Docker Datacenter

Kubernetes, Docker Swarm, Red Hat OpenShift, Docker Cloud, and Cloud Foundry are the most popular alternatives and competitors to Docker Datacenter.
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What is Docker Datacenter and what are its top alternatives?

Docker Datacenter is an integrated solution including open source and commercial software, the integrations between them, full Docker API support, validated configurations and commercial support for your Docker Datacenter environment.
Docker Datacenter is a tool in the Containers as a Service category of a tech stack.

Top Alternatives to Docker Datacenter

  • 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 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. ...

  • 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. ...

  • Docker Cloud

    Docker Cloud

    Docker Cloud is the best way to deploy and manage Dockerized applications. Docker Cloud makes it easy for new Docker users to manage and deploy the full spectrum of applications, from single container apps to distributed microservices stacks, to any cloud or on-premises infrastructure. ...

  • 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. ...

  • Amazon EC2 Container Service

    Amazon EC2 Container Service

    Amazon EC2 Container Service lets you launch and stop container-enabled applications with simple API calls, allows you to query the state of your cluster from a centralized service, and gives you access to many familiar Amazon EC2 features like security groups, EBS volumes and IAM roles. ...

  • Google Kubernetes Engine

    Google Kubernetes Engine

    Container Engine takes care of provisioning and maintaining the underlying virtual machine cluster, scaling your application, and operational logistics like logging, monitoring, and health management. ...

  • Amazon EKS

    Amazon EKS

    Amazon Elastic Container Service for Kubernetes (Amazon EKS) is a managed service that makes it easy for you to run Kubernetes on AWS without needing to install and operate your own Kubernetes clusters. ...

Docker Datacenter 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

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
Docker Swarm logo

Docker Swarm

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Native clustering for Docker. Turn a pool of Docker hosts into a single, virtual host.
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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 · 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
Red Hat OpenShift logo

Red Hat OpenShift

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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
  • 39
    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
    PostgreSQL support
  • 6
    Autoscaling
  • 6
    Easy deployment
  • 6
    Good balance between Heroku and AWS for flexibility
  • 5
    Free, Easy Setup, Lot of Gear or D.I.Y Gear
  • 4
    Shell access to gears
  • 3
    Great Support
  • 2
    Overly complicated and over engineered in majority of e
  • 2
    Golang support
  • 2
    Its free and offer custom domain usage
  • 1
    Meteor support
  • 1
    Easy setup and great customer support
  • 1
    High Security
  • 1
    No credit card needed
  • 1
    because it is easy to manage
  • 1
    Logging & Metrics
  • 1
    Autoscaling at a good price point
  • 1
    Great free plan with excellent support
  • 1
    This is the only free one among the three as of today
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 · | 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

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
Docker Cloud logo

Docker Cloud

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A hosted service for Docker container management and deployment
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PROS OF DOCKER CLOUD
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    Easy to use
  • 2
    Seamless transition from docker compose
CONS OF DOCKER CLOUD
    Be the first to leave a con

    related Docker Cloud posts

    Cloud Foundry logo

    Cloud Foundry

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    Deploy and scale applications in seconds on your choice of private or public cloud
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    PROS OF CLOUD FOUNDRY
    • 2
      Perfectly aligned with springboot
    • 1
      Free distributed tracing (zipkin)
    • 1
      Application health management
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      Free service discovery (Eureka)
    CONS OF CLOUD FOUNDRY
      Be the first to leave a con

      related Cloud Foundry posts

      Amazon EC2 Container Service logo

      Amazon EC2 Container Service

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      Container management service that supports Docker containers
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      PROS OF AMAZON EC2 CONTAINER SERVICE
      • 99
        Backed by amazon
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        Familiar to ec2
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        Cluster based
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        Simple API
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        Iam roles
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        Cluster management
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        Programmatic Control
      • 7
        Scheduler
      • 4
        Socker support
      • 4
        Container-enabled applications
      • 1
        No additional cost
      • 1
        Easy to use and cheap
      CONS OF AMAZON EC2 CONTAINER SERVICE
        Be the first to leave a con

        related Amazon EC2 Container Service posts

        Cyril Duchon-Doris

        We build a Slack app using the Bolt framework from slack https://api.slack.com/tools/bolt, a Node.js express app. It allows us to easily implement some administration features so we can easily communicate with our backend services, and we don't have to develop any frontend app since Slack block kit will do this for us. It can act as a Chatbot or handle message actions and custom slack flows for our employees.

        This app is deployed as a microservice on Amazon EC2 Container Service with AWS Fargate. It uses very little memory (and money) and can communicate easily with our backend services. Slack is connected to this app through a ALB ( AWS Elastic Load Balancing (ELB) )

        See more

        We started using Amazon EC2 Container Service 3 years ago because it was the easiest containers orchestration tool to start with. At the time it was missing a lot of features compared to other tools, but it was still the fastest way to deploy a container on AWS. As with any AWS product, over time they caught up and improved it significantly. Today it probably one of the best tools in its category. It might not have all the feature Kubernetes has, but it also has less complexity. And it definitely has all the features a small company/team needs.

        See more
        Google Kubernetes Engine logo

        Google Kubernetes Engine

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        Deploy, manage, and scale containerized applications on Kubernetes, powered by Google Cloud
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        PROS OF GOOGLE KUBERNETES ENGINE
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          Backed by Google
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          Powered by kubernetes
        • 12
          Docker
        • 11
          Scalable
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          Open source
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          Command line interface is intuitive
        • 2
          Decoupled app
        • 1
          Provisioning
        • 1
          Declarative management
        CONS OF GOOGLE KUBERNETES ENGINE
          Be the first to leave a con

          related Google Kubernetes Engine posts

          Omar Mehilba
          Co-Founder and COO at Magalix · | 19 upvotes · 260.2K views

          We are hardcore Kubernetes users and contributors. We loved the automation it provides. However, as our team grew and added more clusters and microservices, capacity and resources management becomes a massive pain to us. We started suffering from a lot of outages and unexpected behavior as we promote our code from dev to production environments. Luckily we were working on our AI-powered tools to understand different dependencies, predict usage, and calculate the right resources and configurations that should be applied to our infrastructure and microservices. We dogfooded our agent (http://github.com/magalixcorp/magalix-agent) and were able to stabilize as the #autopilot continuously recovered any miscalculations we made or because of unexpected changes in workloads. We are open sourcing our agent in a few days. Check it out and let us know what you think! We run workloads on Microsoft Azure Google Kubernetes Engine and Amazon EC2 and we're all about Go and Python!

          See more
          Emanuel Evans
          Senior Architect at Rainforest QA · | 16 upvotes · 700.2K 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
          Amazon EKS logo

          Amazon EKS

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          Highly available and scalable Kubernetes service
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          PROS OF AMAZON EKS
            Be the first to leave a pro
            CONS OF AMAZON EKS
              Be the first to leave a con

              related Amazon EKS posts

              We are looking for a centralised monitoring solution for our application deployed on Amazon EKS. We would like to monitor using metrics from Kubernetes, AWS services (NeptuneDB, AWS Elastic Load Balancing (ELB), Amazon EBS, Amazon S3, etc) and application microservice's custom metrics.

              We are expected to use around 80 microservices (not replicas). I think a total of 200-250 microservices will be there in the system with 10-12 slave nodes.

              We tried Prometheus but it looks like maintenance is a big issue. We need to manage scaling, maintaining the storage, and dealing with multiple exporters and Grafana. I felt this itself needs few dedicated resources (at least 2-3 people) to manage. Not sure if I am thinking in the correct direction. Please confirm.

              You mentioned Datadog and Sysdig charges per host. Does it charge per slave node?

              See more
              Sebastian Gębski

              Heroku was a decent choice to start a business, but at some point our platform was too big, too complex & too heterogenic, so Heroku started to be a constraint, not a benefit. First, we've started containerizing our apps with Docker to eliminate "works in my machine" syndrome & uniformize the environment setup. The first orchestration was composed with Docker Compose , but at some point it made sense to move it to Kubernetes. Fortunately, we've made a very good technical decision when starting our work with containers - all the container configuration & provisions HAD (since the beginning) to be done in code (Infrastructure as Code) - we've used Terraform & Ansible for that (correspondingly). This general trend of containerisation was accompanied by another, parallel & equally big project: migrating environments from Heroku to AWS: using Amazon EC2 , Amazon EKS, Amazon S3 & Amazon RDS.

              See more