Alternatives to Amazon EC2 Container Service logo

Alternatives to Amazon EC2 Container Service

Kubernetes, Google Kubernetes Engine, Amazon EKS, AWS Fargate, and Azure Kubernetes Service are the most popular alternatives and competitors to Amazon EC2 Container Service.
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What is Amazon EC2 Container Service and what are its top alternatives?

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.
Amazon EC2 Container Service is a tool in the Containers as a Service category of a tech stack.

Top Alternatives to Amazon EC2 Container Service

  • 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. ...

  • 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. ...

  • AWS Fargate

    AWS Fargate

    AWS Fargate is a technology for Amazon ECS and EKS* that allows you to run containers without having to manage servers or clusters. With AWS Fargate, you no longer have to provision, configure, and scale clusters of virtual machines to run containers. ...

  • Azure Kubernetes Service

    Azure Kubernetes Service

    Deploy and manage containerized applications more easily with a fully managed Kubernetes service. It offers serverless Kubernetes, an integrated continuous integration and continuous delivery (CI/CD) experience, and enterprise-grade security and governance. Unite your development and operations teams on a single platform to rapidly build, deliver, and scale applications with confidence. ...

  • Docker for AWS

    Docker for AWS

    An integrated, easy-to-deploy environment for building, assembling, and shipping applications on AWS, Docker for AWS is a native AWS application optimized to take optimal advantage of the underlying AWS IaaS services while giving you a modern Docker platform that you can use to deploy portable apps. ...

  • 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. ...

  • Azure Container Service

    Azure Container Service

    Azure Container Service optimizes the configuration of popular open source tools and technologies specifically for Azure. You get an open solution that offers portability for both your containers and your application configuration. You select the size, the number of hosts, and choice of orchestrator tools, and Container Service handles everything else. ...

Amazon EC2 Container Service 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
  • 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
  • 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 | 37 upvotes 路 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

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Yshay Yaacobi

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

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

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

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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
  • 17
    Powered by kubernetes
  • 12
    Docker
  • 11
    Scalable
  • 6
    Open source
  • 2
    Command line interface is intuitive
  • 2
    Decoupled app
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    Provisioning
  • 1
    Declarative management
CONS OF GOOGLE KUBERNETES ENGINE
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    related Google Kubernetes Engine posts

    Omar Mehilba
    Co-Founder and COO at Magalix | 18 upvotes 路 243.8K 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
    Kir Shatrov
    Engineering Lead at Shopify | 15 upvotes 路 504.6K views

    At Shopify, over the years, we moved from shards to the concept of "pods". A pod is a fully isolated instance of Shopify with its own datastores like MySQL, Redis, Memcached. A pod can be spawned in any region. This approach has helped us eliminate global outages. As of today, we have more than a hundred pods, and since moving to this architecture we haven't had any major outages that affected all of Shopify. An outage today only affects a single pod or region.

    As we grew into hundreds of shards and pods, it became clear that we needed a solution to orchestrate those deployments. Today, we use Docker, Kubernetes, and Google Kubernetes Engine to make it easy to bootstrap resources for new Shopify Pods.

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    Amazon EKS logo

    Amazon EKS

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    Highly available and scalable Kubernetes service
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    PROS OF AMAZON EKS
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      CONS OF AMAZON EKS
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        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 G臋bski

        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.

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        AWS Fargate logo

        AWS Fargate

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        Run Containers Without Managing Infrastructure
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        PROS OF AWS FARGATE
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          CONS OF AWS FARGATE
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            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) )

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            Azure Kubernetes Service logo

            Azure Kubernetes Service

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            Simplify Kubernetes management, deployment, and operations.
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            PROS OF AZURE KUBERNETES SERVICE
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              CONS OF AZURE KUBERNETES SERVICE
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                Farzad Jalali
                Senior Software Architect at BerryWorld | 8 upvotes 路 159.1K views

                Visual Studio Azure DevOps Azure Functions Azure Websites #Azure #AzureKeyVault #AzureAD #AzureApps

                #Azure Cloud Since Amazon is potentially our competitor then we need a different cloud vendor, also our programmers are microsoft oriented so the choose were obviously #Azure for us.

                Azure DevOps Because we need to be able to develop a neww pipeline into Azure environment ina few minutes.

                Azure Kubernetes Service We already in #Azure , also need to use K8s , so let's use AKS as it's a manged Kubernetes in the #Azure

                See more
                Docker for AWS logo

                Docker for AWS

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                Install a Swarm of Docker Engines secured end to end with TLS by default on AWS
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                    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
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                      Seamless transition from docker compose
                    CONS OF DOCKER CLOUD
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                      related Docker Cloud posts

                      Azure Container Service logo

                      Azure Container Service

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                      Deploy and manage containers using the tools you choose
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                      PROS OF AZURE CONTAINER SERVICE
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                        Easy to setup, very agnostic
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                        It supports Kubernetes, Mesos DC/OS and Docker Swarm
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                        It has a nice command line interface (CLI) tool
                      CONS OF AZURE CONTAINER SERVICE
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