Alternatives to Azure Container Service logo

Alternatives to Azure Container Service

Kubernetes, Docker, Azure Kubernetes Service, Azure App Service, and Azure Container Instances are the most popular alternatives and competitors to Azure Container Service.
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What is Azure Container Service and what are its top alternatives?

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

Top Alternatives to Azure 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. ...

  • 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 ...

  • 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. ...

  • Azure App Service
    Azure App Service

    Quickly build, deploy, and scale web apps created with popular frameworks .NET, .NET Core, Node.js, Java, PHP, Ruby, or Python, in containers or running on any operating system. Meet rigorous, enterprise-grade performance, security, and compliance requirements by using the fully managed platform for your operational and monitoring tasks. ...

  • Azure Container Instances
    Azure Container Instances

    It is a solution for any scenario that can operate in isolated containers, without orchestration. Run event-driven applications, quickly deploy from your container development pipelines, and run data processing and build jobs. ...

  • 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. ...

Azure 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
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    Leading docker container management solution
  • 126
    Simple and powerful
  • 103
    Open source
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    Backed by google
  • 56
    The right abstractions
  • 24
    Scale services
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    Replication controller
  • 10
    Permission managment
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    Cheap
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    Supports autoscaling
  • 7
    Simple
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    Reliable
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    Self-healing
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    No cloud platform lock-in
  • 3
    Quick cloud setup
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    Open, powerful, stable
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    Scalable
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    Promotes modern/good infrascture practice
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    Captain of Container Ship
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    A self healing environment with rich metadata
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    Cloud Agnostic
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    Runs on azure
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    Backed by Red Hat
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    Custom and extensibility
  • 1
    Golang
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    Expandable
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    Gke
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    Easy setup
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    Sfg
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    Everything of CaaS
CONS OF KUBERNETES
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    Poor workflow for development
  • 14
    Steep learning curve
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    Orchestrates only infrastructure
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    High resource requirements for on-prem clusters
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    Too heavy for simple systems
  • 1
    Additional Technology Overhead
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    More moving parts to secure
  • 1
    Additional vendor lock-in (Docker)

related Kubernetes posts

Conor Myhrvold
Tech Brand Mgr, Office of CTO at Uber · | 41 upvotes · 5.4M 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

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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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Docker logo

Docker

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Enterprise Container Platform for High-Velocity Innovation.
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PROS OF DOCKER
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    Rapid integration and build up
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    Isolation
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    Open source
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    Testa­bil­i­ty and re­pro­ducibil­i­ty
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    Lightweight
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    Standardization
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    Scalable
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    Upgrading / down­grad­ing / ap­pli­ca­tion versions
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    Security
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    Private paas environments
  • 33
    Portability
  • 25
    Limit resource usage
  • 16
    Game changer
  • 15
    I love the way docker has changed virtualization
  • 13
    Fast
  • 11
    Concurrency
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    Docker's Compose tools
  • 5
    Fast and Portable
  • 5
    Easy setup
  • 4
    Because its fun
  • 3
    Makes shipping to production very simple
  • 2
    It's dope
  • 2
    Highly useful
  • 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
  • 1
    MacOS support FAKE
  • 1
    Its cool
  • 1
    Docker hub for the FTW
  • 1
    HIgh Throughput
CONS OF DOCKER
  • 8
    New versions == broken features
  • 6
    Unreliable networking
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    Documentation not always in sync
  • 4
    Moves quickly
  • 3
    Not Secure

related Docker posts

Simon Reymann
Senior Fullstack Developer at QUANTUSflow Software GmbH · | 29 upvotes · 4.8M 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.
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Tymoteusz Paul
Devops guy at X20X Development LTD · | 23 upvotes · 5.4M 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.

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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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      related Azure Kubernetes Service posts

      Farzad Jalali
      Senior Software Architect at BerryWorld · | 8 upvotes · 215.9K 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
      Azure App Service logo

      Azure App Service

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      Build, deploy, and scale web apps on a fully managed platform
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      PROS OF AZURE APP SERVICE
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        Visual studio
      • 4
        .Net Framework
      CONS OF AZURE APP SERVICE
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        related Azure App Service posts

        Mehdi Baaboura
        Managing Director at Gigadrive · | 2 upvotes · 5.2K views

        Easier setup and integration for PHP based applications. Azure App Service requires a lot of extra configuration, while AWS Elastic Beanstalk has most things set-up out of the box. On top of this, Azure is much more expensive.

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        Azure Container Instances logo

        Azure Container Instances

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        PROS OF AZURE CONTAINER INSTANCES
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            related Azure Container Instances posts

            Amazon EC2 Container Service logo

            Amazon EC2 Container Service

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            PROS OF AMAZON EC2 CONTAINER SERVICE
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              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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              Scheduler
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              Cluster management
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              Programmatic Control
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              Socker support
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              Container-enabled applications
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              No additional cost
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              Easy to use and cheap
            CONS OF AMAZON EC2 CONTAINER SERVICE
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              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) )

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              Russel Werner
              Lead Engineer at StackShare · | 7 upvotes · 230.7K views

              We began our hosting journey, as many do, on Heroku because they make it easy to deploy your application and automate some of the routine tasks associated with deployments, etc. However, as our team grew and our product matured, our needs have outgrown Heroku. I will dive into the history and reasons for this in a future blog post.

              We decided to migrate our infrastructure to Kubernetes running on Amazon EKS. Although Google Kubernetes Engine has a slightly more mature Kubernetes offering and is more user-friendly; we decided to go with EKS because we already using other AWS services (including a previous migration from Heroku Postgres to AWS RDS). We are still in the process of moving our main website workloads to EKS, however we have successfully migrate all our staging and testing PR apps to run in a staging cluster. We developed a Slack chatops application (also running in the cluster) which automates all the common tasks of spinning up and managing a production-like cluster for a pull request. This allows our engineering team to iterate quickly and safely test code in a full production environment. Helm plays a central role when deploying our staging apps into the cluster. We use CircleCI to build docker containers for each PR push, which are then published to Amazon EC2 Container Service (ECR). An upgrade-operator process watches the ECR repository for new containers and then uses Helm to rollout updates to the staging environments. All this happens automatically and makes it really easy for developers to get code onto servers quickly. The immutable and isolated nature of our staging environments means that we can do anything we want in that environment and quickly re-create or restore the environment to start over.

              The next step in our journey is to migrate our production workloads to an EKS cluster and build out the CD workflows to get our containers promoted to that cluster after our QA testing is complete in our staging environments.

              See more
              Google Kubernetes Engine logo

              Google Kubernetes Engine

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              PROS OF GOOGLE KUBERNETES ENGINE
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                Backed by Google
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                Powered by kubernetes
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                Docker
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                Scalable
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                Command line interface is intuitive
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                Provisioning
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                Declarative management
              CONS OF GOOGLE KUBERNETES ENGINE
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                related Google Kubernetes Engine posts

                Emanuel Evans
                Senior Architect at Rainforest QA · | 19 upvotes · 1.1M 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.

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                Omar Mehilba
                Co-Founder and COO at Magalix · | 19 upvotes · 277.9K 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!

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                Amazon EKS logo

                Amazon EKS

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                PROS OF AMAZON EKS
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                  Better control
                • 1
                  Possibility to log in into the pods
                • 1
                  Broad package manager using helm
                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 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.

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