Alternatives to Azure Kubernetes Service logo

Alternatives to Azure Kubernetes Service

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

Azure Kubernetes Service (AKS) is a fully managed container orchestration service by Microsoft that simplifies deploying, managing, and scaling containerized applications using Kubernetes. Key features include native integration with Azure monitoring and networking services, automated updates and patching, and seamless integration with Azure Active Directory for role-based access control. However, some limitations include higher costs compared to self-managed Kubernetes clusters and potential lack of flexibility in certain configurations.

  1. Amazon Elastic Kubernetes Service (EKS): Amazon EKS is a managed Kubernetes service on AWS that offers scalability, security, and reliability. Key features include deep integration with AWS services, flexible deployment options, and efficient resource management. Pros include seamless integration with other AWS services, while cons may include higher costs for organizations not already heavily invested in AWS.
  2. Google Kubernetes Engine (GKE): Google GKE is a managed Kubernetes service on Google Cloud Platform that provides features like automatic container scaling, version upgrades, and powerful monitoring tools. Pros include deep integration with Google Cloud services and strong community support. Cons may include potential learning curve for beginners.
  3. Docker Enterprise: Docker Enterprise is a container platform that offers secure, scalable, and flexible solutions for deploying applications. Key features include built-in security, centralized management, and compatibility with any infrastructure. Pros include seamless integration with Docker tools, while cons may include potentially higher costs compared to other solutions.
  4. Rancher: Rancher is an open-source container management platform that simplifies the deployment and management of Kubernetes clusters. Key features include multi-cluster management, built-in monitoring, and support for multiple cloud providers. Pros include open-source community support, while cons may include potential complexity for beginners.
  5. Red Hat OpenShift: Red Hat OpenShift is a container application platform that offers automated operations, multi-cluster management, and enterprise-grade security. Key features include developer-friendly tools, built-in CI/CD pipelines, and support for hybrid cloud environments. Pros include strong enterprise support from Red Hat, while cons may include potential licensing costs for certain features.
  6. Container Service by IBM Cloud: IBM Cloud Container Service is a managed Kubernetes service that provides high availability, auto-scaling, and integrated monitoring tools. Key features include industry-leading security measures, easy integration with IBM Cloud services, and advanced networking capabilities. Pros include strong security features, while cons may include potential complexity for beginners.
  7. DigitalOcean Kubernetes: DigitalOcean Kubernetes is a managed Kubernetes service that offers simplicity, flexibility, and cost-effectiveness. Key features include automatic upgrades, horizontal scaling, and integrations with other DigitalOcean services. Pros include affordable pricing, while cons may include less feature-rich compared to other providers.
  8. VMware Tanzu: VMware Tanzu is a Kubernetes platform that simplifies the deployment, management, and monitoring of containerized applications. Key features include enterprise-grade security, seamless integration with VMware infrastructure, and support for VMware Tanzu Kubernetes Grid. Pros include strong integration with existing VMware solutions, while cons may include potential costs for additional VMware products.
  9. Pivotal Platform: Pivotal Platform (formerly Pivotal Cloud Foundry) is a cloud-native platform that offers automated software deployment, scaling, and operations. Key features include developer-friendly tools, built-in monitoring, and support for multi-cloud deployments. Pros include simplicity of deployment, while cons may include potential learning curve for complex configurations.
  10. KubeSphere: KubeSphere is an open-source Kubernetes platform that provides a multi-tenant, distributed, and easy-to-use enterprise-grade container orchestration platform. Key features include DevOps automation, application management, and multi-cluster management capabilities. Pros include open-source community support, while cons may include potentially limited enterprise-grade features compared to other solutions.

Top Alternatives to Azure Kubernetes Service

  • Azure Service Fabric
    Azure Service Fabric

    Azure Service Fabric is a distributed systems platform that makes it easy to package, deploy, and manage scalable and reliable microservices. Service Fabric addresses the significant challenges in developing and managing cloud apps. ...

  • 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. ...

  • 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. ...

  • 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. ...

  • Azure Functions
    Azure Functions

    Azure Functions is an event driven, compute-on-demand experience that extends the existing Azure application platform with capabilities to implement code triggered by events occurring in virtually any Azure or 3rd party service as well as on-premises systems. ...

  • 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 ...

  • 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. ...

Azure Kubernetes Service alternatives & related posts

Azure Service Fabric logo

Azure Service Fabric

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Distributed systems platform that simplifies build, package, deploy, and management of scalable microservices apps
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PROS OF AZURE SERVICE FABRIC
  • 5
    Intelligent, fast, reliable
  • 4
    Runs most of Azure core services
  • 3
    Reliability
  • 3
    Superior programming models
  • 3
    More reliable than Kubernetes
  • 3
    Open source
  • 2
    Quickest recovery and healing in the world
  • 1
    Deploy anywhere
  • 1
    Is data storage technology
  • 1
    Battle hardened in Azure > 10 Years
CONS OF AZURE SERVICE FABRIC
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    related Azure Service Fabric 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
    • 164
      Leading docker container management solution
    • 128
      Simple and powerful
    • 106
      Open source
    • 76
      Backed by google
    • 58
      The right abstractions
    • 25
      Scale services
    • 20
      Replication controller
    • 11
      Permission managment
    • 9
      Supports autoscaling
    • 8
      Cheap
    • 8
      Simple
    • 6
      Self-healing
    • 5
      No cloud platform lock-in
    • 5
      Promotes modern/good infrascture practice
    • 5
      Open, powerful, stable
    • 5
      Reliable
    • 4
      Scalable
    • 4
      Quick cloud setup
    • 3
      Cloud Agnostic
    • 3
      Captain of Container Ship
    • 3
      A self healing environment with rich metadata
    • 3
      Runs on azure
    • 3
      Backed by Red Hat
    • 3
      Custom and extensibility
    • 2
      Sfg
    • 2
      Gke
    • 2
      Everything of CaaS
    • 2
      Golang
    • 2
      Easy setup
    • 2
      Expandable
    CONS OF KUBERNETES
    • 16
      Steep learning curve
    • 15
      Poor workflow for development
    • 8
      Orchestrates only infrastructure
    • 4
      High resource requirements for on-prem clusters
    • 2
      Too heavy for simple systems
    • 1
      Additional vendor lock-in (Docker)
    • 1
      More moving parts to secure
    • 1
      Additional Technology Overhead

    related Kubernetes posts

    Conor Myhrvold
    Tech Brand Mgr, Office of CTO at Uber · | 44 upvotes · 11.2M 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
    Ashish Singh
    Tech Lead, Big Data Platform at Pinterest · | 38 upvotes · 3M views

    To provide employees with the critical need of interactive querying, we’ve worked with Presto, an open-source distributed SQL query engine, over the years. Operating Presto at Pinterest’s scale has involved resolving quite a few challenges like, supporting deeply nested and huge thrift schemas, slow/ bad worker detection and remediation, auto-scaling cluster, graceful cluster shutdown and impersonation support for ldap authenticator.

    Our infrastructure is built on top of Amazon EC2 and we leverage Amazon S3 for storing our data. This separates compute and storage layers, and allows multiple compute clusters to share the S3 data.

    We have hundreds of petabytes of data and tens of thousands of Apache Hive tables. Our Presto clusters are comprised of a fleet of 450 r4.8xl EC2 instances. Presto clusters together have over 100 TBs of memory and 14K vcpu cores. Within Pinterest, we have close to more than 1,000 monthly active users (out of total 1,600+ Pinterest employees) using Presto, who run about 400K queries on these clusters per month.

    Each query submitted to Presto cluster is logged to a Kafka topic via Singer. Singer is a logging agent built at Pinterest and we talked about it in a previous post. Each query is logged when it is submitted and when it finishes. When a Presto cluster crashes, we will have query submitted events without corresponding query finished events. These events enable us to capture the effect of cluster crashes over time.

    Each Presto cluster at Pinterest has workers on a mix of dedicated AWS EC2 instances and Kubernetes pods. Kubernetes platform provides us with the capability to add and remove workers from a Presto cluster very quickly. The best-case latency on bringing up a new worker on Kubernetes is less than a minute. However, when the Kubernetes cluster itself is out of resources and needs to scale up, it can take up to ten minutes. Some other advantages of deploying on Kubernetes platform is that our Presto deployment becomes agnostic of cloud vendor, instance types, OS, etc.

    #BigData #AWS #DataScience #DataEngineering

    See more
    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
    • 6
      Easy to setup, very agnostic
    • 3
      It supports Kubernetes, Mesos DC/OS and Docker Swarm
    • 2
      It has a nice command line interface (CLI) tool
    CONS OF AZURE CONTAINER SERVICE
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      related Azure Container Service posts

      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
      • 6
        .Net Framework
      • 5
        Visual studio
      CONS OF AZURE APP SERVICE
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        related Azure App Service posts

        Mehdi Baaboura
        Managing Director at Gigadrive · | 2 upvotes · 20.7K 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.

        See more
        Azure Container Instances logo

        Azure Container Instances

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        Easily run containers on Azure without managing servers
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        PROS OF AZURE CONTAINER INSTANCES
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          CONS OF AZURE CONTAINER INSTANCES
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            related Azure Container Instances posts

            Azure Functions logo

            Azure Functions

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            Listen and react to events across your stack
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            PROS OF AZURE FUNCTIONS
            • 14
              Pay only when invoked
            • 11
              Great developer experience for C#
            • 9
              Multiple languages supported
            • 7
              Great debugging support
            • 5
              Can be used as lightweight https service
            • 4
              Easy scalability
            • 3
              WebHooks
            • 3
              Costo
            • 2
              Event driven
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              Azure component events for Storage, services etc
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              Poor developer experience for C#
            CONS OF AZURE FUNCTIONS
            • 1
              No persistent (writable) file system available
            • 1
              Poor support for Linux environments
            • 1
              Sporadic server & language runtime issues
            • 1
              Not suited for long-running applications

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            Kestas Barzdaitis
            Entrepreneur & Engineer · | 16 upvotes · 766.6K views

            CodeFactor being a #SAAS product, our goal was to run on a cloud-native infrastructure since day one. We wanted to stay product focused, rather than having to work on the infrastructure that supports the application. We needed a cloud-hosting provider that would be reliable, economical and most efficient for our product.

            CodeFactor.io aims to provide an automated and frictionless code review service for software developers. That requires agility, instant provisioning, autoscaling, security, availability and compliance management features. We looked at the top three #IAAS providers that take up the majority of market share: Amazon's Amazon EC2 , Microsoft's Microsoft Azure, and Google Compute Engine.

            AWS has been available since 2006 and has developed the most extensive services ant tools variety at a massive scale. Azure and GCP are about half the AWS age, but also satisfied our technical requirements.

            It is worth noting that even though all three providers support Docker containerization services, GCP has the most robust offering due to their investments in Kubernetes. Also, if you are a Microsoft shop, and develop in .NET - Visual Studio Azure shines at integration there and all your existing .NET code works seamlessly on Azure. All three providers have serverless computing offerings (AWS Lambda, Azure Functions, and Google Cloud Functions). Additionally, all three providers have machine learning tools, but GCP appears to be the most developer-friendly, intuitive and complete when it comes to #Machinelearning and #AI.

            The prices between providers are competitive across the board. For our requirements, AWS would have been the most expensive, GCP the least expensive and Azure was in the middle. Plus, if you #Autoscale frequently with large deltas, note that Azure and GCP have per minute billing, where AWS bills you per hour. We also applied for the #Startup programs with all three providers, and this is where Azure shined. While AWS and GCP for startups would have covered us for about one year of infrastructure costs, Azure Sponsorship would cover about two years of CodeFactor's hosting costs. Moreover, Azure Team was terrific - I felt that they wanted to work with us where for AWS and GCP we were just another startup.

            In summary, we were leaning towards GCP. GCP's advantages in containerization, automation toolset, #Devops mindset, and pricing were the driving factors there. Nevertheless, we could not say no to Azure's financial incentives and a strong sense of partnership and support throughout the process.

            Bottom line is, IAAS offerings with AWS, Azure, and GCP are evolving fast. At CodeFactor, we aim to be platform agnostic where it is practical and retain the flexibility to cherry-pick the best products across providers.

            See more

            REST API for SaaS application

            I'm currently developing an Azure Functions REST API with TypeScript, tsoa, Mongoose, and Typegoose that contains simple CRUD activities. It does the job and has type-safety as well as the ability to generate OpenAPI specs for me.

            However, as the app scales up, there are more duplicated codes (for similar operations - like CRUD in each different model). It's also becoming more complex because I need to implement a multi-tenancy SaaS for both the API and the database.

            So I chose to implement a repository pattern, and I have a "feeling" that .NET and C# will make development easier because, unlike TypeScript, it includes native support for Dependency Injection and great things like LINQ.

            It wouldn't take much effort to migrate because I can easily translate interfaces and basic CRUD operations to C#. So, I'm looking for advice on whether it's worth converting from TypeScript to.NET.

            See more
            Docker logo

            Docker

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            Enterprise Container Platform for High-Velocity Innovation.
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            PROS OF DOCKER
            • 823
              Rapid integration and build up
            • 691
              Isolation
            • 521
              Open source
            • 505
              Testa­bil­i­ty and re­pro­ducibil­i­ty
            • 460
              Lightweight
            • 218
              Standardization
            • 185
              Scalable
            • 106
              Upgrading / down­grad­ing / ap­pli­ca­tion versions
            • 88
              Security
            • 85
              Private paas environments
            • 34
              Portability
            • 26
              Limit resource usage
            • 17
              Game changer
            • 16
              I love the way docker has changed virtualization
            • 14
              Fast
            • 12
              Concurrency
            • 8
              Docker's Compose tools
            • 6
              Easy setup
            • 6
              Fast and Portable
            • 5
              Because its fun
            • 4
              Makes shipping to production very simple
            • 3
              Highly useful
            • 3
              It's dope
            • 2
              Very easy to setup integrate and build
            • 2
              HIgh Throughput
            • 2
              Package the environment with the application
            • 2
              Does a nice job hogging memory
            • 2
              Open source and highly configurable
            • 2
              Simplicity, isolation, resource effective
            • 2
              MacOS support FAKE
            • 2
              Its cool
            • 2
              Docker hub for the FTW
            • 2
              Super
            • 0
              Asdfd
            CONS OF DOCKER
            • 8
              New versions == broken features
            • 6
              Unreliable networking
            • 6
              Documentation not always in sync
            • 4
              Moves quickly
            • 3
              Not Secure

            related Docker posts

            Simon Reymann
            Senior Fullstack Developer at QUANTUSflow Software GmbH · | 30 upvotes · 9.9M views

            Our whole DevOps stack consists of the following tools:

            • GitHub (incl. GitHub Pages/Markdown for Documentation, GettingStarted and HowTo's) for collaborative review and code management tool
            • Respectively Git as revision control system
            • SourceTree as Git GUI
            • Visual Studio Code as IDE
            • CircleCI for continuous integration (automatize development process)
            • Prettier / TSLint / ESLint as code linter
            • SonarQube as quality gate
            • Docker as container management (incl. Docker Compose for multi-container application management)
            • VirtualBox for operating system simulation tests
            • Kubernetes as cluster management for docker containers
            • Heroku for deploying in test environments
            • nginx as web server (preferably used as facade server in production environment)
            • SSLMate (using OpenSSL) for certificate management
            • Amazon EC2 (incl. Amazon S3) for deploying in stage (production-like) and production environments
            • PostgreSQL as preferred database system
            • Redis as preferred in-memory database/store (great for caching)

            The main reason we have chosen Kubernetes over Docker Swarm is related to the following artifacts:

            • Key features: Easy and flexible installation, Clear dashboard, Great scaling operations, Monitoring is an integral part, Great load balancing concepts, Monitors the condition and ensures compensation in the event of failure.
            • Applications: An application can be deployed using a combination of pods, deployments, and services (or micro-services).
            • Functionality: Kubernetes as a complex installation and setup process, but it not as limited as Docker Swarm.
            • Monitoring: It supports multiple versions of logging and monitoring when the services are deployed within the cluster (Elasticsearch/Kibana (ELK), Heapster/Grafana, Sysdig cloud integration).
            • Scalability: All-in-one framework for distributed systems.
            • Other Benefits: Kubernetes is backed by the Cloud Native Computing Foundation (CNCF), huge community among container orchestration tools, it is an open source and modular tool that works with any OS.
            See more
            Tymoteusz Paul
            Devops guy at X20X Development LTD · | 23 upvotes · 8.9M 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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            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
            • 55
              Docker friendly
            • 46
              Easy to setup
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              Standard Docker API
            • 38
              Easy to use
            • 23
              Native
            • 22
              Free
            • 13
              Clustering made easy
            • 12
              Simple usage
            • 11
              Integral part of docker
            • 6
              Cross Platform
            • 5
              Labels and annotations
            • 5
              Performance
            • 3
              Easy Networking
            • 3
              Shallow learning curve
            CONS OF DOCKER SWARM
            • 9
              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 · | 30 upvotes · 9.9M 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