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?

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

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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231
25
Distributed systems platform that simplifies build, package, deploy, and management of scalable microservices apps
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231
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PROS OF AZURE SERVICE FABRIC
  • 5
    Intelligent, fast, reliable
  • 3
    Open source
  • 3
    Superior programming models
  • 3
    More reliable than Kubernetes
  • 3
    Runs most of Azure core services
  • 3
    Reliability
  • 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
    Be the first to leave a con

    related Azure Service Fabric posts

    Kubernetes logo

    Kubernetes

    38.9K
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    628
    Manage a cluster of Linux containers as a single system to accelerate Dev and simplify Ops
    38.9K
    33.2K
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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
    Azure Container Service logo

    Azure Container Service

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    170
    10
    Deploy and manage containers using the tools you choose
    81
    170
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    10
    PROS OF AZURE CONTAINER SERVICE
    • 5
      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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      268
      7
      Build, deploy, and scale web apps on a fully managed platform
      225
      268
      + 1
      7
      PROS OF AZURE APP SERVICE
      • 4
        .Net Framework
      • 3
        Visual studio
      CONS OF AZURE APP SERVICE
        Be the first to leave a con

        related Azure App Service posts

        Azure Container Instances logo

        Azure Container Instances

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        53
        0
        Easily run containers on Azure without managing servers
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        53
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        PROS OF AZURE CONTAINER INSTANCES
          Be the first to leave a pro
          CONS OF AZURE CONTAINER INSTANCES
            Be the first to leave a con

            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
            • 12
              Pay only when invoked
            • 8
              Great developer experience for C#
            • 6
              Multiple languages supported
            • 5
              Great debugging support
            • 2
              Poor developer experience for C#
            • 2
              Easy scalability
            • 2
              Can be used as lightweight https service
            • 1
              WebHooks
            • 1
              Event driven
            • 1
              Azure component events for Storage, services etc
            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

            related Azure Functions posts

            Kestas Barzdaitis
            Entrepreneur & Engineer · | 16 upvotes · 453.5K 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
            Michal Nowak

            In a couple of recent projects we had an opportunity to try out the new Serverless approach to building web applications. It wasn't necessarily a question if we should use any particular vendor but rather "if" we can consider serverless a viable option for building apps. Obviously our goal was also to get a feel for this technology and gain some hands-on experience.

            We did consider AWS Lambda, Firebase from Google as well as Azure Functions. Eventually we went with AWS Lambdas.

            PROS
            • No servers to manage (obviously!)
            • Limited fixed costs – you pay only for used time
            • Automated scaling and balancing
            • Automatic failover (or, at this level of abstraction, no failover problem at all)
            • Security easier to provide and audit
            • Low overhead at the start (with the certain level of knowledge)
            • Short time to market
            • Easy handover - deployment coupled with code
            • Perfect choice for lean startups with fast-paced iterations
            • Augmentation for the classic cloud, server(full) approach
            CONS
            • Not much know-how and best practices available about structuring the code and projects on the market
            • Not suitable for complex business logic due to the risk of producing highly coupled code
            • Cost difficult to estimate (helpful tools: serverlesscalc.com)
            • Difficulty in migration to other platforms (Vendor lock⚠️)
            • Little engineers with experience in serverless on the job market
            • Steep learning curve for engineers without any cloud experience

            More details are on our blog: https://evojam.com/blog/2018/12/5/should-you-go-serverless-meet-the-benefits-and-flaws-of-new-wave-of-cloud-solutions I hope it helps 🙌 & I'm curious of your experiences.

            See more
            Docker logo

            Docker

            112.5K
            92K
            3.8K
            Enterprise Container Platform for High-Velocity Innovation.
            112.5K
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            + 1
            3.8K
            PROS OF DOCKER
            • 821
              Rapid integration and build up
            • 688
              Isolation
            • 517
              Open source
            • 505
              Testa­bil­i­ty and re­pro­ducibil­i­ty
            • 459
              Lightweight
            • 217
              Standardization
            • 182
              Scalable
            • 105
              Upgrading / down­grad­ing / ap­pli­ca­tion versions
            • 86
              Security
            • 84
              Private paas environments
            • 33
              Portability
            • 25
              Limit resource usage
            • 15
              I love the way docker has changed virtualization
            • 15
              Game changer
            • 12
              Fast
            • 11
              Concurrency
            • 7
              Docker's Compose tools
            • 4
              Fast and Portable
            • 4
              Easy setup
            • 4
              Because its fun
            • 3
              Makes shipping to production very simple
            • 2
              It's dope
            • 1
              Highly useful
            • 1
              MacOS support FAKE
            • 1
              Its cool
            • 1
              Docker hub for the FTW
            • 1
              Very easy to setup integrate and build
            • 1
              Package the environment with the application
            • 1
              Does a nice job hogging memory
            • 1
              Open source and highly configurable
            • 1
              Simplicity, isolation, resource effective
            CONS OF DOCKER
            • 7
              New versions == broken features
            • 5
              Documentation not always in sync
            • 5
              Unreliable networking
            • 3
              Moves quickly
            • 2
              Not Secure

            related Docker posts

            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
            Tymoteusz Paul
            Devops guy at X20X Development LTD · | 23 upvotes · 4.6M views

            Often enough I have to explain my way of going about setting up a CI/CD pipeline with multiple deployment platforms. Since I am a bit tired of yapping the same every single time, I've decided to write it up and share with the world this way, and send people to read it instead ;). I will explain it on "live-example" of how the Rome got built, basing that current methodology exists only of readme.md and wishes of good luck (as it usually is ;)).

            It always starts with an app, whatever it may be and reading the readmes available while Vagrant and VirtualBox is installing and updating. Following that is the first hurdle to go over - convert all the instruction/scripts into Ansible playbook(s), and only stopping when doing a clear vagrant up or vagrant reload we will have a fully working environment. As our Vagrant environment is now functional, it's time to break it! This is the moment to look for how things can be done better (too rigid/too lose versioning? Sloppy environment setup?) and replace them with the right way to do stuff, one that won't bite us in the backside. This is the point, and the best opportunity, to upcycle the existing way of doing dev environment to produce a proper, production-grade product.

            I should probably digress here for a moment and explain why. I firmly believe that the way you deploy production is the same way you should deploy develop, shy of few debugging-friendly setting. This way you avoid the discrepancy between how production work vs how development works, which almost always causes major pains in the back of the neck, and with use of proper tools should mean no more work for the developers. That's why we start with Vagrant as developer boxes should be as easy as vagrant up, but the meat of our product lies in Ansible which will do meat of the work and can be applied to almost anything: AWS, bare metal, docker, LXC, in open net, behind vpn - you name it.

            We must also give proper consideration to monitoring and logging hoovering at this point. My generic answer here is to grab Elasticsearch, Kibana, and Logstash. While for different use cases there may be better solutions, this one is well battle-tested, performs reasonably and is very easy to scale both vertically (within some limits) and horizontally. Logstash rules are easy to write and are well supported in maintenance through Ansible, which as I've mentioned earlier, are at the very core of things, and creating triggers/reports and alerts based on Elastic and Kibana is generally a breeze, including some quite complex aggregations.

            If we are happy with the state of the Ansible it's time to move on and put all those roles and playbooks to work. Namely, we need something to manage our CI/CD pipelines. For me, the choice is obvious: TeamCity. It's modern, robust and unlike most of the light-weight alternatives, it's transparent. What I mean by that is that it doesn't tell you how to do things, doesn't limit your ways to deploy, or test, or package for that matter. Instead, it provides a developer-friendly and rich playground for your pipelines. You can do most the same with Jenkins, but it has a quite dated look and feel to it, while also missing some key functionality that must be brought in via plugins (like quality REST API which comes built-in with TeamCity). It also comes with all the common-handy plugins like Slack or Apache Maven integration.

            The exact flow between CI and CD varies too greatly from one application to another to describe, so I will outline a few rules that guide me in it: 1. Make build steps as small as possible. This way when something breaks, we know exactly where, without needing to dig and root around. 2. All security credentials besides development environment must be sources from individual Vault instances. Keys to those containers should exist only on the CI/CD box and accessible by a few people (the less the better). This is pretty self-explanatory, as anything besides dev may contain sensitive data and, at times, be public-facing. Because of that appropriate security must be present. TeamCity shines in this department with excellent secrets-management. 3. Every part of the build chain shall consume and produce artifacts. If it creates nothing, it likely shouldn't be its own build. This way if any issue shows up with any environment or version, all developer has to do it is grab appropriate artifacts to reproduce the issue locally. 4. Deployment builds should be directly tied to specific Git branches/tags. This enables much easier tracking of what caused an issue, including automated identifying and tagging the author (nothing like automated regression testing!).

            Speaking of deployments, I generally try to keep it simple but also with a close eye on the wallet. Because of that, I am more than happy with AWS or another cloud provider, but also constantly peeking at the loads and do we get the value of what we are paying for. Often enough the pattern of use is not constantly erratic, but rather has a firm baseline which could be migrated away from the cloud and into bare metal boxes. That is another part where this approach strongly triumphs over the common Docker and CircleCI setup, where you are very much tied in to use cloud providers and getting out is expensive. Here to embrace bare-metal hosting all you need is a help of some container-based self-hosting software, my personal preference is with Proxmox and LXC. Following that all you must write are ansible scripts to manage hardware of Proxmox, similar way as you do for Amazon EC2 (ansible supports both greatly) and you are good to go. One does not exclude another, quite the opposite, as they can live in great synergy and cut your costs dramatically (the heavier your base load, the bigger the savings) while providing production-grade resiliency.

            See more
            Docker Swarm logo

            Docker Swarm

            705
            841
            267
            Native clustering for Docker. Turn a pool of Docker hosts into a single, virtual host.
            705
            841
            + 1
            267
            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