Alternatives to AWS Fargate logo

Alternatives to AWS Fargate

Google App Engine, Kubernetes, AWS Batch, AWS Lambda, and Batch are the most popular alternatives and competitors to AWS Fargate.
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What is AWS Fargate and what are its top alternatives?

AWS Fargate is a technology for Amazon ECS and EKS* that allows you to run containers without having to manage servers or clusters. With AWS Fargate, you no longer have to provision, configure, and scale clusters of virtual machines to run containers.
AWS Fargate is a tool in the Containers as a Service category of a tech stack.

Top Alternatives to AWS Fargate

  • Google App Engine

    Google App Engine

    Google has a reputation for highly reliable, high performance infrastructure. With App Engine you can take advantage of the 10 years of knowledge Google has in running massively scalable, performance driven systems. App Engine applications are easy to build, easy to maintain, and easy to scale as your traffic and data storage needs grow. ...

  • 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. ...

  • AWS Batch

    AWS Batch

    It enables developers, scientists, and engineers to easily and efficiently run hundreds of thousands of batch computing jobs on AWS. It dynamically provisions the optimal quantity and type of compute resources (e.g., CPU or memory optimized instances) based on the volume and specific resource requirements of the batch jobs submitted. ...

  • AWS Lambda

    AWS Lambda

    AWS Lambda is a compute service that runs your code in response to events and automatically manages the underlying compute resources for you. You can use AWS Lambda to extend other AWS services with custom logic, or create your own back-end services that operate at AWS scale, performance, and security. ...

  • Batch

    Batch

    Yes, we’re really free. So, how do we keep the lights on? Instead of charging you a monthly fee, we sell ads on your behalf to the top 500 mobile advertisers in the world. With Batch, you earn money each month while accessing great engagement tools for free. ...

  • Google Cloud Run

    Google Cloud Run

    A managed compute platform that enables you to run stateless containers that are invocable via HTTP requests. It's serverless by abstracting away all infrastructure management. ...

  • Beanstalk

    Beanstalk

    A single process to commit code, review with the team, and deploy the final result to your customers. ...

  • 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 ...

AWS Fargate alternatives & related posts

Google App Engine logo

Google App Engine

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Build web applications on the same scalable systems that power Google applications
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PROS OF GOOGLE APP ENGINE
  • 144
    Easy to deploy
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    Auto scaling
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    Good free plan
  • 64
    Easy management
  • 58
    Scalability
  • 35
    Low cost
  • 33
    Comprehensive set of features
  • 29
    All services in one place
  • 23
    Simple scaling
  • 20
    Quick and reliable cloud servers
  • 5
    Granular Billing
  • 4
    Easy to develop and unit test
  • 3
    Monitoring gives comprehensive set of key indicators
  • 2
    Create APIs quickly with cloud endpoints
  • 2
    Really easy to quickly bring up a full stack
  • 1
    Mostly up
  • 1
    No Ops
CONS OF GOOGLE APP ENGINE
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    related Google App Engine posts

    Nick Rockwell
    SVP, Engineering at Fastly · | 11 upvotes · 284.1K views

    So, the shift from Amazon EC2 to Google App Engine and generally #AWS to #GCP was a long decision and in the end, it's one that we've taken with eyes open and that we reserve the right to modify at any time. And to be clear, we continue to do a lot of stuff with AWS. But, by default, the content of the decision was, for our consumer-facing products, we're going to use GCP first. And if there's some reason why we don't think that's going to work out great, then we'll happily use AWS. In practice, that hasn't really happened. We've been able to meet almost 100% of our needs in GCP.

    So it's basically mostly Google Kubernetes Engine , we're mostly running stuff on Kubernetes right now.

    #AWStoGCPmigration #cloudmigration #migration

    See more
    Aliadoc Team

    In #Aliadoc, we're exploring the crowdfunding option to get traction before launch. We are building a SaaS platform for website design customization.

    For the Admin UI and website editor we use React and we're currently transitioning from a Create React App setup to a custom one because our needs have become more specific. We use CloudFlare as much as possible, it's a great service.

    For routing dynamic resources and proxy tasks to feed websites to the editor we leverage CloudFlare Workers for improved responsiveness. We use Firebase for our hosting needs and user authentication while also using several Cloud Functions for Firebase to interact with other services along with Google App Engine and Google Cloud Storage, but also the Real Time Database is on the radar for collaborative website editing.

    We generally hate configuration but honestly because of the stage of our project we lack resources for doing heavy sysops work. So we are basically just relying on Serverless technologies as much as we can to do all server side processing.

    Visual Studio Code definitively makes programming a much easier and enjoyable task, we just love it. We combine it with Bitbucket for our source code control needs.

    See more
    Kubernetes logo

    Kubernetes

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    Manage a cluster of Linux containers as a single system to accelerate Dev and simplify Ops
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    PROS OF KUBERNETES
    • 159
      Leading docker container management solution
    • 124
      Simple and powerful
    • 101
      Open source
    • 75
      Backed by google
    • 56
      The right abstractions
    • 24
      Scale services
    • 18
      Replication controller
    • 9
      Permission managment
    • 7
      Simple
    • 7
      Supports autoscaling
    • 6
      Cheap
    • 4
      Self-healing
    • 4
      Reliable
    • 4
      No cloud platform lock-in
    • 3
      Open, powerful, stable
    • 3
      Scalable
    • 3
      Quick cloud setup
    • 3
      Promotes modern/good infrascture practice
    • 2
      Backed by Red Hat
    • 2
      Runs on azure
    • 2
      Cloud Agnostic
    • 2
      Custom and extensibility
    • 2
      Captain of Container Ship
    • 2
      A self healing environment with rich metadata
    • 1
      Golang
    • 1
      Easy setup
    • 1
      Everything of CaaS
    • 1
      Sfg
    • 1
      Expandable
    • 1
      Gke
    CONS OF KUBERNETES
    • 13
      Poor workflow for development
    • 11
      Steep learning curve
    • 5
      Orchestrates only infrastructure
    • 2
      High resource requirements for on-prem clusters

    related Kubernetes posts

    Conor Myhrvold
    Tech Brand Mgr, Office of CTO at Uber · | 39 upvotes · 4.2M views

    How Uber developed the open source, end-to-end distributed tracing Jaeger , now a CNCF project:

    Distributed tracing is quickly becoming a must-have component in the tools that organizations use to monitor their complex, microservice-based architectures. At Uber, our open source distributed tracing system Jaeger saw large-scale internal adoption throughout 2016, integrated into hundreds of microservices and now recording thousands of traces every second.

    Here is the story of how we got here, from investigating off-the-shelf solutions like Zipkin, to why we switched from pull to push architecture, and how distributed tracing will continue to evolve:

    https://eng.uber.com/distributed-tracing/

    (GitHub Pages : https://www.jaegertracing.io/, GitHub: https://github.com/jaegertracing/jaeger)

    Bindings/Operator: Python Java Node.js Go C++ Kubernetes JavaScript OpenShift C# Apache Spark

    See more
    Yshay Yaacobi

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

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

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

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    AWS Batch logo

    AWS Batch

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    Fully Managed Batch Processing at Any Scale
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    PROS OF AWS BATCH
    • 2
      Scalable
    • 2
      Containerized
    CONS OF AWS BATCH
    • 1
      More overhead than lambda
    • 1
      Image management

    related AWS Batch posts

    Sumit Singh Chauhan
    Data Scientist at Entropik · | 6 upvotes · 4.8K views

    I have started using AWS Batch for some long ML inference jobs. So far it's working well and giving a decent performance. Since it is fully managed, it saves a lot of extra work as well. But Batch takes a good amount of time to create a new cluster and then load the job based on the priority of the queue. Going forward would love to put effort into something which is fast to start and give more flexibility as well. What other tools you would suggest for long-running backend jobs which can scale well. I am not looking for something fully managed so ignore the options similar to batch in Google Cloud Platform or Microsoft Azure, Looking for open-source alternatives here. Do you think Kubernetes, RabbitMQ/Kafka will be a good fit or just overkill for my problem. Usually w we get 1000s of requests in parallel and each job might take 20-30 mins in a 2 vCPU system.

    See more
    AWS Lambda logo

    AWS Lambda

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    PROS OF AWS LAMBDA
    • 127
      No infrastructure
    • 82
      Cheap
    • 69
      Quick
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      Stateless
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      No deploy, no server, great sleep
    • 9
      AWS Lambda went down taking many sites with it
    • 6
      Event Driven Governance
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      Auto scale and cost effective
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      Extensive API
    • 5
      Easy to deploy
    • 4
      VPC Support
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      Integrated with various AWS services
    CONS OF AWS LAMBDA
    • 5
      Cant execute ruby or go
    • 0
      Can't execute PHP w/o significant effort

    related AWS Lambda posts

    Jeyabalaji Subramanian

    Recently we were looking at a few robust and cost-effective ways of replicating the data that resides in our production MongoDB to a PostgreSQL database for data warehousing and business intelligence.

    We set ourselves the following criteria for the optimal tool that would do this job: - The data replication must be near real-time, yet it should NOT impact the production database - The data replication must be horizontally scalable (based on the load), asynchronous & crash-resilient

    Based on the above criteria, we selected the following tools to perform the end to end data replication:

    We chose MongoDB Stitch for picking up the changes in the source database. It is the serverless platform from MongoDB. One of the services offered by MongoDB Stitch is Stitch Triggers. Using stitch triggers, you can execute a serverless function (in Node.js) in real time in response to changes in the database. When there are a lot of database changes, Stitch automatically "feeds forward" these changes through an asynchronous queue.

    We chose Amazon SQS as the pipe / message backbone for communicating the changes from MongoDB to our own replication service. Interestingly enough, MongoDB stitch offers integration with AWS services.

    In the Node.js function, we wrote minimal functionality to communicate the database changes (insert / update / delete / replace) to Amazon SQS.

    Next we wrote a minimal micro-service in Python to listen to the message events on SQS, pickup the data payload & mirror the DB changes on to the target Data warehouse. We implemented source data to target data translation by modelling target table structures through SQLAlchemy . We deployed this micro-service as AWS Lambda with Zappa. With Zappa, deploying your services as event-driven & horizontally scalable Lambda service is dumb-easy.

    In the end, we got to implement a highly scalable near realtime Change Data Replication service that "works" and deployed to production in a matter of few days!

    See more
    Tim Nolet

    Heroku Docker GitHub Node.js hapi Vue.js AWS Lambda Amazon S3 PostgreSQL Knex.js Checkly is a fairly young company and we're still working hard to find the correct mix of product features, price and audience.

    We are focussed on tech B2B, but I always wanted to serve solo developers too. So I decided to make a $7 plan.

    Why $7? Simply put, it seems to be a sweet spot for tech companies: Heroku, Docker, Github, Appoptics (Librato) all offer $7 plans. They must have done a ton of research into this, so why not piggy back that and try it out.

    Enough biz talk, onto tech. The challenges were:

    • Slice of a portion of the functionality so a $7 plan is still profitable. We call this the "plan limits"
    • Update API and back end services to handle and enforce plan limits.
    • Update the UI to kindly state plan limits are in effect on some part of the UI.
    • Update the pricing page to reflect all changes.
    • Keep the actual processing backend, storage and API's as untouched as possible.

    In essence, we went from strictly volume based pricing to value based pricing. Here come the technical steps & decisions we made to get there.

    1. We updated our PostgreSQL schema so plans now have an array of "features". These are string constants that represent feature toggles.
    2. The Vue.js frontend reads these from the vuex store on login.
    3. Based on these values, the UI has simple v-if statements to either just show the feature or show a friendly "please upgrade" button.
    4. The hapi API has a hook on each relevant API endpoint that checks whether a user's plan has the feature enabled, or not.

    Side note: We offer 10 SMS messages per month on the developer plan. However, we were not actually counting how many people were sending. We had to update our alerting daemon (that runs on Heroku and triggers SMS messages via AWS SNS) to actually bump a counter.

    What we build is basically feature-toggling based on plan features. It is very extensible for future additions. Our scheduling and storage backend that actually runs users' monitoring requests (AWS Lambda) and stores the results (S3 and Postgres) has no knowledge of all of this and remained unchanged.

    Hope this helps anyone building out their SaaS and is in a similar situation.

    See more
    Batch logo

    Batch

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    Free retention toolkit for indie developers & startups - push notifications, user analytics, reward engine, and native ads
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    PROS OF BATCH
    • 1
      Revenuecat
    CONS OF BATCH
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      related Batch posts

      Google Cloud Run logo

      Google Cloud Run

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      Run stateless HTTP containers on a fully managed environment or in your own GKE cluster
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      PROS OF GOOGLE CLOUD RUN
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        Pay per use
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        Fully managed
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        HTTPS endpoints
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        Concurrency: multiple requests sent to each container
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        Custom domains with auto SSL
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        Serverless
      • 6
        Deploy containers
      • 4
        "Invoke IAM permission" to manage authentication
      CONS OF GOOGLE CLOUD RUN
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        related Google Cloud Run posts

        I use Google Cloud Run because it's like bring your own docker image to Google Cloud Functions.

        I use it for building Dash Apps

        It creates a nice url for web apps, and I see it being the evolution of serverless if GCP can scale this up.

        My Real-Time Python App Example

        See more

        What are the best options to host a Spring Boot application that acts as a receiver and publisher from Google Cloud Pub/Sub. I am using Google App Engine to do that, but there is Google Cloud Dataflow and Google Cloud Run that can be used. Which is the best option that can be used for this purpose and also that can handle the failover scenarios as well. Thanks!

        See more
        Beanstalk logo

        Beanstalk

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        Private code hosting for teams.
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        PROS OF BEANSTALK
        • 14
          Ftp deploy
        • 9
          Deployment
        • 8
          Easy to navigate
        • 4
          Code Editing
        • 4
          HipChat Integration
        • 4
          Integrations
        • 3
          Code review
        • 2
          HTML Preview
        • 1
          Security
        • 1
          Blame Tool
        • 1
          Cohesion
        CONS OF BEANSTALK
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          related Beanstalk posts

          Docker logo

          Docker

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          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