Alternatives to Code Climate logo

Alternatives to Code Climate

Codacy, Codecov, Coveralls, SonarQube, and GitPrime are the most popular alternatives and competitors to Code Climate.
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What is Code Climate and what are its top alternatives?

After each Git push, Code Climate analyzes your code for complexity, duplication, and common smells to determine changes in quality and surface technical debt hotspots.
Code Climate is a tool in the Code Review category of a tech stack.

Top Alternatives to Code Climate

  • Codacy
    Codacy

    Codacy automates code reviews and monitors code quality on every commit and pull request on more than 40 programming languages reporting back the impact of every commit or PR, issues concerning code style, best practices and security. ...

  • Codecov
    Codecov

    Our patrons rave about our elegant coverage reports, integrated pull request comments, interactive commit graphs, our Chrome plugin and security. ...

  • Coveralls
    Coveralls

    Coveralls works with your CI server and sifts through your coverage data to find issues you didn't even know you had before they become a problem. Free for open source, pro accounts for private repos, instant sign up with GitHub OAuth. ...

  • SonarQube
    SonarQube

    SonarQube provides an overview of the overall health of your source code and even more importantly, it highlights issues found on new code. With a Quality Gate set on your project, you will simply fix the Leak and start mechanically improving. ...

  • GitPrime
    GitPrime

    GitPrime uses data from GitHub, GitLab, BitBucket—or any Git based code repository—to help engineering leaders move faster, optimize work patterns, and advocate for engineering with concrete data. ...

  • RuboCop
    RuboCop

    RuboCop is a Ruby static code analyzer. Out of the box it will enforce many of the guidelines outlined in the community Ruby Style Guide. ...

  • Scrutinizer
    Scrutinizer

    Scrutinizer is a continuous inspection platform helping you to create better software. ...

  • Git
    Git

    Git is a free and open source distributed version control system designed to handle everything from small to very large projects with speed and efficiency. ...

Code Climate alternatives & related posts

Codacy logo

Codacy

297
551
248
Automate and Standardize Code Reviews for 40+ languages
297
551
+ 1
248
PROS OF CODACY
  • 45
    Automated code review
  • 35
    Easy setup
  • 29
    Free for open source
  • 20
    Customizable
  • 18
    Helps reduce technical debt
  • 14
    Better coding
  • 13
    Best scala support
  • 11
    Faster Employee Onboarding
  • 10
    Duplication detector
  • 10
    Great UI
  • 9
    PHP integration
  • 6
    Python inspection
  • 5
    Tools for JVM analysis
  • 5
    Many integrations
  • 4
    Github Integration
  • 3
    Must-have for Java
  • 3
    Easy Travis integration
  • 3
    Items can be ignored in the UI
  • 3
    Asdasdas
  • 2
    Gitlab
  • 0
    Asdas
CONS OF CODACY
  • 6
    No support for private Git or Azure DevOps git

related Codacy posts

Ganesa Vijayakumar
Full Stack Coder | Technical Architect · | 19 upvotes · 5.3M views

I'm planning to create a web application and also a mobile application to provide a very good shopping experience to the end customers. Shortly, my application will be aggregate the product details from difference sources and giving a clear picture to the user that when and where to buy that product with best in Quality and cost.

I have planned to develop this in many milestones for adding N number of features and I have picked my first part to complete the core part (aggregate the product details from different sources).

As per my work experience and knowledge, I have chosen the followings stacks to this mission.

UI: I would like to develop this application using React, React Router and React Native since I'm a little bit familiar on this and also most importantly these will help on developing both web and mobile apps. In addition, I'm gonna use the stacks JavaScript, jQuery, jQuery UI, jQuery Mobile, Bootstrap wherever required.

Service: I have planned to use Java as the main business layer language as I have 7+ years of experience on this I believe I can do better work using Java than other languages. In addition, I'm thinking to use the stacks Node.js.

Database and ORM: I'm gonna pick MySQL as DB and Hibernate as ORM since I have a piece of good knowledge and also work experience on this combination.

Search Engine: I need to deal with a large amount of product data and it's in-detailed info to provide enough details to end user at the same time I need to focus on the performance area too. so I have decided to use Solr as a search engine for product search and suggestions. In addition, I'm thinking to replace Solr by Elasticsearch once explored/reviewed enough about Elasticsearch.

Host: As of now, my plan to complete the application with decent features first and deploy it in a free hosting environment like Docker and Heroku and then once it is stable then I have planned to use the AWS products Amazon S3, EC2, Amazon RDS and Amazon Route 53. I'm not sure about Microsoft Azure that what is the specialty in it than Heroku and Amazon EC2 Container Service. Anyhow, I will do explore these once again and pick the best suite one for my requirement once I reached this level.

Build and Repositories: I have decided to choose Apache Maven and Git as these are my favorites and also so popular on respectively build and repositories.

Additional Utilities :) - I would like to choose Codacy for code review as their Startup plan will be very helpful to this application. I'm already experienced with Google CheckStyle and SonarQube even I'm looking something on Codacy.

Happy Coding! Suggestions are welcome! :)

Thanks, Ganesa

See more

It is very important to have clean code. To be sure that the code quality is not really bad I use a few tools. I love SonarQube with many relevant hints and deep analysis of code. codebeat isn't so detailed, but it can find complexity issues and duplications. Codacy cannot find more bugs then your IDE. The winner for me is SonarQube that shows me really relevant bugs in my code.

See more
Codecov logo

Codecov

2.4K
322
102
Hosted coverage reports with awesome features to enhance your CI workflow
2.4K
322
+ 1
102
PROS OF CODECOV
  • 17
    More stable than coveralls
  • 17
    Easy setup
  • 14
    GitHub integration
  • 11
    They reply their users
  • 10
    Easy setup,great ui
  • 5
    Easily see per-commit coverage in GitHub
  • 5
    Steve is the man
  • 4
    Merges coverage from multiple CI jobs
  • 4
    Golang support
  • 3
    Free for public repositories
  • 3
    Code coverage
  • 3
    JSON in web hook
  • 3
    Newest Android SDK preinstalled
  • 2
    Cool diagrams
  • 1
    Bitbucket Integration
CONS OF CODECOV
  • 1
    GitHub org / team integration is a little too tight
  • 0
    Delayed results by hours since recent outage
  • 0
    Support does not respond to email

related Codecov posts

Tim Abbott
Shared insights
on
CodecovCodecovCoverallsCoveralls
at

We use Codecov because it's a lot better than Coveralls. Both of them provide the useful feature of having nice web-accessible reports of which files have what level of test coverage (though every coverage tool produces reasonably nice HTML in a directory on the local filesystem), and can report on PRs cases where significant new code was added without test coverage.

That said, I'm pretty unhappy with both of them for our use case. The fundamental problem with both of them is that they don't handle the ~1% probability situations with missing data due to networking flakiness well. The reason I think our use case is relevant is that we submit coverage data from multiple jobs (one that runs our frontend test suite and another that runs our backend test suite), and the coverage provider is responsible for combining that data together.

I think the problem is if a test suite runs successfully but due to some operational/networking error between Travis/CircleCI and Codecov the coverage data for part of the codebase doesn't get submitted, Codecov will report a huge coverage drop in a way that is very confusing for our contributors (because they experience it as "why did the coverage drop 12%, all I did was added a test").

We migrated from Coveralls to Codecov because empirically this sort of breakage happened 10x less on Codecov, but it still happens way more often than I'd like.

I wish they put more effort in their retry mechanism and/or providing clearer debugging information (E.g. a big "Missing data" banner) so that one didn't need to be specifically told to ignore Codecov/Coveralls when it reports a giant coverage drop.

See more
Shared insights
on
CodecovCodecovCoverallsCoveralls

Codecov Although I actually use both codecov and Coveralls, I very much like the graphs I get from codecov, and some of their diagnostic tools.

See more
Coveralls logo

Coveralls

1.4K
277
68
Track your project's code coverage over time, changes to files, and badge your GitHub repo
1.4K
277
+ 1
68
PROS OF COVERALLS
  • 45
    Free for public repositories
  • 13
    Code coverage
  • 7
    Ease of integration
  • 2
    More stable than Codecov
  • 1
    Combines coverage from multiple/parallel test runs
CONS OF COVERALLS
    Be the first to leave a con

    related Coveralls posts

    Tim Abbott
    Shared insights
    on
    CodecovCodecovCoverallsCoveralls
    at

    We use Codecov because it's a lot better than Coveralls. Both of them provide the useful feature of having nice web-accessible reports of which files have what level of test coverage (though every coverage tool produces reasonably nice HTML in a directory on the local filesystem), and can report on PRs cases where significant new code was added without test coverage.

    That said, I'm pretty unhappy with both of them for our use case. The fundamental problem with both of them is that they don't handle the ~1% probability situations with missing data due to networking flakiness well. The reason I think our use case is relevant is that we submit coverage data from multiple jobs (one that runs our frontend test suite and another that runs our backend test suite), and the coverage provider is responsible for combining that data together.

    I think the problem is if a test suite runs successfully but due to some operational/networking error between Travis/CircleCI and Codecov the coverage data for part of the codebase doesn't get submitted, Codecov will report a huge coverage drop in a way that is very confusing for our contributors (because they experience it as "why did the coverage drop 12%, all I did was added a test").

    We migrated from Coveralls to Codecov because empirically this sort of breakage happened 10x less on Codecov, but it still happens way more often than I'd like.

    I wish they put more effort in their retry mechanism and/or providing clearer debugging information (E.g. a big "Missing data" banner) so that one didn't need to be specifically told to ignore Codecov/Coveralls when it reports a giant coverage drop.

    See more
    Shared insights
    on
    CodecovCodecovCoverallsCoveralls

    Codecov Although I actually use both codecov and Coveralls, I very much like the graphs I get from codecov, and some of their diagnostic tools.

    See more
    SonarQube logo

    SonarQube

    1.7K
    2K
    52
    Continuous Code Quality
    1.7K
    2K
    + 1
    52
    PROS OF SONARQUBE
    • 26
      Tracks code complexity and smell trends
    • 16
      IDE Integration
    • 9
      Complete code Review
    • 1
      Difficult to deploy
    CONS OF SONARQUBE
    • 7
      Sales process is long and unfriendly
    • 7
      Paid support is poor, techs arrogant and unhelpful
    • 1
      Does not integrate with Snyk

    related SonarQube posts

    Simon Reymann
    Senior Fullstack Developer at QUANTUSflow Software GmbH · | 30 upvotes · 11M 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
    Ganesa Vijayakumar
    Full Stack Coder | Technical Architect · | 19 upvotes · 5.3M views

    I'm planning to create a web application and also a mobile application to provide a very good shopping experience to the end customers. Shortly, my application will be aggregate the product details from difference sources and giving a clear picture to the user that when and where to buy that product with best in Quality and cost.

    I have planned to develop this in many milestones for adding N number of features and I have picked my first part to complete the core part (aggregate the product details from different sources).

    As per my work experience and knowledge, I have chosen the followings stacks to this mission.

    UI: I would like to develop this application using React, React Router and React Native since I'm a little bit familiar on this and also most importantly these will help on developing both web and mobile apps. In addition, I'm gonna use the stacks JavaScript, jQuery, jQuery UI, jQuery Mobile, Bootstrap wherever required.

    Service: I have planned to use Java as the main business layer language as I have 7+ years of experience on this I believe I can do better work using Java than other languages. In addition, I'm thinking to use the stacks Node.js.

    Database and ORM: I'm gonna pick MySQL as DB and Hibernate as ORM since I have a piece of good knowledge and also work experience on this combination.

    Search Engine: I need to deal with a large amount of product data and it's in-detailed info to provide enough details to end user at the same time I need to focus on the performance area too. so I have decided to use Solr as a search engine for product search and suggestions. In addition, I'm thinking to replace Solr by Elasticsearch once explored/reviewed enough about Elasticsearch.

    Host: As of now, my plan to complete the application with decent features first and deploy it in a free hosting environment like Docker and Heroku and then once it is stable then I have planned to use the AWS products Amazon S3, EC2, Amazon RDS and Amazon Route 53. I'm not sure about Microsoft Azure that what is the specialty in it than Heroku and Amazon EC2 Container Service. Anyhow, I will do explore these once again and pick the best suite one for my requirement once I reached this level.

    Build and Repositories: I have decided to choose Apache Maven and Git as these are my favorites and also so popular on respectively build and repositories.

    Additional Utilities :) - I would like to choose Codacy for code review as their Startup plan will be very helpful to this application. I'm already experienced with Google CheckStyle and SonarQube even I'm looking something on Codacy.

    Happy Coding! Suggestions are welcome! :)

    Thanks, Ganesa

    See more
    GitPrime logo

    GitPrime

    12
    36
    0
    Metrics for data-driven engineering leaders
    12
    36
    + 1
    0
    PROS OF GITPRIME
      Be the first to leave a pro
      CONS OF GITPRIME
        Be the first to leave a con

        related GitPrime posts

        RuboCop logo

        RuboCop

        1.1K
        221
        41
        A Ruby static code analyzer, based on the community Ruby style guide
        1.1K
        221
        + 1
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        PROS OF RUBOCOP
        • 9
          Open-source
        • 8
          Completely free
        • 7
          Runs Offline
        • 4
          Follows the Ruby Style Guide by default
        • 4
          Can automatically fix some problems
        • 4
          Customizable
        • 2
          Atom package
        • 2
          Integrates with Vim/Emacs/Atom/Sublime/
        • 1
          Integrates With Custom CMS
        CONS OF RUBOCOP
          Be the first to leave a con

          related RuboCop posts

          Francisco Quintero
          Tech Lead at Dev As Pros · | 7 upvotes · 459.9K views

          For many(if not all) small and medium size business time and cost matter a lot.

          That's why languages, frameworks, tools, and services that are easy to use and provide 0 to productive in less time, it's best.

          Maybe Node.js frameworks might provide better features compared to Rails but in terms of MVPs, for us Rails is the leading alternative.

          Amazon EC2 might be cheaper and more customizable than Heroku but in the initial terms of a project, you need to complete configurationos and deploy early.

          Advanced configurations can be done down the road, when the project is running and making money, not before.

          But moving fast isn't the only thing we care about. We also take the job to leave a good codebase from the beginning and because of that we try to follow, as much as we can, style guides in Ruby with RuboCop and in JavaScript with ESLint and StandardJS.

          Finally, comunication and keeping a good history of conversations, decisions, and discussions is important so we use a mix of Slack and Twist

          See more
          Jerome Dalbert
          Principal Backend Software Engineer at StackShare · | 6 upvotes · 644.2K views

          The continuous integration process for our Rails backend app starts by opening a GitHub pull request. This triggers a CircleCI build and some Code Climate checks.

          The CircleCI build is a workflow that runs the following jobs:

          • check for security vulnerabilities with Brakeman
          • check code quality with RuboCop
          • run RSpec tests in parallel with the knapsack gem, and output test coverage reports with the simplecov gem
          • upload test coverage to Code Climate

          Code Climate checks the following:

          • code quality metrics like code complexity
          • test coverage minimum thresholds

          The CircleCI jobs and Code Climate checks above have corresponding GitHub status checks.

          Once all the mandatory GitHub checks pass and the code+functionality have been reviewed, developers can merge their pull request into our Git master branch. Code is then ready to deploy!

          #ContinuousIntegration

          See more
          Scrutinizer logo

          Scrutinizer

          88
          65
          20
          Continuous inspection platform - improve code quality and find bugs before they hit production
          88
          65
          + 1
          20
          PROS OF SCRUTINIZER
          • 7
            Github integration / sync
          • 4
            Bitbucket integration / sync
          • 2
            Gitlab integration / sync
          • 2
            Private Git repo sync
          • 1
            Python inspection
          • 1
            Easy setup
          • 1
            Code review features
          • 1
            Coverage Report changes
          • 1
            Free for open source
          CONS OF SCRUTINIZER
          • 1
            Pricing

          related Scrutinizer posts

          Git logo

          Git

          296.9K
          178.2K
          6.6K
          Fast, scalable, distributed revision control system
          296.9K
          178.2K
          + 1
          6.6K
          PROS OF GIT
          • 1.4K
            Distributed version control system
          • 1.1K
            Efficient branching and merging
          • 959
            Fast
          • 845
            Open source
          • 726
            Better than svn
          • 368
            Great command-line application
          • 306
            Simple
          • 291
            Free
          • 232
            Easy to use
          • 222
            Does not require server
          • 27
            Distributed
          • 22
            Small & Fast
          • 18
            Feature based workflow
          • 15
            Staging Area
          • 13
            Most wide-spread VSC
          • 11
            Role-based codelines
          • 11
            Disposable Experimentation
          • 7
            Frictionless Context Switching
          • 6
            Data Assurance
          • 5
            Efficient
          • 4
            Just awesome
          • 3
            Github integration
          • 3
            Easy branching and merging
          • 2
            Compatible
          • 2
            Flexible
          • 2
            Possible to lose history and commits
          • 1
            Rebase supported natively; reflog; access to plumbing
          • 1
            Light
          • 1
            Team Integration
          • 1
            Fast, scalable, distributed revision control system
          • 1
            Easy
          • 1
            Flexible, easy, Safe, and fast
          • 1
            CLI is great, but the GUI tools are awesome
          • 1
            It's what you do
          • 0
            Phinx
          CONS OF GIT
          • 16
            Hard to learn
          • 11
            Inconsistent command line interface
          • 9
            Easy to lose uncommitted work
          • 8
            Worst documentation ever possibly made
          • 5
            Awful merge handling
          • 3
            Unexistent preventive security flows
          • 3
            Rebase hell
          • 2
            Ironically even die-hard supporters screw up badly
          • 2
            When --force is disabled, cannot rebase
          • 1
            Doesn't scale for big data

          related Git posts

          Simon Reymann
          Senior Fullstack Developer at QUANTUSflow Software GmbH · | 30 upvotes · 11M 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 · 9.7M 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