Alternatives to Skaffold logo

Alternatives to Skaffold

Helm, Argo, Docker Compose, Jib, and Spinnaker are the most popular alternatives and competitors to Skaffold.
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What is Skaffold and what are its top alternatives?

Skaffold is a command line tool that facilitates continuous development for Kubernetes applications. You can iterate on your application source code locally then deploy to local or remote Kubernetes clusters. Skaffold handles the workflow for building, pushing and deploying your application. It can also be used in an automated context such as a CI/CD pipeline to leverage the same workflow and tooling when moving applications to production.
Skaffold is a tool in the Container Tools category of a tech stack.
Skaffold is an open source tool with 11.4K GitHub stars and 1.2K GitHub forks. Here’s a link to Skaffold's open source repository on GitHub

Top Alternatives to Skaffold

  • Helm

    Helm

    Helm is the best way to find, share, and use software built for Kubernetes.

  • Argo

    Argo

    Argo is an open source container-native workflow engine for getting work done on Kubernetes. Argo is implemented as a Kubernetes CRD (Custom Resource Definition). ...

  • Docker Compose

    Docker Compose

    With Compose, you define a multi-container application in a single file, then spin your application up in a single command which does everything that needs to be done to get it running. ...

  • Jib

    Jib

    Jib builds Docker and OCI images for your Java applications and is available as plugins for Maven and Gradle. ...

  • Spinnaker

    Spinnaker

    Created at Netflix, it has been battle-tested in production by hundreds of teams over millions of deployments. It combines a powerful and flexible pipeline management system with integrations to the major cloud providers. ...

  • Jenkins

    Jenkins

    In a nutshell Jenkins CI is the leading open-source continuous integration server. Built with Java, it provides over 300 plugins to support building and testing virtually any project. ...

  • kaniko

    kaniko

    A tool to build container images from a Dockerfile, inside a container or Kubernetes cluster. kaniko doesn't depend on a Docker daemon and executes each command within a Dockerfile completely in userspace. This enables building container images in environments that can't easily or securely run a Docker daemon, such as a standard Kubernetes cluster. ...

  • 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. ...

Skaffold alternatives & related posts

Helm logo

Helm

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The Kubernetes Package Manager
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PROS OF HELM
  • 4
    Infrastructure as code
  • 3
    Open source
  • 2
    Easy setup
  • 1
    Testa­bil­i­ty and re­pro­ducibil­i­ty
CONS OF HELM
    Be the first to leave a con

    related Helm posts

    Emanuel Evans
    Senior Architect at Rainforest QA · | 13 upvotes · 578.1K views

    We recently moved our main applications from Heroku to Kubernetes . The 3 main driving factors behind the switch were scalability (database size limits), security (the inability to set up PostgreSQL instances in private networks), and costs (GCP is cheaper for raw computing resources).

    We prefer using managed services, so we are using Google Kubernetes Engine with Google Cloud SQL for PostgreSQL for our PostgreSQL databases and Google Cloud Memorystore for Redis . For our CI/CD pipeline, we are using CircleCI and Google Cloud Build to deploy applications managed with Helm . The new infrastructure is managed with Terraform .

    Read the blog post to go more in depth.

    See more
    Ido Shamun
    at The Elegant Monkeys · | 6 upvotes · 303.3K views

    Kubernetes powers our #backend services as it is very easy in terms of #devops (the managed version). We deploy everything using @helm charts as it provides us to manage deployments the same way we manage our code on GitHub . On every commit a CircleCI job is triggered to run the tests, build Docker images and deploy them to the registry. Finally on every master commit CircleCI also deploys the relevant service using Helm chart to our Kubernetes cluster

    See more
    Argo logo

    Argo

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    Container-native workflows for Kubernetes
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    195
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    PROS OF ARGO
      Be the first to leave a pro
      CONS OF ARGO
        Be the first to leave a con

        related Argo posts

        Docker Compose logo

        Docker Compose

        13.1K
        9.5K
        473
        Define and run multi-container applications with Docker
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        473
        PROS OF DOCKER COMPOSE
        • 119
          Multi-container descriptor
        • 108
          Fast development environment setup
        • 75
          Easy linking of containers
        • 65
          Simple yaml configuration
        • 58
          Easy setup
        • 15
          Yml or yaml format
        • 11
          Use Standard Docker API
        • 7
          Open source
        • 4
          Go from template to application in minutes
        • 4
          Can choose Discovery Backend
        • 2
          Kubernetes integration
        • 2
          Easy configuration
        • 2
          Scalable
        • 1
          Quick and easy
        CONS OF DOCKER COMPOSE
        • 7
          Tied to single machine
        • 4
          Still very volatile, changing syntax often

        related Docker Compose posts

        Simon Reymann
        Senior Fullstack Developer at QUANTUSflow Software GmbH · | 28 upvotes · 2.6M 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

        Recently I have been working on an open source stack to help people consolidate their personal health data in a single database so that AI and analytics apps can be run against it to find personalized treatments. We chose to go with a #containerized approach leveraging Docker #containers with a local development environment setup with Docker Compose and nginx for container routing. For the production environment we chose to pull code from GitHub and build/push images using Jenkins and using Kubernetes to deploy to Amazon EC2.

        We also implemented a dashboard app to handle user authentication/authorization, as well as a custom SSO server that runs on Heroku which allows experts to easily visit more than one instance without having to login repeatedly. The #Backend was implemented using my favorite #Stack which consists of FeathersJS on top of Node.js and ExpressJS with PostgreSQL as the main database. The #Frontend was implemented using React, Redux.js, Semantic UI React and the FeathersJS client. Though testing was light on this project, we chose to use AVA as well as ESLint to keep the codebase clean and consistent.

        See more
        Jib logo

        Jib

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        Containerize your Java application (by Google)
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        PROS OF JIB
        • 0
          No docker files to maintain
        • 0
          Build is faster than Docker
        • 0
          Native
        • 0
          Coder friendly with Maven and Gradle plugins
        CONS OF JIB
          Be the first to leave a con

          related Jib posts

          Spinnaker logo

          Spinnaker

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          Multi-cloud continuous delivery platform for releasing software changes with high velocity and confidence
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          PROS OF SPINNAKER
          • 9
            Mature
          CONS OF SPINNAKER
          • 2
            No GitOps
          • 1
            Configuration time
          • 1
            Management overhead
          • 1
            Ease of use

          related Spinnaker posts

          John Kodumal

          LaunchDarkly is almost a five year old company, and our methodology for deploying was state of the art... for 2014. We recently undertook a project to modernize the way we #deploy our software, moving from Ansible-based deploy scripts that executed on our local machines, to using Spinnaker (along with Terraform and Packer) as the basis of our deployment system. We've been using Armory's enterprise Spinnaker offering to make this project a reality.

          See more
          Jenkins logo

          Jenkins

          39.2K
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          An extendable open source continuous integration server
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          PROS OF JENKINS
          • 521
            Hosted internally
          • 463
            Free open source
          • 313
            Great to build, deploy or launch anything async
          • 243
            Tons of integrations
          • 208
            Rich set of plugins with good documentation
          • 108
            Has support for build pipelines
          • 72
            Open source and tons of integrations
          • 63
            Easy setup
          • 61
            It is open-source
          • 54
            Workflow plugin
          • 11
            Configuration as code
          • 10
            Very powerful tool
          • 9
            Many Plugins
          • 8
            Great flexibility
          • 8
            Git and Maven integration is better
          • 7
            Continuous Integration
          • 6
            Github integration
          • 6
            Slack Integration (plugin)
          • 5
            100% free and open source
          • 5
            Self-hosted GitLab Integration (plugin)
          • 5
            Easy customisation
          • 4
            Docker support
          • 3
            Pipeline API
          • 3
            Excellent docker integration
          • 3
            Platform idnependency
          • 3
            Fast builds
          • 2
            Hosted Externally
          • 2
            It`w worked
          • 2
            Can be run as a Docker container
          • 2
            Customizable
          • 2
            AWS Integration
          • 2
            It's Everywhere
          • 2
            JOBDSL
          • 1
            NodeJS Support
          • 1
            PHP Support
          • 1
            Ruby/Rails Support
          • 1
            Universal controller
          • 1
            Easily extendable with seamless integration
          • 1
            Build PR Branch Only
          CONS OF JENKINS
          • 12
            Workarounds needed for basic requirements
          • 8
            Groovy with cumbersome syntax
          • 6
            Plugins compatibility issues
          • 6
            Limited abilities with declarative pipelines
          • 5
            Lack of support
          • 4
            No YAML syntax
          • 2
            Too tied to plugins versions

          related Jenkins posts

          Thierry Schellenbach

          Releasing new versions of our services is done by Travis CI. Travis first runs our test suite. Once it passes, it publishes a new release binary to GitHub.

          Common tasks such as installing dependencies for the Go project, or building a binary are automated using plain old Makefiles. (We know, crazy old school, right?) Our binaries are compressed using UPX.

          Travis has come a long way over the past years. I used to prefer Jenkins in some cases since it was easier to debug broken builds. With the addition of the aptly named “debug build” button, Travis is now the clear winner. It’s easy to use and free for open source, with no need to maintain anything.

          #ContinuousIntegration #CodeCollaborationVersionControl

          See more
          Tymoteusz Paul
          Devops guy at X20X Development LTD · | 21 upvotes · 4.3M 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
          kaniko logo

          kaniko

          22
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          4
          Build container images in Kubernetes
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          + 1
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          PROS OF KANIKO
          • 3
            No need for docker demon
          • 1
            Automation using jules
          CONS OF KANIKO
          • 1
            Slow compared to docker

          related kaniko posts

          Kubernetes logo

          Kubernetes

          33.7K
          28.2K
          601
          Manage a cluster of Linux containers as a single system to accelerate Dev and simplify Ops
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          28.2K
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          601
          PROS OF KUBERNETES
          • 152
            Leading docker container management solution
          • 121
            Simple and powerful
          • 96
            Open source
          • 73
            Backed by google
          • 56
            The right abstractions
          • 24
            Scale services
          • 17
            Replication controller
          • 9
            Permission managment
          • 6
            Simple
          • 5
            Supports autoscaling
          • 5
            Cheap
          • 3
            Reliable
          • 3
            No cloud platform lock-in
          • 3
            Self-healing
          • 3
            Open, powerful, stable
          • 3
            Scalable
          • 3
            Promotes modern/good infrascture practice
          • 2
            Cloud Agnostic
          • 2
            Backed by Red Hat
          • 2
            Custom and extensibility
          • 2
            Quick cloud setup
          • 2
            Captain of Container Ship
          • 2
            A self healing environment with rich metadata
          • 1
            Everything of CaaS
          • 1
            Easy setup
          • 1
            Expandable
          • 1
            Runs on azure
          • 1
            Sfg
          • 1
            Golang
          • 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 · | 37 upvotes · 3.7M 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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