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  5. Argo vs Skaffold

Argo vs Skaffold

OverviewComparisonAlternatives

Overview

Skaffold
Skaffold
Stacks86
Followers186
Votes0
Argo
Argo
Stacks761
Followers470
Votes6

Argo vs Skaffold: What are the differences?

Introduction

In the world of Kubernetes deployment tools, Argo and Skaffold are two popular choices. While both tools have the objective of simplifying the deployment process, there are key differences between the two. In this article, we will explore six significant differences between Argo and Skaffold.

  1. Architecture: The fundamental difference between Argo and Skaffold lies in their architecture. Argo is built as a workflow engine, while Skaffold is designed as a local development tool. Argo focuses on defining and executing complex workflows, making it ideal for CI/CD pipelines and automation tasks. On the other hand, Skaffold enables developers to rapidly iterate their applications locally with ease.

  2. Workflow vs. Configuration: Argo emphasizes defining and managing workflows using YAML or JSON configuration files. These workflows consist of various steps that can be executed sequentially, in parallel, or conditionally. In contrast, Skaffold's configuration is centered around defining how to build, push, and deploy applications to a Kubernetes cluster. It provides an intuitive way to automate these processes and enables developers to focus on writing code.

  3. Support for Helm: Helm, a popular package manager for Kubernetes, can be utilized with both Argo and Skaffold. However, the level of support differs. Argo has first-class integration with Helm charts, allowing users to define Helm-based workflow steps easily. While Skaffold does not directly integrate with Helm, it provides extensibility through custom scripts and allows developers to invoke Helm commands within their build and deployment pipeline.

  4. Developer Experience: Skaffold places a strong emphasis on developer experience by providing fast iterative development cycles. It automatically synchronizes local code changes into the deployment environment, allowing developers to see their changes in real-time. Argo, on the other hand, is more focused on automation and orchestration, making it less developer-centric.

  5. Integration with GitOps: Argo has native support for GitOps workflows, which promote declarative and version-controlled deployment practices. It seamlessly integrates with Git repositories and continuously monitors changes to automatically trigger the desired workflows. Skaffold, while not specifically designed for GitOps, can be incorporated into a GitOps workflow by integrating with other tools such as Flux or Argo CD.

  6. Community and Maturity: Both Argo and Skaffold have active and growing communities, but Argo benefits from being a part of the Cloud Native Computing Foundation (CNCF) ecosystem. It has a larger user base and a more significant number of contributors, leading to a more mature tool with extensive documentation, support, and regular updates. Skaffold, although still widely used, may be considered relatively newer in comparison.

In Summary, Argo and Skaffold differ in terms of their architecture, focus (workflow vs. configuration), support for Helm, developer experience, integration with GitOps, and community maturity. These tools cater to different needs and use cases, with Argo excelling in automation and orchestration, while Skaffold enhances the local development experience.

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Detailed Comparison

Skaffold
Skaffold
Argo
Argo

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.

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).

No server-side component. No overhead to your cluster.;Detect changes in your source code and automatically build/push/deploy.;Image tag management. Stop worrying about updating the image tags in Kubernetes manifests to push out changes during development.;Supports existing tooling and workflows. Build and deploy APIs make each implementation composable to support many different workflows.;Support for multiple application components. Build and deploy only the pieces of your stack that have changed.;Deploy regularly when saving files or run one off deployments using the same configuration
DAG or Steps based declaration of workflows;Artifact support (S3, Artifactory, HTTP, Git, raw);Step level input & outputs (artifacts/parameters);Loops;Parameterization;Conditionals;Timeouts (step & workflow level);Retry (step & workflow level);Resubmit (memoized);Suspend & Resume;Cancellation;K8s resource orchestration;Exit Hooks (notifications, cleanup);Garbage collection of completed workflow;Scheduling (affinity/tolerations/node selectors);Volumes (ephemeral/existing);Parallelism limits;Daemoned steps;DinD (docker-in-docker);Script steps
Statistics
Stacks
86
Stacks
761
Followers
186
Followers
470
Votes
0
Votes
6
Pros & Cons
No community feedback yet
Pros
  • 3
    Open Source
  • 2
    Autosinchronize the changes to deploy
  • 1
    Online service, no need to install anything
Integrations
Kubernetes
Kubernetes
Google Kubernetes Engine
Google Kubernetes Engine
Docker
Docker
Kubernetes
Kubernetes
Docker
Docker

What are some alternatives to Skaffold, Argo?

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.

Rancher

Rancher

Rancher is an open source container management platform that includes full distributions of Kubernetes, Apache Mesos and Docker Swarm, and makes it simple to operate container clusters on any cloud or infrastructure platform.

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.

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.

Tutum

Tutum

Tutum lets developers easily manage and run lightweight, portable, self-sufficient containers from any application. AWS-like control, Heroku-like ease. The same container that a developer builds and tests on a laptop can run at scale in Tutum.

Portainer

Portainer

It is a universal container management tool. It works with Kubernetes, Docker, Docker Swarm and Azure ACI. It allows you to manage containers without needing to know platform-specific code.

Codefresh

Codefresh

Automate and parallelize testing. Codefresh allows teams to spin up on-demand compositions to run unit and integration tests as part of the continuous integration process. Jenkins integration allows more complex pipelines.

CAST.AI

CAST.AI

It is an AI-driven cloud optimization platform for Kubernetes. Instantly cut your cloud bill, prevent downtime, and 10X the power of DevOps.

k3s

k3s

Certified Kubernetes distribution designed for production workloads in unattended, resource-constrained, remote locations or inside IoT appliances. Supports something as small as a Raspberry Pi or as large as an AWS a1.4xlarge 32GiB server.

Flocker

Flocker

Flocker is a data volume manager and multi-host Docker cluster management tool. With it you can control your data using the same tools you use for your stateless applications. This means that you can run your databases, queues and key-value stores in Docker and move them around as easily as the rest of your app.

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