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  5. Argo vs K9s

Argo vs K9s

OverviewComparisonAlternatives

Overview

Argo
Argo
Stacks761
Followers470
Votes6
K9s
K9s
Stacks75
Followers103
Votes2
GitHub Stars31.7K
Forks2.0K

Argo vs K9s: What are the differences?

Introduction

In this analysis, we will compare and contrast the key differences between Argo and K9s. Both Argo and K9s are popular tools used in the Kubernetes ecosystem for managing and monitoring applications.

  1. CLI vs Web UI: Argo is primarily a CLI (Command Line Interface) tool, whereas K9s provides a web-based UI. This difference in interface can influence user preferences, with some users favoring the simplicity and efficiency of a CLI, while others may find a web UI more intuitive and visually appealing.

  2. Workflow vs Cluster Management: Argo focuses on workflow management within Kubernetes, providing features for creating, running, and monitoring complex workflows. On the other hand, K9s is primarily focused on providing a comprehensive overview and management of the Kubernetes cluster itself, allowing users to navigate and interact with various resources in the cluster.

  3. Visualization Capabilities: Argo incorporates visualization capabilities, allowing users to create and view graphical representations of workflows, helping in understanding the flow and dependencies between different tasks. K9s, on the other hand, does not offer visualization features, focusing more on providing a compact and efficient view of cluster resources.

  4. Customizability: Argo offers a high level of customization, allowing users to define and configure workflows based on their specific requirements. This flexibility includes support for custom resource definitions (CRDs) and integration with various tools like GitOps, enabling users to tailor the workflow management experience to their needs. In contrast, K9s is primarily designed to provide a consistent and streamlined interface for managing existing Kubernetes resources, with less emphasis on customization options.

  5. User Base and Community: Argo has gained a significant user base and has an active community contributing to its development and advancement. This has led to a rich ecosystem of extensions and integrations being available for Argo. K9s also has a growing user base, but its community is relatively smaller compared to Argo.

  6. Maturity and Stability: Argo has been in development for several years and has reached a level of maturity and stability that is trusted by many organizations for critical workflow management. K9s, although a reliable tool, is comparatively newer and may still be evolving rapidly with occasional updates and changes to its features.

In summary, Argo and K9s differ in their interface (CLI vs web UI), focus (workflow vs cluster management), visualization capabilities, customizability, user base and community support, and maturity and stability. Both tools have their strengths and cater to different use cases within the Kubernetes ecosystem.

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Detailed Comparison

Argo
Argo
K9s
K9s

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).

K9s provides a curses based terminal UI to interact with your Kubernetes clusters. The aim of this project is to make it easier to navigate, observe and manage your applications in the wild. K9s continually watches Kubernetes for changes and offers subsequent commands to interact with observed resources.

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
GitHub Stars
-
GitHub Stars
31.7K
GitHub Forks
-
GitHub Forks
2.0K
Stacks
761
Stacks
75
Followers
470
Followers
103
Votes
6
Votes
2
Pros & Cons
Pros
  • 3
    Open Source
  • 2
    Autosinchronize the changes to deploy
  • 1
    Online service, no need to install anything
Pros
  • 2
    Nice UI and fast way to manage my kubernetes clusters
Integrations
Kubernetes
Kubernetes
Docker
Docker
Kubernetes
Kubernetes

What are some alternatives to Argo, K9s?

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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