Kubernetes vs Packer

Overview

Packer

Stacks573

Followers566

Votes41

Kubernetes

Stacks61.2K

Followers52.8K

Votes685

Kubernetes vs Packer: What are the differences?

Introduction

Kubernetes and Packer are both widely used tools in the DevOps world. While they serve different purposes, they both contribute to the automation and management of infrastructure. However, there are some key differences between the two.

Scalability: One major difference between Kubernetes and Packer is their focus on scalability. Kubernetes is primarily designed for managing containers and orchestrating their deployment, scaling, and management across a cluster of nodes. On the other hand, Packer is a tool for creating machine images or virtual machine templates. It allows you to build scalable, identical machine images for different platforms, including virtual machines, containers, and cloud providers.
Abstraction Level: Another key difference between Kubernetes and Packer is their level of abstraction. Kubernetes operates at a higher level of abstraction, providing a container orchestration platform that abstracts away the underlying infrastructure details. It focuses on managing applications running in containers. On the other hand, Packer operates at a lower level of abstraction, allowing you to define and customize machine images or templates for different platforms. It provides more control over the infrastructure stack.
Purpose: Kubernetes is primarily used for deploying and managing containerized applications at scale. It provides features like automatic scaling, service discovery, load balancing, and self-healing. Packer, on the other hand, is used for creating machine images or templates that are used as a base for deploying applications or infrastructure. It allows you to define and provision software, dependencies, and configuration as part of the image creation process.
Focus: Kubernetes focuses on container orchestration and management, providing features like pod management, service discovery, and load balancing. It simplifies the deployment and scaling of containerized applications. Packer, on the other hand, focuses on the image creation process, allowing you to define and provision the software stack, dependencies, and configurations for the image. It aims to provide a consistent base image for deployment.
Deployment Targets: Kubernetes is designed to be platform-agnostic and can be used to deploy containers across various cloud providers or on-premises infrastructure. It provides a unified API to manage the deployment and scaling of containerized applications. Packer, on the other hand, supports a wide range of deployment targets, including virtual machines, containers, and cloud platforms. It allows you to create machine images or templates that can be used on different infrastructure providers.
Workflow: The workflow of Kubernetes and Packer differs significantly. Kubernetes focuses on automating the deployment, scaling, and management of containerized applications. It provides declarative configuration files (YAML or JSON) to define the desired state of the infrastructure. Packer, on the other hand, follows an imperative workflow, where you define the steps and configurations needed to build the machine image. It supports various builders and provisioners to customize the image creation process.

In summary, Kubernetes is a container orchestration platform that focuses on managing containerized applications at scale, while Packer is a tool for creating machine images or templates that can be used as a base for deploying applications or infrastructure. Kubernetes operates at a higher level of abstraction and provides extensive automation and management capabilities, while Packer allows for more control over the image creation process and supports various deployment targets.

Share your Stack

Help developers discover the tools you use. Get visibility for your team's tech choices and contribute to the community's knowledge.

View Docs

CLI (Node.js)

Manual

Advice on Packer, Kubernetes

Simon

Senior Fullstack Developer at QUANTUSflow Software GmbH

Apr 27, 2020

Decidedon

GitHub

GitHub Pages

Markdown

Our whole DevOps stack consists of the following tools:

@{GitHub}|tool:27| (incl. @{GitHub Pages}|tool:683|/@{Markdown}|tool:1147| for Documentation, GettingStarted and HowTo's) for collaborative review and code management tool
Respectively @{Git}|tool:1046| as revision control system
@{SourceTree}|tool:1599| as @{Git}|tool:1046| GUI
@{Visual Studio Code}|tool:4202| as IDE
@{CircleCI}|tool:190| for continuous integration (automatize development process)
@{Prettier}|tool:7035| / @{TSLint}|tool:5561| / @{ESLint}|tool:3337| as code linter
@{SonarQube}|tool:2638| as quality gate
@{Docker}|tool:586| as container management (incl. @{Docker Compose}|tool:3136| for multi-container application management)
@{VirtualBox}|tool:774| for operating system simulation tests
@{Kubernetes}|tool:1885| as cluster management for docker containers
@{Heroku}|tool:133| for deploying in test environments
@{nginx}|tool:1052| as web server (preferably used as facade server in production environment)
@{SSLMate}|tool:2752| (using @{OpenSSL}|tool:3091|) for certificate management
@{Amazon EC2}|tool:18| (incl. @{Amazon S3}|tool:25|) for deploying in stage (production-like) and production environments
@{PostgreSQL}|tool:1028| as preferred database system
@{Redis}|tool:1031| 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.

12.8M views12.8M

Comments

Detailed Comparison

Packer	Kubernetes
Packer automates the creation of any type of machine image. It embraces modern configuration management by encouraging you to use automated scripts to install and configure the software within your Packer-made images.	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.
Super fast infrastructure deployment. Packer images allow you to launch completely provisioned and configured machines in seconds, rather than several minutes or hours.;Multi-provider portability. Because Packer creates identical images for multiple platforms, you can run production in AWS, staging/QA in a private cloud like OpenStack, and development in desktop virtualization solutions such as VMware or VirtualBox.;Improved stability. Packer installs and configures all the software for a machine at the time the image is built. If there are bugs in these scripts, they'll be caught early, rather than several minutes after a machine is launched.;Greater testability. After a machine image is built, that machine image can be quickly launched and smoke tested to verify that things appear to be working. If they are, you can be confident that any other machines launched from that image will function properly.	Lightweight, simple and accessible;Built for a multi-cloud world, public, private or hybrid;Highly modular, designed so that all of its components are easily swappable
Statistics
Stacks 573	Stacks 61.2K
Followers 566	Followers 52.8K
Votes 41	Votes 685
Pros & Cons
Pros 27 Cross platform builds 8 Vm creation automation 4 Bake in security 1 Easy to use 1 Good documentation	Pros 166 Leading docker container management solution 130 Simple and powerful 108 Open source 76 Backed by google 58 The right abstractions Cons 16 Steep learning curve 15 Poor workflow for development 8 Orchestrates only infrastructure 4 High resource requirements for on-prem clusters 2 Too heavy for simple systems
Integrations
Amazon EC2 DigitalOcean Docker Google Compute Engine OpenStack VirtualBox	Vagrant Docker Rackspace Cloud Servers Microsoft Azure Google Compute Engine Ansible Google Kubernetes Engine

What are some alternatives to Packer, Kubernetes?

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

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

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 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

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.

AWS CloudFormation

You can use AWS CloudFormation’s sample templates or create your own templates to describe the AWS resources, and any associated dependencies or runtime parameters, required to run your application. You don’t need to figure out the order in which AWS services need to be provisioned or the subtleties of how to make those dependencies work.

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.

Scalr

Scalr is a remote state & operations backend for Terraform with access controls, policy as code, and many quality of life features.

Pulumi

Pulumi is a cloud development platform that makes creating cloud programs easy and productive. Skip the YAML and just write code. Pulumi is multi-language, multi-cloud and fully extensible in both its engine and ecosystem of packages.

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.

Related Comparisons

Stacks573

Followers566

Votes41

Kubernetes

Stacks61.2K

Followers52.8K

Votes685

Kubernetes vs Packer: What are the differences?

Introduction

Scalability: One major difference between Kubernetes and Packer is their focus on scalability. Kubernetes is primarily designed for managing containers and orchestrating their deployment, scaling, and management across a cluster of nodes. On the other hand, Packer is a tool for creating machine images or virtual machine templates. It allows you to build scalable, identical machine images for different platforms, including virtual machines, containers, and cloud providers.
Abstraction Level: Another key difference between Kubernetes and Packer is their level of abstraction. Kubernetes operates at a higher level of abstraction, providing a container orchestration platform that abstracts away the underlying infrastructure details. It focuses on managing applications running in containers. On the other hand, Packer operates at a lower level of abstraction, allowing you to define and customize machine images or templates for different platforms. It provides more control over the infrastructure stack.
Purpose: Kubernetes is primarily used for deploying and managing containerized applications at scale. It provides features like automatic scaling, service discovery, load balancing, and self-healing. Packer, on the other hand, is used for creating machine images or templates that are used as a base for deploying applications or infrastructure. It allows you to define and provision software, dependencies, and configuration as part of the image creation process.
Focus: Kubernetes focuses on container orchestration and management, providing features like pod management, service discovery, and load balancing. It simplifies the deployment and scaling of containerized applications. Packer, on the other hand, focuses on the image creation process, allowing you to define and provision the software stack, dependencies, and configurations for the image. It aims to provide a consistent base image for deployment.
Deployment Targets: Kubernetes is designed to be platform-agnostic and can be used to deploy containers across various cloud providers or on-premises infrastructure. It provides a unified API to manage the deployment and scaling of containerized applications. Packer, on the other hand, supports a wide range of deployment targets, including virtual machines, containers, and cloud platforms. It allows you to create machine images or templates that can be used on different infrastructure providers.
Workflow: The workflow of Kubernetes and Packer differs significantly. Kubernetes focuses on automating the deployment, scaling, and management of containerized applications. It provides declarative configuration files (YAML or JSON) to define the desired state of the infrastructure. Packer, on the other hand, follows an imperative workflow, where you define the steps and configurations needed to build the machine image. It supports various builders and provisioners to customize the image creation process.

Advice on Packer, Kubernetes

Simon

Senior Fullstack Developer at QUANTUSflow Software GmbH

Apr 27, 2020

Decidedon

GitHub

GitHub Pages

Markdown

Our whole DevOps stack consists of the following tools:

@{GitHub}|tool:27| (incl. @{GitHub Pages}|tool:683|/@{Markdown}|tool:1147| for Documentation, GettingStarted and HowTo's) for collaborative review and code management tool
Respectively @{Git}|tool:1046| as revision control system
@{SourceTree}|tool:1599| as @{Git}|tool:1046| GUI
@{Visual Studio Code}|tool:4202| as IDE
@{CircleCI}|tool:190| for continuous integration (automatize development process)
@{Prettier}|tool:7035| / @{TSLint}|tool:5561| / @{ESLint}|tool:3337| as code linter
@{SonarQube}|tool:2638| as quality gate
@{Docker}|tool:586| as container management (incl. @{Docker Compose}|tool:3136| for multi-container application management)
@{VirtualBox}|tool:774| for operating system simulation tests
@{Kubernetes}|tool:1885| as cluster management for docker containers
@{Heroku}|tool:133| for deploying in test environments
@{nginx}|tool:1052| as web server (preferably used as facade server in production environment)
@{SSLMate}|tool:2752| (using @{OpenSSL}|tool:3091|) for certificate management
@{Amazon EC2}|tool:18| (incl. @{Amazon S3}|tool:25|) for deploying in stage (production-like) and production environments
@{PostgreSQL}|tool:1028| as preferred database system
@{Redis}|tool:1031| 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.

12.8M views12.8M

Comments

Detailed Comparison

Packer	Kubernetes
Packer automates the creation of any type of machine image. It embraces modern configuration management by encouraging you to use automated scripts to install and configure the software within your Packer-made images.	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.
Super fast infrastructure deployment. Packer images allow you to launch completely provisioned and configured machines in seconds, rather than several minutes or hours.;Multi-provider portability. Because Packer creates identical images for multiple platforms, you can run production in AWS, staging/QA in a private cloud like OpenStack, and development in desktop virtualization solutions such as VMware or VirtualBox.;Improved stability. Packer installs and configures all the software for a machine at the time the image is built. If there are bugs in these scripts, they'll be caught early, rather than several minutes after a machine is launched.;Greater testability. After a machine image is built, that machine image can be quickly launched and smoke tested to verify that things appear to be working. If they are, you can be confident that any other machines launched from that image will function properly.	Lightweight, simple and accessible;Built for a multi-cloud world, public, private or hybrid;Highly modular, designed so that all of its components are easily swappable
Statistics
Stacks 573	Stacks 61.2K
Followers 566	Followers 52.8K
Votes 41	Votes 685
Pros & Cons
Pros 27 Cross platform builds 8 Vm creation automation 4 Bake in security 1 Easy to use 1 Good documentation	Pros 166 Leading docker container management solution 130 Simple and powerful 108 Open source 76 Backed by google 58 The right abstractions Cons 16 Steep learning curve 15 Poor workflow for development 8 Orchestrates only infrastructure 4 High resource requirements for on-prem clusters 2 Too heavy for simple systems
Integrations
Amazon EC2 DigitalOcean Docker Google Compute Engine OpenStack VirtualBox	Vagrant Docker Rackspace Cloud Servers Microsoft Azure Google Compute Engine Ansible Google Kubernetes Engine

Kubernetes vs Packer

Overview

Kubernetes vs Packer: What are the differences?