Cloud & Container Resource Management

Densify continuously analyzes your cloud and container workloads and automates the resource selection process—improving application performance and stability, reducing risk, and enabling prudent cloud spend.

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Densify’s cloud optimization engine

Automated Cloud Resource Optimization with Cloe

Densify’s machine learning enables your application instances to automatically become self-aware of their resource requirements and to dynamically match their needs to the optimal cloud resources. You get better performing apps with the most efficient use of your cloud budget.

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Automate Your Container Management

Achieve intelligent container sizing with full automation for Kubernetes to run your container environments safely and efficiently.

Automate Your Container Environments
Densify optimizes & automates the leading container platforms

Optimization as Code

One line of code continuously and automatically optimizes your cloud workloads—forever.

Discover Automated Cloud Workload Optimization
provider "aws" {
  region = "${var.aws_region}"
}
resource "aws_instance" "web" {
  name = "Web Server"
  #instance_type = "m4.large"
  instance_type = "${lookup(local.densify_spec,"appr_type") == "all" ? lookup(local.densify_spec,"rec_type") : lookup(local.densify_spec,"cur_type")}"
  ami = "${lookup(var.aws_amis, var.aws_region)}"
}
CI/CD Cheat Sheet Poster

Technical Cheat Sheet: The CI/CD Framework

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DevOps Challenge Quiz Quiz

Confirm Your DevOps Mastery!

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Guide to planning your CI/CD pipeline Whitepaper

The 8 Phases of Continuous Integration & Continuous Delivery

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Bring together your Finance, app admins, CAB, and IT Ops stakeholders around cloud management Blog Post

How to Unite All Business Stakeholders with Cloud Management

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See It in Action

Check out the expert guidance you will get from Densify with this quick overview.

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CloudOps, App Owners, & Finance—Everyone Wins

  • Automated cloud and container workload management
  • Improved application performance
  • Financially-prudent cloud resource selection