Cloud and Container Optimization for Google Cloud (GCP)

Automatically determine the cloud and container resources required to reduce cost and maximize performance in GCP

Google Cloud management and optimization

Optimize Your GCP Compute Engine (GCE) Instances

Densify’s machine learning analyzes workload patterns to determine the optimal size and family for your GCP Compute Engine instances, enabling you to:

  • Reduce application performance and stability issues associated with under allocating resources to your instances
  • Increase utilization and lower costs by ensuring that you are not overallocating capacity to your instances
  • Avoid leaving CPU, memory or other resources stranded by picking the wrong instance family.


Optimize Your Google Kubernetes Engine (GKE) Containers

Densify’s analytics predictively, precisely and continuously determine the appropriate resource settings for your GCP GKE containers allowing you to:

  • Avoid application performance and stability issues
  • Visualize the overall resource health of your entire Kubernetes environment with Histograms
  • Increase node and cluster utilization by avoiding allocating too much CPU and memory to your applications
  • Ensure cluster resource and namespace quotas are constantly aligned with app team requirements
  • Lower your cloud bill by deploying fewer nodes for the same containers

Densify automatically analyzes thousands of containers to determine optimal settings

Enable Collaboration with Product & Application Owners

Densify automatically produces Impact Analysis and Recommendation Reports to share with stakeholders, letting you: 

  • Clearly articulate details for every optimization recommendation, including predicted utilization, effort level, and cost impact
  • Include as an attachment to ITSM change tickets or integrate into business collaboration and approval workflows using Densify’s API 


Integration with Automation API’s and IaC

Integrate With CI/CD Pipelines & Automation Tools

  • Free your teams from manually selecting resources
  • Eliminate errors – use APIs to tie directly into infrastructure as code templates like Terraform or Google Cloud Deployment Manger
  • Ensure Performance by continuously aligning resources with application requirements

    Watch a K8s Automated Resource Optimization demo.

Google Cloud: Choosing GCE Machine Types:
C2, M2, N2, N2D & E2

Learn how to select the best Google Cloud Compute Engine machine for each of your workloads. Read this article How to Choose the Right Google Cloud Compute Engine Machine Type.