Azure Cost Optimization: 1-Day Workshop

Mallow Oy

Analyse your total Azure spend and find ways to optimize, report and budget your cloud costs.

Almost every operation in cloud cost money. Underutilized and orphaned resources, wrong subscription/licensing models and lack of monitoring lead to situation where your organization is paying too much without visibility and predictability of cloud costs. Mallow can analyse your total Azure spend and find ways to optimize, report and budget your cloud costs.

WHY​?

  • Clutter resources that are running because nobody knows who owns them, what they do and what will break if taken down.​
  • You are uncertain of your current licensing and subscription models and if they are the best fit for you​
  • Is every VM rightsized, using correct VM family (SKU), are they running 8/16 but billed 24/7, could a service be replaced with PaaS with a fraction of VM costs?​
  • No monitoring, tagging of cost policies or budgeting in place, creating very unpredictable Azure spend​
  • Cost gateways not in place, accidental deployments of very costly resources are not gated​
  • It is not clear, what are you paying for in the cloud

TOPICS COVERED DURING THIS WORKSHOP

  • Analyze the subscription and Azure Advisor recommendations ​
  • Obsolete resources​
  • Reserved Instances and Hybrid Benefits​
  • Cost policies​
  • Autoscaling​
  • Rightsizing ​
  • PaaSification ​
  • Special VMs ​
  • Cost monitoring​
  • Identifying excess cost workflows​
  • Budgets, recommendation which should be implemented

WHAT WE EXPECT FROM YOU​

  • Relevant stakeholders available if needed​
  • Access to resources to be evaluated​

DELIVERABLES​

  • Written report of actions to be taken with priorities​
  • Estimated cost savings for suggested actions​
https://store-images.s-microsoft.com/image/apps.56970.3c13720b-2874-48d4-a967-7d5e1fad8518.1671992e-7b0f-4d94-8471-25a5f183f934.2087aa7c-3ede-4198-9808-a0ed6f32c69c
https://store-images.s-microsoft.com/image/apps.56970.3c13720b-2874-48d4-a967-7d5e1fad8518.1671992e-7b0f-4d94-8471-25a5f183f934.2087aa7c-3ede-4198-9808-a0ed6f32c69c
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