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Lift & Shift Migration: 2-Wk Optimization Assessment
Through a 2-week engagement, we will assess and adjust your newly migrated Azure virtual machines to ensure they run optimally, both from a cost and security standpoint.
Following a migration, it’s completely normal to notice that very few workloads are optimized from the get-go. Indeed, typical lift & shift migration methods will provision the same resources in the cloud as those present in the source environment. Completing a resource maximization phase, in which you monitor and adjust your cloud-related expenditures, will optimize your operational efficiency and harden your security.
This final step in the Lift & Shift Migration approach allows you to ensure your virtual machines run efficiently and that your costs are controlled and optimized. By monitoring workload performance and measuring trends in real time, you can also establish the right combination of standard and customized policies to manage your subscriptions and resources in an efficient and compatible manner, enabling you to get the most out of your new Azure environment.
During this two-week engagement, we provide the following services:
First week: • Set up monitoring tools • Monitor the Azure virtual machines
Second week: • Analyze the results • Right-size the Azure SKU's • Harden security with network security groups
For 30 years, ITI has combined the energy and innovation of a start-up with the experience of a firmly established company. With 450 employees who hold a total of 1,400 certifications, ITI provides high-level expertise to serve more than 2,000 clients. ITI’s Azure team is a leader in this practice, with thousands of hours of experience working on hundreds of architecture, implementation, migration and modernization projects. ITI’s methodical approach is based on the Microsoft Cloud Adoption Framework for Azure, which enables organizations, both public and private, to plan and execute their migration at a sustainable pace and reap benefits from it in the early stages.
Note: Pricing displayed is per virtual machine being assessed.