Reduce VMware Costs Through Native Cloud Adoption
A phased migration to native Google Cloud virtual machines reduced VMware dependency, preserved Veeam operations, and created a platform for research, AI, and future application modernization.
Challenge
Like many organizations, this higher education institution faced rising VMware licensing costs and needed a strategy that would reduce long-term operating expenses without disrupting academic operations, administrative services, or ongoing technology initiatives.
The institution maintained a substantial VMware footprint supporting academic, administrative, and infrastructure workloads. Leadership wanted to reduce dependence on VMware while accelerating its cloud strategy.
As part of its evaluation, the college considered VMware-based cloud offerings. The analysis showed that maintaining VMware licensing and architecture in the cloud would provide limited financial benefit at the scale of the environment. In several scenarios, projected costs were comparable to, or greater than, continuing to operate VMware on premises.
Google Cloud was already supporting research projects across the institution, making it a familiar and strategically aligned platform. The college also had an internal AI initiative that could eventually scale into Google Cloud as requirements and adoption matured.
Business requirements
Reduce VMware-related costs
Accelerate cloud adoption
Avoid carrying VMware licensing into the cloud
Maintain backup and recovery processes
Support future modernization initiatives
Create a foundation for research and AI
Move workloads at a business-driven pace
Solution
The college selected Google Cloud as its strategic infrastructure platform and chose native Google Compute Engine virtual machines rather than VMware-based cloud infrastructure.
A Google Cloud landing zone was designed and deployed to establish a secure, scalable foundation for migration and future cloud adoption. Google also provided incentives that helped offset a portion of the implementation and migration costs, strengthening the financial case for the project.
Foundation delivered
Landing zone architecture and deployment
Identity and access management configuration
Network connectivity and routing design
Security and governance controls
Migration tooling implementation
Operational readiness planning
Migration Approach
The migration was performed while the company continued normal operations. Veeam was used to move virtual machines from VMware to Hyper-V in controlled waves, limiting downtime to the workloads included in each planned outage.
Rather than taking the full environment offline, only small portions of the infrastructure were unavailable during scheduled cutovers. This reduced business disruption and allowed application validation to occur incrementally.
The team converted VMware hosts as additional Hyper-V resources were needed. After workloads were moved from a VMware host, that server was rebuilt as a Hyper-V host and returned to service to provide capacity for subsequent migration waves.
Results at a Glance
Business operations continued throughout the migration
Veeam moved workloads between hypervisors
Cutovers occurred during planned maintenance windows
Only affected workload groups experienced downtime
Hosts converted as Hyper-V capacity was required
No temporary compute platform was needed
Approximately 70% of the VMware environment was identified as suitable for lift-and-shift migration. Once the foundation and tools were in place, the college controlled migration timing based on application readiness, maintenance windows, and institutional priorities.
Why Native Google Cloud
Native Google Cloud virtual machines provided a more direct path to the financial and strategic benefits the institution sought. Rather than carrying VMware licensing and architecture into the cloud, the college established a native platform that could support infrastructure today and broader modernization over time.
Strategic alignment
Eliminated VMware licensing for migrated workloads
Improved long-term infrastructure economics
Leveraged available Google migration incentives
Built on an existing research cloud footprint
Created a path to scale the internal AI initiative
Enabled future application modernization
Business Outcomes
Reduced VMware Dependency: Approximately 70% of the VMware footprint moved to Google Cloud, reducing exposure to VMware licensing costs.
Avoided VMware-in-the-Cloud Costs: Native Google Cloud virtual machines were selected after VMware-based cloud options did not deliver the desired financial outcome at this scale.
Improved Financial Return: Google Cloud incentives offset a portion of migration costs while native services avoided VMware licensing for migrated workloads.
Enabled Customer-Paced Adoption: The landing zone and migration framework allowed internal teams to control migration schedules and priorities.
Preserved Existing Operations: Veeam continued protecting workloads across both cloud and on-premises infrastructure.
Supported a Practical Hybrid Strategy: On-premises workloads remained on VMware during the project while the institution evaluated future options.
Created a Foundation for Research and AI: The expanded Google Cloud footprint aligned infrastructure with existing research use and future AI scale requirements.
Results
The college established Google Cloud as a strategic infrastructure platform and enabled approximately 70% of its VMware environment to move to native Google Cloud virtual machines.
The secure landing zone, migration tooling, and phased delivery model reduced dependence on VMware without requiring a disruptive migration event. Google Cloud incentives improved the business case by offsetting part of the migration investment, while Veeam continued to protect workloads across both environments.
The move also aligned core infrastructure with the Google Cloud services already used for research. It created a platform where the institution can scale its internal AI initiative and pursue post-migration refactoring as individual workloads justify modernization.
Workloads requiring an on-premises presence remained on VMware during the project, preserving operational flexibility while the institution evaluates the long-term future of the remaining environment.
Challenge:
Rising VMware costs and limited financial benefit from VMware-based cloud offerings prompted an alternative cloud strategy.
Solution:
Deployed a Google Cloud landing zone, leveraged Google migration incentives, and enabled approximately 70% of workloads to move to native Google Cloud virtual machines.
Outcome:
Reduced VMware dependency, preserved Veeam operations, and established a foundation for research, AI, and post-migration refactoring
