The university operated a large Google Cloud environment supporting digital learning platforms, enterprise systems, and academic services. Over time, infrastructure usage expanded rapidly without centralized optimization or governance processes in place.
A detailed assessment identified more than 650 Compute Engine virtual machines as potentially over-provisioned, with machine configurations significantly exceeding actual workload requirements. This created elevated recurring cloud costs and inefficient resource utilization across the environment.
The organization also lacked centralized visibility into infrastructure usage patterns, structured cloud cost governance, and scalable operational analytics capabilities to support continuous optimization efforts.
Non-production systems frequently remained active beyond required operating windows, further increasing unnecessary cloud expenditure. Monitoring and utilization analysis were largely manual, limiting the organization’s ability to proactively identify inefficiencies and make data-driven infrastructure decisions at scale.
Beyond immediate cost reduction, the university needed a more sustainable and operationally mature cloud environment capable of supporting future analytics, automation, and AI-driven initiatives.
YCOTEK conducted a detailed analysis of infrastructure utilization, workload behavior, and cloud expenditure across the organization’s Google Cloud environment.
Using historical performance and consumption data, the team developed a right-sizing strategy aligned with actual workload requirements rather than provisioned capacity. This enabled the organization to optimize machine configurations, reduce excess compute allocation, and improve infrastructure efficiency without impacting operational performance.
YCOTEK also designed a committed-use and discount optimization strategy to improve long-term cloud cost efficiency across production workloads.
To improve operational visibility and governance, the team developed a centralized Executive Cost Dashboard that provided leadership with real-time insights into infrastructure utilization, cloud expenditure, optimization opportunities, and projected savings.
Built entirely on Google Cloud Platform using Compute Engine and BigQuery, the resulting framework established a scalable foundation for continuous optimization, reporting, and cloud governance.
The engagement delivered significant financial and operational value.
Across an initial analysis of 168 virtual machines, the organization identified average monthly savings opportunities of approximately USD 43,227—equivalent to projected annual savings exceeding USD 518,000.
When extended across the broader estate of 650 virtual machines, projected savings increased to approximately USD 210,474 per month, representing an estimated annual savings opportunity of USD 2.52 million.
Beyond cost optimization, the university gained improved real-time visibility into infrastructure utilization, stronger governance capabilities, and more scalable reporting and operational analytics processes.
The engagement established a more efficient and sustainable cloud operating model while creating a stronger foundation for future data, automation, and AI-enabled operational initiatives.
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