How we helped a global TSP optimize multicloud governance and save costs | HCLTech

How we helped a global TSP optimize multicloud governance and save costs

Driving cloud efficiency through an innovative recommendation engine
5 min read
5 min read

About the Client

Our client is one of the leading global providers of communications services (voice, data, video) and technology offerings and solutions on their networks and platforms to meet customer demand for mobility, reliable network connectivity, security and control. They engaged with HCLTech to develop a cloud recommendation engine, enhance cloud optimization and drive cost savings.






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The Challenge

Managing the cost across cloud ecosystems

Our client faced critical challenges around cost and governance. Manual tracking and resource management had become increasingly challenging and there were differing opinions regarding cost visibility. The current usage of GCP for big data/analytics workloads resulted in costs exceeding the budget by 2-4X. Cloud native controls were unable to address most of the over-budget problems in the enterprise due to a lack of internal governance and control.


The Objective

Creating a GCP recommendation engine for efficient cloud cost optimization and governance

Our client needed a comprehensive solution for cloud governance and cost optimization, designed to tackle the intricacies of tracking, managing and optimizing expenditures within multiple cloud ecosystems. The solution had to enhance visibility into cloud costs, automate cost-saving initiatives and mitigate potential pitfalls tied to manual tracking and resource management.

The key objectives included:

  • Develop a cloud engine that automated the removal of costs incurred due to redundant and underutilized cloud resources. This engine was intended to enhance the utilization and provide recommendations for GCP cloud services used by the applications in our client’s cloud environment.
  • Automate tasks such as applying rules and policies, implementing budget constraints during resource deployment and conducting validation and security checks. The resources involved include BigQuery, DataProc, Persistent Disks within Dataproc and GCS.
  • Perform resizing of underutilized resources and the deletion of unused resources.

Our Approach and Solution

To develop GCP cloud optimization automation engine for tracking cloud services and streamline resource utilization.

HCLTech implemented a solution to address the primary causes of inflated cloud bills and suboptimal capacity utilization. The solution assisted in implementing cloud native controls and governance, thereby resolving critical issues related to costs exceeding the budget.

This solution was designed to be extended to other cloud platforms. This was achieved with a functional and technical solution approach as indicated below:

Functional solution approach:


Technical solution approach:

Our Approach and Solution

The Impact

Optimized cloud resource utilization resulting in enhanced cost savings

The solution and its recommendations helped achieve complete real-time visibility into the client’s cloud infrastructure and expenditure. The outcomes delivered by this solution are:

  • Reduced cloud service costs by 25%
  • Saved approximately $5M annually on Dataproc
  • Achieved similar savings for Google Cloud Storage and BigQuery
  • Enhanced governance control implementation for GCP Cloud
  • Centralized CI/CD pipeline for using Terraform, security linting, rule-based engine, budget control and cost estimation of TF IAC template prior to launch infrastructure in the GCP cloud
  • Enabled real-time monitoring and automated cost management during resource deployment
  • Accelerated extension and deployment of cloud services
  • Effectively managed complex resources and costs, ensuring continuous optimization and precise budget allocation while eliminating wasteful expenditures