4 Strategies to Optimize Cloud Investments and Maximize Value

Mayank Bhargava
VP Consulting Services and Cloud Modernization Practice Leader
CGI

As companies scale their cloud strategies, IT and finance leaders are looking to maximize both operation efficiency and return on investment. On their digital transformation journey, companies are migrating more workloads to the cloud, which can incur higher costs during the process due to the higher volume of cloud resources needed. However, it is also an opportunity to create more agile, resilient systems that will lower costs in the long term while increasing its business value.

Additionally, many organizations are increasing the number of advanced cloud-based services. This includes the addition of artificial intelligence, machine learning, data analytics, and similar technologies that demand more resources and have higher associated costs.

Still, when managed effectively, cloud and even multi-cloud strategies can offer significant savings compared to on-premise. However, organizations must be mindful of the challenges that can arise when embracing cloud and multi-cloud strategies, as unexpected costs can balloon quickly.

Organizations can employ several strategies to mitigate rising cloud costs. Implementing a robust cloud governance framework that includes ongoing cost optimization practices is key. Here are four critical components of a cloud governance framework that can help keep cloud costs under control.

1. Right-sizing resources

The first step is to right-size resources by matching the type and size of instances to the needs of the workload. In this process, tech leaders will examine the performance of the company's cloud instances, as well as analyze its cloud usage patterns and needs. By accumulating this data, the organization can determine if unused or underutilized services can be eliminated.

In addition to pure cost cutting, the right-sizing process enables businesses to fully understand their cloud environment and usage. This is especially true with regular analysis, allowing the organization to evolve with shifting priorities.

2. Utilize automation and real-time insights

Traditional methods of deploying cloud workloads require manual processes. This exhausts an IT team's time and creates opportunities for errors. Introducing automation increases the speed, security, and cost-efficiency of various tasks, including autoscaling. This occurs when a tool monitors and adjusts cloud usage to automatically remove or increase compute resources as needed.

Additionally, organizations can take a proactive approach by using cloud management services with real-time insights into usage, costs, and performance. These tools analyze vast amounts of data from cloud operations to provide insights into performance, cost trends, and resource utilization. Predictive analytics can forecast future cloud needs, enabling proactive capacity planning and budgeting. Detailed reporting features allow organizations to track key metrics and KPIs, providing a clear understanding of their cloud environment's health and performance in real-time.

3. Move to a serverless architecture

Moving to a serverless architecture also reduces costs by eliminating the need to manage and pay for always-on servers. In a serverless environment, you only pay for the exact compute resources used during the execution of your code. This approach is particularly beneficial for applications with variable workloads, as it automatically scales based on demand, ensuring that you only incur costs for what you use.

4. Create a cloud cost-aware culture

Finally, the role of culture cannot be underestimated. It is crucial to foster a culture of cloud cost awareness. From the top down, teams should be encouraged to continuously monitor and analyze their own cloud usage patterns. This way, those most familiar with a project's needs and processes can determine what can be streamlined with minimal ripple effects.

Companies that ingrain this into their organization may also consider creating a Cloud Cost Optimization Officer role if they don't already have one. This individual would lead efforts related to analyzing and strategizing cloud usage. Because they would have an overarching view of all cloud usage throughout the company, they would be able to spot opportunities for optimization that others may not have visibility into.

Looking ahead

These are all strategies IT leaders can and should implement today. It is also vital to keep an eye on the future to avoid falling behind. Cloud costs will continue to rise, particularly as cloud adoption deepens and businesses leverage more advanced and specialist cloud services. That doesn't necessarily mean that the cloud will become less cost-effective.

As cloud technologies become more widespread and mature, more cost-management tools and strategies will emerge, offering even more opportunities for organizations to optimize their spending more effectively. For example, artificial intelligence can play a crucial role in analyzing, forecasting, and scaling cloud usage. This can enable greater fine-tuning in automation and, ultimately, lowered costs.

Lastly, the pricing structures offered by various providers will continue to shift based on customers' priorities. There is already a trend toward increasingly granular pricing models. As providers cater to their clients' industry-specific needs and sustainability goals, new and increasingly competitive pricing models may emerge for enterprises to take advantage of and further reduce their cloud spend.

Mayank Bhargava is VP Consulting Services and Cloud Modernization Practice Leader at CGI

Related Links

Hot Topics

The Latest

The Performance Bottleneck in Enterprise AI Has Been Exposed – Now We Must Fix It

Performance bottlenecks aren't uncommon when it comes to rolling out new technology, regardless of how capable or game-changing that technology might be. Every generation of new tech has encountered roadblocks that had to be overcome before it was truly able to shine. Virtualization forced organizations to rethink resource allocation, cloud transformation had us shift our focus toward scalability and elasticity, and microservices introduced entirely new challenges around observability and distributed systems. There's something different about AI, however ...

What Capital Markets Can Teach Enterprises About Integrated Data Infrastructure

Consider a single order represented across order-management, execution, and settlement systems. Each database, message broker, and application may be online and processing its own records correctly. Yet the workflow has failed if related events arrive on different clocks, rely on inconsistent state, or cannot be reconciled before an operational decision must be made ...

Why AI in IT Is Producing High Productivity but Heavy Workloads

AI now exists in almost every IT workflow. In a recent survey of more than 800 IT service professionals, all respondents indicated the use of AI in some form within their organization. But there's a growing paradox: if dashboards are clearing faster and alerts are resolved at unprecedented speed, why aren't IT service desks reporting lighter workloads? The research found that 71% of IT teams said their actual workload has remained flat or increased since adopting AI. This reality appears to contradict what we’ve been told about AI ...

Implementing AI Isn't the Hard Part Anymore - Adoption Is

Two years ago, almost every customer conversation about AI started with the same questions: Which model should we use? What can it do? Is it ready for the enterprise? Today, those discussions have moved on. CIOs are far more interested in how to govern AI, integrate it with existing systems, prepare their workforce and make it part of everyday operations. The challenge is no longer to prove that AI can deliver value. It's instead about how to embed AI into the business in a way that's secure, scalable and delivers measurable outcomes ...

The Incident Load Is Rising, but Recovery Times Aren't Moving

Two things happened to production incidents between 2023 and now, and they did not happen at the same speed. The first is that a class of dependency that barely existed three years ago now accounts for one incident in ten. Incidents disclosed by AI model and AI application providers rose from 1.7% of all disclosed unplanned incidents in 2023 to 10.7% in 2026 year to date, roughly a sixfold rise; that counts only incidents at AI companies themselves, so the true share is higher. The second is that the time to close an incident has not come down ...

Your AI Pipeline May Have a Document Observability Problem

When an AI assistant gives an incomplete or incorrect answer, teams often blame the model. They adjust prompts, switch models, increase context windows or test a new retrieval strategy. However the model may not be a problem. In many enterprise AI workflows, the problem begins inside the document-ingestion pipeline ...

The SIEM Isn't the Problem. Your Telemetry Architecture Is

If you talk to any security or observability teams right now, they're all fighting the same fire: their tooling was built to ingest X, but their sources are pumping Y and soon to be doing Z. The knee-jerk reaction is always the same: we need more platform. However, this reaction is wrong. Let me explain why, because the solution to this problem is foundational, not financial. Instead of hurling yet more money at the problem, make sure you've done what's needed upstream ...

As AI Scales Across Enterprises, Breaking Points Emerge

Rapid AI adoption and the unique ways AI workloads operate is redefining the scope and structure of what these teams must deliver. This shift is forcing organizations to rethink how they manage scale, automation, and control, according to The State of SRE and Platform Engineering 2026, a new report from Dynatrace ...

Preparing Network Infrastructure for AI Starts with the Basics

AI is usually talked about as a software tool, but it also depends heavily on the network behind it. Whether a company is using AI for chatbots, automation, monitoring, analytics, or employee support, all of that information has to move across the network in a reliable and secure way. That means AI is not just an application decision. It is also an infrastructure decision. Before organizations rush into AI, they should ask a simple question: Is our network ready to support it? ...

Agentic Operations Begin with Governed Middleware Access

Enterprise AI often lacks governed access to where business processes actually execute. Without that access, AI agents may be able to reason, but they cannot operate reliably across enterprise workflows. For AI agents to effectively carry out workflows, they will require integration-layer context and controls. Organizations can implement these prerequisites by providing AI with managed access to the middleware layer ...

4 Strategies to Optimize Cloud Investments and Maximize Value

Mayank Bhargava
VP Consulting Services and Cloud Modernization Practice Leader
CGI

As companies scale their cloud strategies, IT and finance leaders are looking to maximize both operation efficiency and return on investment. On their digital transformation journey, companies are migrating more workloads to the cloud, which can incur higher costs during the process due to the higher volume of cloud resources needed. However, it is also an opportunity to create more agile, resilient systems that will lower costs in the long term while increasing its business value.

Additionally, many organizations are increasing the number of advanced cloud-based services. This includes the addition of artificial intelligence, machine learning, data analytics, and similar technologies that demand more resources and have higher associated costs.

Still, when managed effectively, cloud and even multi-cloud strategies can offer significant savings compared to on-premise. However, organizations must be mindful of the challenges that can arise when embracing cloud and multi-cloud strategies, as unexpected costs can balloon quickly.

Organizations can employ several strategies to mitigate rising cloud costs. Implementing a robust cloud governance framework that includes ongoing cost optimization practices is key. Here are four critical components of a cloud governance framework that can help keep cloud costs under control.

1. Right-sizing resources

The first step is to right-size resources by matching the type and size of instances to the needs of the workload. In this process, tech leaders will examine the performance of the company's cloud instances, as well as analyze its cloud usage patterns and needs. By accumulating this data, the organization can determine if unused or underutilized services can be eliminated.

In addition to pure cost cutting, the right-sizing process enables businesses to fully understand their cloud environment and usage. This is especially true with regular analysis, allowing the organization to evolve with shifting priorities.

2. Utilize automation and real-time insights

Traditional methods of deploying cloud workloads require manual processes. This exhausts an IT team's time and creates opportunities for errors. Introducing automation increases the speed, security, and cost-efficiency of various tasks, including autoscaling. This occurs when a tool monitors and adjusts cloud usage to automatically remove or increase compute resources as needed.

Additionally, organizations can take a proactive approach by using cloud management services with real-time insights into usage, costs, and performance. These tools analyze vast amounts of data from cloud operations to provide insights into performance, cost trends, and resource utilization. Predictive analytics can forecast future cloud needs, enabling proactive capacity planning and budgeting. Detailed reporting features allow organizations to track key metrics and KPIs, providing a clear understanding of their cloud environment's health and performance in real-time.

3. Move to a serverless architecture

Moving to a serverless architecture also reduces costs by eliminating the need to manage and pay for always-on servers. In a serverless environment, you only pay for the exact compute resources used during the execution of your code. This approach is particularly beneficial for applications with variable workloads, as it automatically scales based on demand, ensuring that you only incur costs for what you use.

4. Create a cloud cost-aware culture

Finally, the role of culture cannot be underestimated. It is crucial to foster a culture of cloud cost awareness. From the top down, teams should be encouraged to continuously monitor and analyze their own cloud usage patterns. This way, those most familiar with a project's needs and processes can determine what can be streamlined with minimal ripple effects.

Companies that ingrain this into their organization may also consider creating a Cloud Cost Optimization Officer role if they don't already have one. This individual would lead efforts related to analyzing and strategizing cloud usage. Because they would have an overarching view of all cloud usage throughout the company, they would be able to spot opportunities for optimization that others may not have visibility into.

Looking ahead

These are all strategies IT leaders can and should implement today. It is also vital to keep an eye on the future to avoid falling behind. Cloud costs will continue to rise, particularly as cloud adoption deepens and businesses leverage more advanced and specialist cloud services. That doesn't necessarily mean that the cloud will become less cost-effective.

As cloud technologies become more widespread and mature, more cost-management tools and strategies will emerge, offering even more opportunities for organizations to optimize their spending more effectively. For example, artificial intelligence can play a crucial role in analyzing, forecasting, and scaling cloud usage. This can enable greater fine-tuning in automation and, ultimately, lowered costs.

Lastly, the pricing structures offered by various providers will continue to shift based on customers' priorities. There is already a trend toward increasingly granular pricing models. As providers cater to their clients' industry-specific needs and sustainability goals, new and increasingly competitive pricing models may emerge for enterprises to take advantage of and further reduce their cloud spend.

Mayank Bhargava is VP Consulting Services and Cloud Modernization Practice Leader at CGI

Related Links

Hot Topics

The Latest

The Performance Bottleneck in Enterprise AI Has Been Exposed – Now We Must Fix It

Performance bottlenecks aren't uncommon when it comes to rolling out new technology, regardless of how capable or game-changing that technology might be. Every generation of new tech has encountered roadblocks that had to be overcome before it was truly able to shine. Virtualization forced organizations to rethink resource allocation, cloud transformation had us shift our focus toward scalability and elasticity, and microservices introduced entirely new challenges around observability and distributed systems. There's something different about AI, however ...

What Capital Markets Can Teach Enterprises About Integrated Data Infrastructure

Consider a single order represented across order-management, execution, and settlement systems. Each database, message broker, and application may be online and processing its own records correctly. Yet the workflow has failed if related events arrive on different clocks, rely on inconsistent state, or cannot be reconciled before an operational decision must be made ...

Why AI in IT Is Producing High Productivity but Heavy Workloads

AI now exists in almost every IT workflow. In a recent survey of more than 800 IT service professionals, all respondents indicated the use of AI in some form within their organization. But there's a growing paradox: if dashboards are clearing faster and alerts are resolved at unprecedented speed, why aren't IT service desks reporting lighter workloads? The research found that 71% of IT teams said their actual workload has remained flat or increased since adopting AI. This reality appears to contradict what we’ve been told about AI ...

Implementing AI Isn't the Hard Part Anymore - Adoption Is

Two years ago, almost every customer conversation about AI started with the same questions: Which model should we use? What can it do? Is it ready for the enterprise? Today, those discussions have moved on. CIOs are far more interested in how to govern AI, integrate it with existing systems, prepare their workforce and make it part of everyday operations. The challenge is no longer to prove that AI can deliver value. It's instead about how to embed AI into the business in a way that's secure, scalable and delivers measurable outcomes ...

The Incident Load Is Rising, but Recovery Times Aren't Moving

Two things happened to production incidents between 2023 and now, and they did not happen at the same speed. The first is that a class of dependency that barely existed three years ago now accounts for one incident in ten. Incidents disclosed by AI model and AI application providers rose from 1.7% of all disclosed unplanned incidents in 2023 to 10.7% in 2026 year to date, roughly a sixfold rise; that counts only incidents at AI companies themselves, so the true share is higher. The second is that the time to close an incident has not come down ...

Your AI Pipeline May Have a Document Observability Problem

When an AI assistant gives an incomplete or incorrect answer, teams often blame the model. They adjust prompts, switch models, increase context windows or test a new retrieval strategy. However the model may not be a problem. In many enterprise AI workflows, the problem begins inside the document-ingestion pipeline ...

The SIEM Isn't the Problem. Your Telemetry Architecture Is

If you talk to any security or observability teams right now, they're all fighting the same fire: their tooling was built to ingest X, but their sources are pumping Y and soon to be doing Z. The knee-jerk reaction is always the same: we need more platform. However, this reaction is wrong. Let me explain why, because the solution to this problem is foundational, not financial. Instead of hurling yet more money at the problem, make sure you've done what's needed upstream ...

As AI Scales Across Enterprises, Breaking Points Emerge

Rapid AI adoption and the unique ways AI workloads operate is redefining the scope and structure of what these teams must deliver. This shift is forcing organizations to rethink how they manage scale, automation, and control, according to The State of SRE and Platform Engineering 2026, a new report from Dynatrace ...

Preparing Network Infrastructure for AI Starts with the Basics

AI is usually talked about as a software tool, but it also depends heavily on the network behind it. Whether a company is using AI for chatbots, automation, monitoring, analytics, or employee support, all of that information has to move across the network in a reliable and secure way. That means AI is not just an application decision. It is also an infrastructure decision. Before organizations rush into AI, they should ask a simple question: Is our network ready to support it? ...

Agentic Operations Begin with Governed Middleware Access

Enterprise AI often lacks governed access to where business processes actually execute. Without that access, AI agents may be able to reason, but they cannot operate reliably across enterprise workflows. For AI agents to effectively carry out workflows, they will require integration-layer context and controls. Organizations can implement these prerequisites by providing AI with managed access to the middleware layer ...