Skip to main content

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

Hot Topics

The Latest

Edge AI is strategically embedded in core IT and infrastructure spending across industries, according to the 2026 Edge AI Survey from ZEDEDA. The research shows that 83% of C-suite and IT executive respondents say edge AI is important to their core business strategy ...

As AI adoption accelerates, operational complexity — not model intelligence — is becoming the primary barrier to reliable AI at scale, according to the State of AI Engineering 2026 from Datadog ... The report highlights a compounding complexity challenge as AI systems scale ... Around 5% of AI model requests fail in production, with nearly 60% of those failures caused by capacity limits ...

For years, production operations teams have treated alert fatigue as a quality-of-life problem: something that makes on-call rotations miserable but isn't considered a direct contributor to outages. That framing doesn't capture how these systems fail, and we now have data to show why. More importantly, it's now clear alert fatigue is a symptom of a deeper issue: production systems have outgrown the current operational approaches ...

I was on a customer call last fall when an enterprise architect said something I haven't been able to shake. Her team had just spent four months trying to swap one AI vendor for another. The original plan said three weeks. "We didn't switch vendors," she told me. "We rebuilt half our integrations and discovered what we'd actually been depending on." Most enterprise leaders don't expect that to be the experience ...

Ask any senior SRE or platform engineer what keeps them up at night, and the answer probably isn't the monitoring tool — it's the data feeding it. The proliferation of APM, observability, and AIOps platforms has created a telemetry sprawl problem that most teams manage reactively rather than architect proactively. Metrics are going to one platform. Traces routed somewhere else. Logs duplicated across multiple backends because nobody wants to be caught without them when something breaks. Every redundant stream costs money ...

80% of respondents agree that the IT role is shifting from operators to orchestrators, according to the 2026 IT Trends Report: The Human Side of Autonomous IT from SolarWinds ...

40% of organizations deploying AI will implement dedicated AI observability tools by 2028 to monitor model performance, bias and outputs, according to Gartner ...

Until AI-powered engineering tools have live visibility of how code behaves at runtime, they cannot be trusted to autonomously ensure reliable systems, according to the State of AI-Powered Engineering Report 2026 report from Lightrun. The report reveals that a major volume of manual work is required when AI-generated code is deployed: 43% of AI-generated code requires manual debugging in production, even after passing QA or staging tests. Furthermore, an average of three manual redeploy cycles are required to verify a single AI-suggested code fix in production ...

Many organizations describe AI as strategic, but they do not manage it strategically. When AI plans are disconnected from strategy, detached from organizational learning, and protected from serious assumptions testing, the problem is no longer technical immaturity; it is a failure of management discipline ... Executives too often tell organizations to "use AI" before they define what AI is supposed to change. The problem deepens in organizations where strategy isn't well articulated in the first place ...

Across the enterprise technology landscape, a quiet crisis is playing out. Organizations have run hundreds, sometimes thousands, of generative AI pilots. Leadership has celebrated the proof of concept (POCs) ... Industry experience points to a sobering reality: only 5-10% of AI POCs that progress to the pilot stage successfully reach scaled production. The remaining 90% fail because the enterprise environment around them was never ready to absorb them, not the AI models ...

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

Hot Topics

The Latest

Edge AI is strategically embedded in core IT and infrastructure spending across industries, according to the 2026 Edge AI Survey from ZEDEDA. The research shows that 83% of C-suite and IT executive respondents say edge AI is important to their core business strategy ...

As AI adoption accelerates, operational complexity — not model intelligence — is becoming the primary barrier to reliable AI at scale, according to the State of AI Engineering 2026 from Datadog ... The report highlights a compounding complexity challenge as AI systems scale ... Around 5% of AI model requests fail in production, with nearly 60% of those failures caused by capacity limits ...

For years, production operations teams have treated alert fatigue as a quality-of-life problem: something that makes on-call rotations miserable but isn't considered a direct contributor to outages. That framing doesn't capture how these systems fail, and we now have data to show why. More importantly, it's now clear alert fatigue is a symptom of a deeper issue: production systems have outgrown the current operational approaches ...

I was on a customer call last fall when an enterprise architect said something I haven't been able to shake. Her team had just spent four months trying to swap one AI vendor for another. The original plan said three weeks. "We didn't switch vendors," she told me. "We rebuilt half our integrations and discovered what we'd actually been depending on." Most enterprise leaders don't expect that to be the experience ...

Ask any senior SRE or platform engineer what keeps them up at night, and the answer probably isn't the monitoring tool — it's the data feeding it. The proliferation of APM, observability, and AIOps platforms has created a telemetry sprawl problem that most teams manage reactively rather than architect proactively. Metrics are going to one platform. Traces routed somewhere else. Logs duplicated across multiple backends because nobody wants to be caught without them when something breaks. Every redundant stream costs money ...

80% of respondents agree that the IT role is shifting from operators to orchestrators, according to the 2026 IT Trends Report: The Human Side of Autonomous IT from SolarWinds ...

40% of organizations deploying AI will implement dedicated AI observability tools by 2028 to monitor model performance, bias and outputs, according to Gartner ...

Until AI-powered engineering tools have live visibility of how code behaves at runtime, they cannot be trusted to autonomously ensure reliable systems, according to the State of AI-Powered Engineering Report 2026 report from Lightrun. The report reveals that a major volume of manual work is required when AI-generated code is deployed: 43% of AI-generated code requires manual debugging in production, even after passing QA or staging tests. Furthermore, an average of three manual redeploy cycles are required to verify a single AI-suggested code fix in production ...

Many organizations describe AI as strategic, but they do not manage it strategically. When AI plans are disconnected from strategy, detached from organizational learning, and protected from serious assumptions testing, the problem is no longer technical immaturity; it is a failure of management discipline ... Executives too often tell organizations to "use AI" before they define what AI is supposed to change. The problem deepens in organizations where strategy isn't well articulated in the first place ...

Across the enterprise technology landscape, a quiet crisis is playing out. Organizations have run hundreds, sometimes thousands, of generative AI pilots. Leadership has celebrated the proof of concept (POCs) ... Industry experience points to a sobering reality: only 5-10% of AI POCs that progress to the pilot stage successfully reach scaled production. The remaining 90% fail because the enterprise environment around them was never ready to absorb them, not the AI models ...