Cloud costs continue to increase dramatically, creating headaches for organizations of all sizes. According to the Flexera 2025 State of the Cloud Report, 72 percent of organizations have made it their top priority to optimize their existing use of the cloud. The objective: to achieve cost savings.
It isn’t just a matter of bottom-line costs. Organizations often lack a clear understanding of where their money is being spent, leading to waste. Much of the waste comes from underutilized or unused resources, such as old testing environments or idle virtual machines. Unchecked growth and the rush to adopt new technologies can lead to escalating costs if not properly managed.
AI and machine learning are redefining cloud cost management by automating analysis, optimizing resource allocation and predicting future spending. This proactive, data-driven approach helps organizations overcome the limitations of traditional cost management, addressing issues such as resource waste, inaccurate forecasts and complex pricing models.
Why Cloud Costs Are Skyrocketing
Lack of visibility is a primary cause of cloud waste and overspending. Many organizations are unsure why their cloud costs are rising, and cannot allocate them to specific business units, departments or budgets. The complexity of cloud services, especially with multi-cloud strategies, can make it difficult to track and manage costs effectively.
As a result, IT leaders are unable to measure the ROI of their cloud spend, making it hard to justify costs. According to research from Akamai, 68 percent of organizations have cut spending on cybersecurity, AI and other initiatives due to rising cloud expenses.
Many organizations are adopting FinOps practices to get a better handle on cloud costs. FinOps, short for “Finance” and “DevOps,” is a cultural and operational framework for bringing financial accountability to the cloud. A large percentage of organizations now have formal FinOps teams. The goal is to maximize the business value of cloud investments by reducing waste, managing commitment-based discounts and improving forecasting.
FinOps: Opportunities and Challenges
FinOps includes processes for identifying and optimizing resources, pricing and architecture to ensure the most cost-effective cloud usage. It holds teams accountable for their cloud usage and costs, ensuring a better understanding of where money is being spent. It relies on data and analytics to make timely, informed decisions about cloud resources and spending patterns.
However, FinOps is a framework, not a technology, and many organizations still grapple with low visibility, inaccurate forecasting and the complexity of cloud pricing. FinOps teams often find themselves drowning in data, making it hard to identify what’s relevant for decision-making.
AI can help relieve the burden. AI-powered tools can analyze historical usage patterns, seasonal trends and other factors to predict future cloud resource requirements. This enables organizations to accurately forecast peak usage so they can allocate the right amount of resources. AI can also recommend the most cost-effective long-term commitment options by analyzing stable workload patterns.
How AI Helps Optimize Cloud Costs
AI-powered tools continuously monitor resource utilization and performance metrics in real time. While traditional auto-scaling uses fixed rules, AI can proactively adjust resources based on predicted demand spikes and drops. AI can also identify over-provisioned instances and detect and shut down idle or unused resources.
For organizations using multiple cloud providers, AI can intelligently optimize costs and performance across different environments. It can distribute workloads across various cloud regions or providers based on factors such as cost, latency and performance. It can also intelligently balance compute resources and network usage to achieve cost-efficiency without compromising performance.
AI algorithms can quickly detect unusual spending patterns that manual monitoring might miss. This allows IT teams to address abnormal or excessive resource usage and prevent cloud bill shock.
AI facilitates FinOps by automating routine, time-consuming tasks. Many AI-powered tools allow for one-click approval of cost-saving recommendations, instantly modifying cloud services to apply optimizations. AI also provides granular, real-time insights into spending, automatically attributing costs to specific teams, projects or applications to increase accountability.
How Cerium Can Help
The Cerium team is here to help you utilize AI-powered tools to understand cloud usage and optimize costs. We can also help you fine-tune configurations, leverage reserved instances and use other techniques to reduce expenses without impacting performance. Contact one of our cloud specialists to schedule a no-obligation consultation.
