According to "The CIO Cloud Trends Survey & Report" by Azul Systems, 83% of CIOs surveyed reported that their actual infrastructure and application cloud spend was more than their anticipated cloud spend. Of that percentage, 60% said spending was over by at least 26%.

In general, organizations lack real-time visibility into cloud spending due to the complexity of cloud deployments and the rampant provisioning across infrastructures, often with little oversight. Manual interventions to control costs, from analyzing usage patterns to filtering telemetry data, are no longer sufficient. Instead, IT leaders are adopting predictive AI and ML to continuously learn from historical data, forecast resource demands and reduce cloud costs.

With AI and ML technologies, enterprises can enhance resource demand forecasting to ensure more accurate predictions and better manage rampant cloud spending.

Why traditional cost optimization tools are failing enterprises

These cloud spend issues are not going unnoticed by business leaders. Azul found that 43% of CIOs stated that their organization's CEO and/or board of directors have concerns about their cloud spend.

Traditionally, cost optimization tools for cloud services have focused on monitoring system-level metrics, including CPU usage, memory consumption, and network traffic. They rely on static rules for passing CPU and storage usage thresholds, tracking error rates and other fixed metrics. These single-use tools are not only ineffective for measuring today's cloud resource demand complexity, but they also offer limited visibility into consumption, making it difficult to identify cost drivers. Manual interventions to recalibrate cloud usage and reduce costs are also inefficient and only add to the IT burden.

Rapid updates and deployments often outpace IT leaders' ability to control cloud service fees and manage the dramatic fluctuations in resource demands.

Along with running multiple cloud services, enterprises increasingly deploy cloud-native software. These high-functioning applications rely on fast releases and accelerated continuous integration and continuous delivery (CI/CD) pipelines. Rapid updates and deployments often outpace IT leaders' ability to control cloud service fees and manage the dramatic fluctuations in resource demands. This lack of analysis for application-level performance also extends to an inability to accurately predict future resource needs, leading to additional cost inefficiencies.

A similar challenge arises when optimizing expenses and ensuring consistent high availability (HA) for cloud workloads. Enterprises end up paying for the discrepancies between inaccurate forecasts, which can result in resource shortages, excess resource allocation or idle services. Organizations that use traditional cloud monitoring tools also encounter challenges related to the complexity of cloud pricing. Some of these issues include overlooking data transfer costs and failing to account for data transfer fees between regions and services.

Concerns around costs and efficient service monitoring also include compliance and regulatory practices. Organizations that lack granular cloud awareness risk security and data privacy vulnerabilities that result in mitigation expenses and costly penalties. Each of these limitations highlights the potential problems associated with human error and outdated tools.

By contrast, dynamic AI approaches automatically adjust cloud resource allocations and automate cloud cost management strategies to help reduce expenses.

How AI boosts cloud cost optimization

Increasingly, the path to greater operational flexibility and improved cloud spend is based on the analytical power of AI and machine learning (ML). As organizations adopt AI-driven tools, they gain:

• Improved cloud usage.
• Elimination of idle resources.
• Cost savings through rightsizing instances.
• Ability to calculate future cloud spending based on predicted growth.

For example, organizations can use predictive AI to compare current cloud usage data with established operational baselines, identifying cost irregularities in real time.

The quality and accuracy of cloud usage data drive the capabilities of AI for adjusting resource allocation and predicting costs. Machine learning models proactively and independently correct resource allocations related to over-provisioning or underutilization using detailed cloud information, such as CPU usage, memory allocations and network traffic.

In terms of improving IT operations, AI can recommend the most cost-effective instance type, such as general, spot, compute and storage-intensive, based on the service provider. And in addition to detecting cost anomalies, AI tools can generate budget alerts using predefined spending thresholds.

How AI-powered FinOps delivers business value

FinOps provides an operational framework for setting cloud spending policies and plays a crucial role in strategizing investments and promoting cloud cost awareness throughout an organization. Offering a broad approach to managing cloud finances and ensuring business value, FinOps establishes collaborative accountability between engineering, finance, operations and product teams.

A comprehensive FinOps approach provides the needed strategy, financial accountability and granular insights to direct AI-driven cost controls.

However, it's not only a methodology for reining in cloud costs. For example, AI-powered FinOps platforms offer one option for automated optimization, predictive cost forecasting and intelligent resource labeling in multi-cloud environments. And while enterprises can use AI alone to automate system-level cost controls, a comprehensive FinOps approach provides the needed strategy, financial accountability and granular insights to direct AI-driven cost controls.

The introduction of Agentic AI and the use of SaaS represent the next stage in the evolution of intelligent cloud cost optimization. In this approach, AI agents autonomously identify, recommend and implement cost optimizations on an ongoing basis. This framework combines these innovations to achieve two key objectives:

1. Control costs.
2. Maximize business value.

It represents an intelligence-fueled, strategic approach to accurately predicting demand, dynamically responding to cloud usage and achieving substantial cost savings.

Kerry Doyle writes about technology for a variety of publications and platforms. His current focus is on issues relevant to IT and enterprise leaders across a range of topics, from nanotech and cloud to distributed services and AI.