Navigating Cloud Spending: How AI Enhances FinOps

"Moving to the cloud a few years ago, we soon realized we needed to rein in our cloud spending across hundreds of accounts," says Zach Nimboorkar, Senior VP of Global Technology Services at Schneider Electric SE. His experience echoes a growing sentiment among business leaders contending with the complexities and expenses of cloud computing. As companies eagerly shift to the cloud, attracted by scalability and innovation, a Gartner report forecasts the global cloud market to reach $678.8 billion in 2024. The CIO of Goldman Sachs, Marco Argenti, commented on a podcast about how the future of financial services is on the cloud. He went on to say that they see the cloud as an accelerator to increase productivity. This is why many companies are drawn to migrating to the cloud.

However, they face the daunting task of managing spiraling cloud costs. According to a survey, nearly 70% of companies acknowledge they are paying for cloud capacity they do not use. Also, more than 40% of companies said they utilize less than 60% of the public cloud computing resources they are paying for.

Traditional financial operations (FinOps) methods must keep up with the cloud's rapid growth and unique challenges. Standard financial management practices must be more flexible and faster, missing crucial opportunities to optimize costs in an environment that changes by the minute.

This is where artificial intelligence (AI) can make a significant impact. Research shows that when companies integrate AI into their FinOps practices, they're 53% more likely to see cost savings of over 20%. 

AI can analyze data about cloud resource consumption and predict future cloud spending based on trends. It can also adjust cloud resources on the fly to match actual needs, thereby reducing unnecessary waste and over-provisioning. The shift towards an AI-driven approach in FinOps enables more innovative, accurate, and proactive cloud cost optimization. This article explores how integrating AI into FinOps changes financial management and operational efficiency, particularly in cloud cost optimization.

Cloud Cost Predictions: Prepare for a 29% Increase

In 2023, many companies invested in cloud computing, making up more than 60% of all spending on IT infrastructure worldwide. 

Technically speaking, it’s a great step forward, but it’s also created a new challenge of managing and keeping cloud costs under control. A report found that companies often find their cloud spending overshooting budgets by an average of 13%, with an expected rise in cloud costs by 29% in the near future. More alarmingly, businesses estimate that about 32% of their cloud spend will be wasted.

The struggle with manging cloud finances, or FinOps, often comes down to outdated methods like spreadsheets and basic cost models. These manual techniques could be faster and prone to mistakes. They need to scale up better as cloud usage grows. As the cloud gets more complex, these old-school methods must catch up, leading to budget overruns and inefficiencies.

In addition, humans are also a challenge related to manual FinOps. As cloud technology advances quickly, a team that can stay up-to-date with the latest cloud tech is needed. The pace at which the cloud environment evolves means that even experts might need to learn and adapt to keep up constantly. This is where the need for an automated, advanced FinOps solution develops.

AI and FinOps: A Partnership for Financial Excellence

AI can be applied to automate complex tasks, speed up data analysis, and bring in-depth insights that manual processes can't match. Using machine learning, AI can sift through huge volumes of cloud usage and spending data, spotting trends and anomalies that might go unnoticed. Using AI for FinOps leads to more accurate forecasts and uncovers ways to cut costs, like identifying underused resources. Businesses can manage their cloud expenses more effectively by making more intelligent decisions based on real-time data and strategic financial planning.

Let’s look at a few examples of how AI can be applied to FinOps.

Unlock Cost Savings: How Cloud Anomaly Detection Works

Cloud cost anomaly detection helps analyze cloud usage and spending data through AI algorithms and machine learning models to identify irregularities. By ingesting data related to historical usage trends, real-time resource consumption, and billing information, the AI models can understand what data patterns represent normal operations for an organization. These models can alert organizations to potential issues in real-time. Companies can then take proactive measures like adjusting resources or investigating unauthorized usage. They can more easily prevent unwanted expenses and optimize cloud expenditures.

Virtasant offers various cloud solutions. One of its services is the ability to analyze cloud data deeply to pinpoint cost anomalies and operational inefficiencies. Virtasant customers can proactively manage their cloud infrastructure and reduce costs by over 50%. 

Michael Kearns, the CEO of Virtasant, states that to operate at scale, there’s no way humanly possible to be cost-effective without automating both the discovery of issues and creating resolutions to optimize cloud resource allocation. Organizations must go beyond merely cutting cloud costs and improve their cloud usage's effectiveness.

H&R Block's Success: AI for Efficient Cloud Utilization

Rightsizing resources is an everyday use case of AI in FinOps because 94% of companies have experienced avoidable cloud expenses due to underused and overprovisioned resources. Rightsizing resources involves analyzing cloud usage patterns and resource demand in real time. AI algorithms sift through vast amounts of data to pinpoint where resources are underutilized or overprovisioned. AI tools can detect when a server runs at low capacity for extended periods, indicating that resources can be scaled down without impacting performance.

For example, H&R Block, a tax preparation company, overcame the common problem of spending on underutilized infrastructure by rightsizing its cloud resources. Initially, they had to ensure their on-premises servers could handle the influx of users during tax season, resulting in wasted capacity during off-peak periods. By right-sizing resources, they could scale their resources according to actual demand, effectively reducing costs. 

Anticipate Cloud Needs with AI-Powered Reserved Instances

Reserved instance optimization is a strategic approach to reducing cloud computing costs by purchasing reserved instances (RIs) from cloud providers. These providers offer discounted rates in exchange for a commitment to use a specific computing capacity over a set period. AI plays a vital role in this process by analyzing historical cloud usage data to predict future needs. AI can ensure that organizations purchase the right amount and type of RIs to match their usage patterns closely. 

Bernard Golden, Capital One's director of cloud strategy, recommends using reserved instances as one of the industry's four best practices for managing cloud costs. Integrating AI into this key strategy makes it even more reliable to cut costs by only buying the computing power you’ve planned for at a lower cost ahead of time. In line with this, a forecast by Everest Group showed that the adoption of savings plans and reserved instance automation is projected to see an 80% rise over the coming three years.

Integrating AI Tools into Your FinOps Strategy

Implementing AI for FinOps can help you and your company stay ahead of the curve in cloud cost management. According to a 2022 survey, 37.1% of cloud FinOps practitioners were still in the "crawl" stage, grappling with the basics. Embrace AI-powered solutions to manage cloud costs efficiently and stay ahead of your competition.

Starting your journey with AI-powered FinOps can be manageable. Here’s how businesses can get started: 

• Step 1 - Assess Your Current Cloud Setup: Begin by thoroughly analyzing how your cloud services are currently being used and where your money is going. Look for inefficiencies, such as underused resources, that could be optimized for cost savings.
• Step 2 - Set Clear AI FinOps Goals: Determine precisely what you hope to achieve by incorporating AI into your FinOps practices. Whether it’s cutting cloud costs by a specific figure, achieving greater efficiency in resource utilization, or automating financial operations, having clear targets is crucial.
• Step 3 - Choose the Right AI Tool: With a variety of AI FinOps solutions available, it's important to select one that fits well with your specific needs. Consider factors like compatibility with your cloud providers, the ability to integrate with your current tech stack, and the tool’s capacity to meet your defined goals.
• Step 4 - Plan for Integration: Carefully plan how the AI tool will be integrated into your operations. This step is essential. It ensures that your system and AI tool are technically compatible. You’ll need to prepare for data migration if necessary and consider any adjustments to workflows that might be required.
• Step 5 - Manage Change Among Your Team: Introducing new AI tools will likely change how your team works. Manage this change by communicating its benefits, providing necessary training, and supporting your team through the transition. Everyone needs to be on board and able to use the new tools effectively.
• Step 6 - Monitor Performance and Adjust as Needed: After implementation, monitor how the AI tool performs against your objectives. Be prepared to adjust your FinOps strategy based on real-world performance and evolving business needs. You may need to fine-tune your AI tool’s settings, revise your cloud usage practices, or set new financial goals.

AI & Humans: A Dynamic Duo for FinOps Success

Regarding FinOps, AI isn't here to replace humans but to make our jobs easier. AI brings robust data analysis and predictive modeling capabilities, but human expertise remains crucial for interpreting insights, setting strategic goals, and making informed decisions. Together, we make a formidable team to keep improving how we use cloud resources.

Continuous improvement is at the core of AI-driven FinOps strategies. Through ongoing monitoring and refinement, organizations can adapt to evolving cloud environments, optimize resource allocation, and identify cost-saving opportunities in real-time. Following an iterative approach, AI models can stay accurate and pertinent, leading to sustained efficiency gains over time. 

The importance of FinOps is on the rise. Many businesses now prioritize investments in this area for effective cloud cost management. AI-driven tools offer solutions by providing deeper insights and enabling more intelligent decision-making, leading to significant cost savings. As we continue to embrace AI-driven strategies in FinOps, we empower ourselves to unlock the full potential of cloud resources efficiently and sustainably.

Take the first step towards optimizing your cloud costs with AI-powered FinOps. Unlock substantial savings and enhance your cloud efficiency. Join the ranks of businesses redefining their cloud management strategies using AI.