The perfect storm is coming that will force enterprises to rethink their cloud strategy. Cloud architecture will take center stage during 2025. This isn’t just another hype cycle. An enterprise cloud revolution is coming in 2025, and there will be winners and losers. Generative AI and its computational demands are driving this shift, making traditional cloud deployments seem inadequate. It’s imperative for organizations to create a practical blueprint for success amidst these changing dynamics. According to Gartner’s projections, enterprise AI workloads will consume over 30% of total cloud infrastructure capacity by 2025, indicating a significant elevation in AI-driven cloud spending already underway.
Public cloud costs are the latest headache in the boardroom, with companies spending two or three times their initial budgets for cloud services. The financial strain is exacerbated when AI workloads are added to the mix. The most common consulting request nowadays is to figure out why IT is spending excessively on public cloud resources. These requests come from top executives who had little interest in IT infrastructure a decade ago but are now grappling with these escalating costs. Investors are driving demand for solutions to this challenge, pushing for optimized resource utilization and cost reduction.
1. Organize Your Current Setup
Enterprises must first get their existing cloud environment in order to prepare for the changes ahead. Spend the next three to six months thoroughly examining your current cloud spending and usage patterns. Look at the real numbers, not the sanitized versions presented to executives. This deep dive will help you map out your AI and machine learning (ML) workload projections, which are likely to exceed your current estimates. Identifying which workloads in your public cloud deployments are the most expensive can be an eye-opener. You’ll be shocked at what you find when you scrutinize every detail.
It’s not enough to understand where your money is going; you need to strategize how you can optimize spending and resource utilization to support future demands. With AI workloads expected to grow exponentially, it’s crucial to examine the cost implications thoroughly. Besides cost, performance and data sensitivity also play pivotal roles. By understanding these factors, you can make more informed decisions about workload placement and resource allocation. Knowing the current state of your cloud environment lays a solid foundation for future optimizations and strategic planning.
2. Create a Workload Placement Plan
Developing a workload placement strategy that makes sense is the next crucial step in preparing for 2025. This isn’t about following the latest trends but making decisions that align with your business’s realities and goals. Consider factors like data gravity, performance needs, and regulatory requirements when mapping out your strategy. Data gravity refers to the idea that large datasets can attract applications and services, much like a planet attracts matter. This means that the location of your data can influence where your applications should run for optimal performance.
It’s essential to create clear ROI models for your hybrid and private cloud investments. These models should take into account the unique demands of AI and ML workloads. For instance, you might find that certain workloads are better suited for on-prem environments due to their data sensitivity or performance requirements. Others might benefit from the scalability and flexibility of public or hybrid cloud solutions. A well-thought-out workload placement plan can significantly impact performance and cost efficiency by aligning resources with the actual needs of your enterprise.
3. Focus on Technical Design
An optimized technical design is critical for supporting the growing demands of AI and ML workloads. Your attention should be on optimizing data pipelines, integrating edge computing, and meeting AI/ML infrastructure needs. Multicloud connectivity is no longer optional—it’s essential for survival in the evolving cloud landscape. Effective data pipeline management ensures that data flows efficiently between different systems and applications, reducing latency and improving performance. Given the increasing importance of real-time data processing, integrating edge computing is becoming indispensable for many enterprises.
However, these technical advancements must go hand in hand with strong security and compliance frameworks. As data privacy regulations become more stringent, maintaining robust security measures is imperative. Security must not be compromised as you scale and integrate new technologies. Compliance with industry standards and regulations should be a fundamental part of your cloud strategy. This balanced approach ensures that you meet technical requirements while safeguarding your enterprise against potential security breaches and regulatory penalties.
4. Establish a Cloud Economics Office
Many enterprises get it wrong when it comes to managing cloud costs effectively. To address this, set up a Cloud Economics Office that blends infrastructure specialists, data scientists, financial analysts, and security experts. This isn’t just another IT team; it’s a business function designed to drive real value. A Cloud Economics Office ensures that your investments in cloud technologies are aligned with your business objectives, providing actionable insights to optimize spending and resource utilization.
Shift your investment priorities to focus on automated orchestration tools, cloud management platforms, and data fabric solutions. Automated orchestration tools are vital for managing complex cloud environments, reducing manual intervention, and improving operational efficiency. Cloud management platforms provide centralized control over your cloud resources, aiding in better governance and cost management. Data fabric solutions enable seamless data integration and management across multicloud and hybrid environments, ensuring data consistency and accessibility.
5. Implement Financial Management Practices
Effective financial management practices are essential for controlling cloud spending. Implement proper chargeback mechanisms to make departments accountable for their cloud usage. Develop clear total-cost-of-ownership models to provide a comprehensive view of cloud-related costs. When departments see the actual costs of their cloud usage, behaviors change, leading to more mindful consumption of resources. However, be cautious with finops. While there is value in finops, the way some consultants explain and implement it can lead to misleading metrics.
Chargeback mechanisms distribute cloud costs back to the departments or business units using them, promoting greater accountability and efficiency. Total-cost-of-ownership models encompass all costs associated with cloud services, including direct and indirect expenses. These models help businesses understand the true cost of their cloud investments and make more informed financial decisions. By prioritizing transparency and accountability, enterprises can better manage and optimize their cloud spending, ensuring that resources are used effectively.
6. Execute the Transformation
A significant shift is forecasted, compelling enterprises to revisit their cloud strategies. By 2025, cloud architecture will become a focal point. This upcoming change is not just a passing trend but signals a genuine enterprise cloud revolution with distinct winners and losers. The surge in generative AI and its heavy computational requirements are pivotal in this transition, rendering traditional cloud setups ineffective. Organizations must design actionable plans to thrive in this evolving landscape. Gartner predicts that by 2025, AI workloads will consume over 30% of cloud infrastructure, highlighting a notable rise in AI-driven cloud investment.
Rising public cloud costs have become a pressing issue in boardrooms, with many companies overshooting their initial budgets by two to three times. Adding AI workloads amplifies this financial burden. Currently, the most frequent consulting queries revolve around understanding excessive public cloud expenses. Interestingly, these concerns often come from top executives previously uninterested in IT infrastructure, now grappling with these surging costs. Investors are pushing for solutions to optimize resources and slash spending, emphasizing the need for efficiency.
