Despite cloud being an enabling and democratizing force in AI, cost is becoming a prohibitive barrier.
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Cloud costs have long been a concern for IT departments – never more so than now. Since the widespread adoption of AI and GenAI, the costs have ballooned to the point of potentially halting projects and disrupting innovation. Cloud spending has surged by an average of 30% over the last year, and 72% of IT and finance leaders attribute the increase mainly to traditional and generative AI adoption. It makes sense, since the use of GenAI has also increased from 33% in 2023 to a massive 71% in 2024 and is still increasing.
93% of IT leaders will be deploying AI agents over the next two years. For them, genAI and agentic AI are not just tech projects, they are business imperatives without which they risk losing their competitiveness in the long run. As organizations build and train their own AI agents, the demand for compute and the resulting hikes in cloud spending are apparently inevitable. But here’s the conundrum: What happens when these nascent AI initiatives start running corporate budgets dry? It’s not a distant reality, it’s happening right now!
IT leadership and cloud teams must take action immediately to control costs before they bring key initiatives to a screeching halt. Cloud management platforms like emma are a necessity for managing and controlling cloud costs and optimizing resource usage to ensure snowballing cloud costs don’t become a silent disruptor of AI innovation.
“According to IBM, 100% of executives report cancelling or postponing at least one genAI initiative due to unmanageable costs. Despite cloud being an enabling and democratizing force in AI, its cost is becoming a prohibitive barrier.”
In the past couple of years, cloud has become the launchpad for AI innovation. It gives instant access to the specialized GPUs and TPUs needed for AI model training and inference, but the cost of those is a premium compared to your typical compute instances. Given the high demand of specialized hardware resources, enterprises feel compelled to invest in dedicated or reserved cloud capacity to fuel their genAI ambitions without interruptions. Without accurate predictions, the pursuit of preparedness can result in overprovisioning, and cloud bills can get out of control.
In addition, compute capacity is just one part of the equation. AI workloads need storage for the massive datasets they generate and process for training and real-time inference. They also need networking equipment and services to store and access that data. During development, DevOps and AI teams can run multiple test environments, continuously consuming GPUs and TPUs unabated. If those environments are not properly managed or decommissioned, they can keep accumulating unnecessary costs. These hidden and forgotten costs, in addition to the known operational costs, silently compound over time, turning the final cloud bill into a shockingly high figure beyond all reasonable projections.
The result? According to IBM, 100% of executives report cancelling or postponing at least one genAI initiative due to unmanageable costs. Despite cloud being an enabling and democratizing force in AI, its cost is becoming a prohibitive barrier. Only those who successfully rein in costs will be able to overcome this barrier to lead the AI innovation race, and that requires a proactive cost governance framework.
AI’s resource consumption can be highly variable and unpredictable. It depends on several factors, including model size, complexity, training strategies as well as the nature of applications being powered. For instance, AI-powered applications like chatbots and fraud detection experience fluctuations in demand but are also latency-sensitive. So, companies may end up overprovisioning resources to avoid lag. Similarly, if you’re using spot instances or shared GPUs, they may be interrupted and lead to variable resource consumption overall.
Due to this general variability, static budgeting and retrospective analysis are simply not enough. They can only catch budget overruns after the bill has already arrived. As such, cost control in the age of AI and cloud requires strategic decision-making and proactive cost management, powered by predictive analytics and AI-driven insights.
Combined, these proactive measures can control cloud cost escalation before it becomes serious enough to halt or postpone AI initiatives. Effectively managing compute costs for AI adoption doesn’t just prevent budget overruns, it creates a sustainable competitive advantage, allowing companies to scale AI initiatives without financial roadblocks.
With the emma cloud management platform, you can turn AI to your advantage with capabilities like predictive analytics, anomaly detection, and automated controls that enable strategic cost management across multi-cloud environments.
Proactive cloud cost management is crucial for enterprises hoping to lead in AI innovation. Experts have predicted that the next wave of AI innovation will hinge on efficiency and optimizations. It’s evident in DeepSeek’s meteoric rise to prominence in the AI space. By offering low-cost models that rivaled compute-hungry, highly-expensive ChatGPT models, they skyrocketed to the top almost overnight.
Algorithmic efficiency is just one piece of the puzzle. To truly control AI-powered cloud costs, it’s equally important to ensure efficient resource consumption, reduce unnecessary spend, and keep a close eye on cloud budgets. These practices are key to building a sustainable AI future, where innovation isn’t stalled by cost uncertainties or constraints. In this sense, consider proactive cost control not just an operational necessity but a strategic accelerator for innovation.
See firsthand how emma’s AI-driven cloud cost management can transform your AI roadmap. Explore the platform with a no-commitment, 14-day free trial, or request a personalized demo today!
