AI Innovation through Cost Control: How emma’s AI Fuels Your AI Roadmap

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.

The Cost Complexity Challenge in AI

“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.

Proactive vs. Reactive Cost Management

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.

1. Predict & Plan: Use historical usage data and predictive analytics to predict precise demand and plan resource allocation accordingly.
2. Full Cost Awareness: In addition to training and inference costs, consider the costs of data storage, security, and networking as well.
3. Observability & Monitoring: Enable observability and continuous monitoring not just for finance and FinOps, but for all teams across the board. This will help address resource wastage and unnecessary costs due to underutilization and zombie environments.
4. Model Distillation & Optimization: Use model distillation to reduce the size and complexity of models dedicated to specific tasks.
5. Selective Repatriation: Repatriate certain AI workloads in-house, closer to the data sources. It can cut costs and improve performance, but your FinOps strategy and control planes must extend across both cloud and on-prem environments.
6. Kubernetes & Containers: Use Kubernetes and containerization to schedule tasks during non-peak times, autoscale based on real-time demand, and leverage spot instances with fallback strategies.
7. Automated Budget Controls: Set budget thresholds for individual projects, teams, and business units. Also configure automated, proactive alerts for anomaly detection, real-time cost awareness, and governance across teams and environments.

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.

How emma Transforms Cost Management into Strategic Advantage

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.

1. End-to-End Visibility: emma provides granular visibility into cloud spend across all on-premise and cloud-based environments via a single pane of glass. It ensures a holistic and consistent view of the cloud costs.  
2. Accurate Cost Attribution: Users can view cloud spend by projects, teams, and business units, in line with cost control and FinOps strategies. Users can also implement  department accountability, showback, or chargeback models for establishing cross-team accountability – this allows for efficiency, allowing individuals and teams to maintain cost awareness and spend responsibly instead of leaving IT and finance to deal with the aftermath of their overspending.
3. Data-driven AI Investments: Cost visibility and granular attribution allow companies and decision-makers to make strategic decisions when pursuing AI projects.
4. AI-powered Predictive Analytics: emma’s advanced machine learning algorithms anticipate cost spikes before they occur. It identifies unexpected costs in real-time, allowing you to address cost anomalies immediately. For instance, the platform can forecast when an AI training job is likely to exceed its budget before it reaches a critical threshold. Based on real-time pricing and usage data, emma can recommend scaling down resources, shifting workloads to cost-effective regions, or switching to spot instances.
5. Dynamic Cost Optimization: emma can dynamically allocate resources with cost considerations using the AI-powered actionable insights.
6. Operational Efficiency for Accelerated AI Innovation: emma streamlines provisioning and management of resources through automated workflows. For instance, you can schedule when to run certain AI workloads or environments based on work hours, peak usage periods, or predefined demand patterns. It ensures that instances automatically scale down during off-hours and scale up when needed. This reduces manual overhead, and operational efficiencies achieved through emma directly accelerate AI project timelines and outcomes.

Calculate Your Cloud Savings Potential

With this handy calculator, you’re given a real and tangible look at how much cost you can cut with the right tools. Compare savings between using emma and other Cloud Providers, and see for yourself!

Turning Cost Control into Competitive AI Advantage with emma

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!