The Economics of Running AI Workloads on the Cloud: Costs, Benefits, and Trends in 2025

Graphic with text and design elements for jps event or

Running artificial intelligence (AI) workloads on the cloud has become the new normal for businesses across industries. From startups to global enterprises, the cloud is where AI lives, learns, and scales. But what does it really cost to run AI in the cloud? And what are the economic benefits that make it worth the investment?

In this deep dive, we’ll unpack the economics of running AI workloads on the cloud, explore the latest trends, and break down the numbers that matter most in 2025.

Why the Cloud is the Go-To for AI

Let’s start with the basics: why do so many organizations choose the cloud for their AI workloads?

Scalability and Flexibility

One of the biggest advantages of the cloud is its ability to scale. AI models—especially generative AI—require massive amounts of computing power. On-premises infrastructure often can’t keep up, especially when demand spikes. The cloud, on the other hand, lets you scale up or down as needed, paying only for what you use.

Lower Upfront Costs

Building and maintaining your own AI infrastructure is expensive. You need specialized hardware, cooling systems, and a team of experts to keep everything running. The cloud eliminates most of these upfront costs. Instead of buying servers and GPUs, you rent them from providers like AWS, Microsoft Azure, or Google Cloud.

Faster Time to Market

With the cloud, you can deploy AI models in hours or days, not months. This speed is crucial in today’s fast-paced business environment, where being first to market can make all the difference.

The Cost Drivers of AI Workloads in the Cloud

While the cloud offers many benefits, running AI workloads isn’t cheap. Let’s look at the main cost drivers.

Compute Costs

Compute is the biggest expense when running AI workloads. Training large models like GPT or Llama can require thousands of GPU hours. Cloud providers charge based on the type and number of instances you use, as well as how long you use them.

For example, a single high-end GPU instance on AWS can cost $10–$20 per hour. Training a large model might take hundreds or even thousands of hours, quickly adding up to tens of thousands of dollars.

Storage Costs

AI models generate and process vast amounts of data. Storing this data in the cloud can be expensive, especially if you need high-performance storage for fast access.

Networking Costs

Moving data between cloud services and on-premises systems can incur networking costs. These costs can add up, especially for large datasets or real-time applications.

Managed Services

Many organizations use managed AI services, such as pre-trained models or automated machine learning platforms. These services are convenient but come at a premium.

Hidden Operational Overhead

There are also hidden costs, such as monitoring, security, and compliance. These operational overheads can be significant, especially for large-scale deployments.

The Economic Impact of AI in the Cloud

Despite the costs, the economic benefits of running AI workloads on the cloud are substantial.

Productivity Gains

AI can automate repetitive tasks, analyze data at scale, and make predictions with high accuracy. This leads to significant productivity gains across industries.

According to a recent study, AI is expected to increase productivity and GDP by 1.5% by 2035, nearly 3% by 2055, and 3.7% by 2075. These gains are driven by automation, improved decision-making, and new business models enabled by AI.

Cost Efficiency

Migrating from on-premises infrastructure to the cloud can significantly reduce the costs of deploying and maintaining AI. A study by Microsoft found that organizations experience financial benefits of $500,000 or more over three years when moving to Azure for AI workloads. The cloud also offers 15% lower costs to maintain AI/ML compared to on-premises infrastructure.

Flexibility and Scalability

The cloud’s flexibility and scalability allow organizations to experiment with new AI models and applications without the risk of large upfront investments. This agility is crucial for innovation and staying competitive.

Trends Shaping the Economics of AI in the Cloud

Several trends are shaping the economics of running AI workloads on the cloud in 2025.

The Rise of Generative AI

Generative AI, such as large language models and image generators, is driving much of the growth in AI workloads. These models require immense computing power and are a major driver of cloud spending.

AI-Optimized Infrastructure

Cloud providers are investing heavily in AI-optimized infrastructure, such as specialized GPUs and TPUs. These hardware advancements are making AI workloads more efficient and cost-effective.

The Growth of SaaS, PaaS, and IaaS

The cloud services market is dominated by SaaS, PaaS, and IaaS models. SaaS is expected to lead with projected revenues of $390.5 billion in 2025, followed by PaaS at $208.64 billion and IaaS at $180 billion. IaaS is the fastest-growing segment, with a CAGR of 26.2% through 2025.

The Role of FinOps

FinOps, or cloud financial management, is becoming increasingly important for organizations running AI workloads. FinOps brings financial accountability to engineering teams, helping them understand the cost implications of their decisions and optimize cloud spending.

The Global AI Infrastructure Race

Governments and companies around the world are investing heavily in AI infrastructure. In 2025, over $1 trillion is being invested in AI data centers, including $500 billion for the Stargate Initiative, $80 billion for Microsoft, $65 billion for Meta, and $112 billion from France. This global race is driving innovation and competition, leading to lower costs and better performance.

Strategies for Cost Optimization

Given the high costs of running AI workloads on the cloud, cost optimization is crucial. Here are some proven strategies:

Workload Classification

Classify your AI workloads based on their requirements and priorities. This helps you allocate resources more efficiently and avoid over-provisioning.

Continuous Monitoring

Regularly monitor your cloud usage and costs. Use tools and dashboards to track spending, identify inefficiencies, and make data-driven decisions.

Model Optimization

Optimize your AI models to reduce their computational requirements. Techniques such as model pruning, quantization, and distillation can significantly lower costs without sacrificing performance.

Leveraging Managed Services

Use managed AI services when possible. These services can reduce operational overhead and provide access to the latest technologies and best practices.

FinOps Practices

Adopt FinOps practices to bring financial accountability to your engineering teams. Regular reviews of usage data, forecasting future spend, and aligning budget with business outcomes can help you control costs and maximize ROI.

The Future of AI in the Cloud

Looking ahead, the economics of running AI workloads on the cloud will continue to evolve. Here are some key trends to watch:

Increased Automation

AI will increasingly automate cloud management tasks, such as resource allocation, scaling, and cost optimization. This will make it easier and more cost-effective to run AI workloads at scale.

Greater Integration

AI and cloud services will become more tightly integrated, with cloud providers offering end-to-end solutions for AI development, deployment, and management.

New Business Models

The rise of AI in the cloud will enable new business models, such as AI-as-a-Service and pay-per-use AI platforms. These models will make AI more accessible and affordable for organizations of all sizes.

Regulatory and Ethical Considerations

As AI becomes more pervasive, regulatory and ethical considerations will play a larger role in shaping the economics of cloud-based AI. Organizations will need to navigate these challenges to ensure compliance and maintain public trust.

Conclusion

Running AI workloads on the cloud offers significant economic benefits, from productivity gains to cost efficiency and flexibility. However, it also comes with challenges, such as high compute costs and operational overhead. By understanding the economics of cloud-based AI and adopting best practices for cost optimization, organizations can maximize the value of their AI investments and stay competitive in the digital age.

As the cloud and AI continue to evolve, the future looks bright for organizations that embrace these technologies. The key is to stay informed, be agile, and make smart decisions that align with your business goals.

For express one zone with branding and visual

Scene depicting a collision event from home

Group of team members standing together in an outdoor