Agentic AI at Scale: The Biggest Cost Isn’t What You Think

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Enterprises are racing to deploy AI agents. New models, frameworks, and tools make it easier than ever to build an agent and demonstrate its potential.

But here’s the uncomfortable truth: Building a few successful agents is relatively easy. Scaling to hundreds or thousands, while delivering positive ROI, is an entirely different challenge.

As organizations scale their agentic AI initiatives, costs can escalate rapidly. Contrary to popular belief, the biggest cost driver isn’t necessarily the AI models or the tokens they consume. It’s the complexity of connecting agents to enterprise data.

A new research report by Veqtor8, The ROI of an AI Data Layer for Agentic AI, quantifies this problem, and the business value of solving it with an AI data layer from Denodo.

The Hidden Costs of Scaling Agentic AI

Consider what happens as an enterprise moves from a handful of AI pilots to hundreds of agents operating across the business.

Every new agent needs access to relevant enterprise data, which is often spread across dozens of systems. Each integration requires engineering, semantic context, security controls, and ongoing maintenance. Multiply that effort across hundreds of agents, and the costs quickly become unsustainable.

That’s before the enterprise sees the explosion in token usage, as agents discover, retrieve, reconcile, and process data, and before they account for the cost of correcting mistakes when the agents get it wrong.

As a senior AI leader at a global bank observed, when interviewed for this research:

“Data products with semantics and context make it easier and faster. It reduces deployment by months. It can reduce deployment to days or weeks.”

The fundamental problem is that most enterprises are building integrations, semantics, and governance separately for each agent, rather than creating reusable capabilities that serve all agents.

The ROI Numbers Are Compelling

Veqtor8 interviewed seven large enterprises and developed a composite economic model, examining what happens as an organization scales from 100 to 900 agents over three years.

The modeled results of using Denodo’s AI Data Layer were striking:

• 290% ROI over three years, combining cost avoidance with additional business value.
• 3x faster agent deployment, accelerating time-to-value.
• 79% lower agent-to-data integration costs, the single largest category of cost savings.
• 68% lower AI operating costs, by reducing unnecessary token consumption and associated infrastructure overhead, such as compute.
• 2.9x more business value generated, as more agents reach production and deliver useful work faster than they could without an AI data layer.

Perhaps the most interesting finding is that integration agility delivers greater cost savings than token optimization. While much of the industry focuses on making AI models cheaper to run, an even greater opportunity lies in eliminating the redundant data engineering work required to deploy and maintain agents at scale.

These are modeled results, not guarantees for every enterprise. But they demonstrate how dramatically the economics of agentic AI can change with the right data architecture.

The AI Data Layer Changes the Economics

Denodo addresses these challenges by providing a shared AI data layer that delivers active context to every agent.

Through universal connectivity, zero-copy live data access, unified semantics, and centralized governance, Denodo enables enterprises to connect to data once, define business context once, and enforce governance consistently, then reuse these capabilities across all agents.

Agents get trusted, business-ready data without independently discovering, integrating, and processing everything themselves. This also reduces MCP server sprawl and unnecessary token consumption.

As an applied AI leader at a global custody and asset servicing bank explained:

“Denodo enables us to minimize the number of MCP servers we need, reducing server sprawl. This lowers complexity, improves security and trust, and controls token costs.”

The result? Faster deployment, lower operating costs, more trustworthy agents, and greater business value as adoption grows.

Importantly, these benefits compound with scale. The more agents an organization deploys, the greater the value of shared integration, semantics, and governance.

The Real Opportunity: Scale Without Breaking the Bank

I had the pleasure of working with Andrew Milroy, Chief Analyst at Veqtor8, who produced this research. I was particularly impressed by the rigor of his ROI analysis, the depth of his interviews, and his ability to translate complex architectural considerations into a clear, quantifiable business case.

My biggest takeaway? The question is no longer simply whether agentic AI can deliver business value. It’s whether enterprises can scale that value economically, without introducing unsustainable complexity and costs.

The winners in agentic AI won’t necessarily be those who build the most agents. They’ll be those who can scale them efficiently, reliably, and profitably.

To explore the findings, read the full report, The ROI of an AI Data Layer for Agentic AI, or contact us to discuss how Denodo can help maximize the ROI of your agentic AI initiatives.