AI cost optimization: How model routing improves AI economics

Reading Time: 8 minutes

For most enterprises, the AI conversation has moved past the pilot stage. The models work. The use cases have been proven. Adoption is accelerating, but so are costs, complexity, and data privacy risks.

As AI scales from a handful of experiments to production workloads serving thousands of users, the economics change dramatically. More users, more prompts, more AI agents, and more automated workflows all translate into higher inference costs that compound quickly when every request is routed to the most expensive model available.

According to McKinsey, 93 percent of organizations surveyed have exceeded their AI budgets, making the gap between AI spending and business value increasingly difficult to ignore. At the same time, scaling AI is intensifying data privacy and governance concerns. In a OneTrust survey, 82 percent of respondents said AI risks have accelerated the need to modernize and improve governance.

This budget pressure is driving a new discipline: AI cost optimization.

“Financial discipline (not model choice) is what decides whether a promising [AI] pilot ever scales,” said Tina Schuchman, corporate vice president, Microsoft Foundry, in “The Economics of Agent Optimization.”

Ninety-three percent of organizations have exceeded their AI budgets.

What is AI cost optimization?

AI cost optimization is the practice of maximizing business value from AI while minimizing the total cost of building, running, and scaling AI workloads. This is sometimes referred to as AI cost to value realization.

Importantly, cost optimization isn’t just about reducing cloud spending or purchasing cheaper graphics processing units (GPUs). It encompasses every dimension of AI economics — from infrastructure and inference costs to model selection, workload design, and operational governance.

A key discipline within cost optimization is tokenomics: maximizing business outcomes per dollar spent on AI rather than simply minimizing infrastructure costs. Every AI interaction consumes tokens — the units of text that models process — and those costs add up quickly at enterprise scale.

Understanding and managing token economics is becoming a core competency for companies running AI in production. And while the playbook for AI cost optimization is still being written, organizations increasingly recognize that effective cost management is now table stakes for scaling AI.

Why AI costs escalate so quickly

During an AI pilot, AI costs are manageable. A small team runs a limited number of prompts against a single model, and the monthly bill barely registers.

Production is a different story.

An enterprise AI environment can involve thousands of users, dozens of applications, AI agents, retrieval systems, and automated workflows — all running continuously. Each of those interactions consumes tokens, and those costs compound with every new user, use case, and workflow added to the environment.

AI agents accelerate this tremendously. Unlike a simple chatbot exchange, an agent may call multiple APIs, execute multi-step workflows, review and revise its own outputs, generate summaries, and consult several models before completing a single task. One user request can trigger dozens of model interactions behind the scenes — each one incurring cost.

A large share of those interactions still run on expensive frontier models. Glean CEO Arvind Jain estimates that roughly 95 percent of enterprise AI usage runs on frontier models, including tasks that less expensive alternatives could handle.

The result is a growing disconnect between AI spending and AI value. Companies are paying premium prices for every AI interaction regardless of complexity — from a simple meeting summary to a sophisticated financial analysis.

“The AI conversation in most enterprises has moved from the whiteboard to the budget review,” said Tina Schuchman, corporate vice president, Microsoft Foundry, in “The Economics of Agent Optimization.” “Two years ago, the question was whether AI could work. The question leaders are asking now is sharper and less comfortable: is it paying for itself?”

The answer increasingly depends on whether companies can match the cost of each AI interaction to the value it delivers. And that’s driving interest in one of the most practical tactics within AI cost optimization: model routing.

Companies are paying premium prices for every AI interaction regardless of complexity.

AI model routing: Matching the right model to the task

Model routing addresses a straightforward question: does every AI request need to go to the same model?

Rather than sending every prompt to a single expensive frontier model, a routing layer evaluates each request and determines which model is the best fit based on factors like cost, complexity, latency requirements, and context-window needs. Simpler requests get routed to smaller, less expensive models. Complex tasks that require advanced reasoning or nuanced output get escalated to frontier models.

The concept has a clear analog in how professional services firms already operate.

A law firm doesn’t assign every task to a senior partner. Administrative work goes to paralegals. Routine legal reviews are handled by associates. Complex strategy and high-value client work are reserved for partners. The quality of the overall operation doesn’t suffer— it improves, by sending each task is handled by the right resource at the right cost.

Model routing applies the same logic to AI workloads and the impact on costs can be significant. In one evaluation cited by NVIDIA, combining its NeMo Switchyard open-source routing library with Nemotron 3.5 Lightning reduced agent workflow costs by up to 74 percent compared with running every request through a premium model, depending on the routing configuration and associated accuracy tradeoffs.

“That is the power of a system of models, matching the right model to each step of the workflow,” noted VentureBeat. “The challenge is doing so without creating an operational burden that offsets the savings.”

Model routing is also compelling because it gives organizations one of the first practical levers they can pull to improve the economics side of that equation without sacrificing outcomes.

Model routing gives organizations one of the first practical levers they can pull to improve the economics side of that equation without sacrificing outcomes.

Sovereign AI and AI model routing

But to be clear, model routing isn’t just about cost optimization. It can also help organizations control where sensitive data is processed and which models and providers are permitted to handle it. Routers act as a gateway layer, evaluating requests and directing them to approved providers, private environments, or regional endpoints based on the organization’s data residency, privacy, contractual, and AI governance requirements. These policies can support broader compliance efforts related to regulations such as the General Data Protection Regulation (GDPR) and the EU AI Act.

“Using model routing purely to slash token costs misses some powerful value,” wrote Saad Malik in “Smart model routing is the key to AI governance (not just token savings).” He continued, “Embrace the idea of model routing as a way to enforce policy or governance.”

As a result, routing can help organizations pursue sovereign AI strategies that give them greater control over how AI is developed, deployed, and governed across their infrastructure, models, data, and staff. Enterprises can configure routing policies that direct sensitive workloads to approved models housed in private cloud environments while routing lower-risk tasks to public AI services. This allows teams to factor cost, compliance, security, and performance requirements into a single decision framework rather than managing them separately. In this way, routing decisions help organizations balance cost efficiency with control over where data lives and how it’s used.

“It’s about retaining full control over the entire AI life cycle—from the physical compute to the algorithmic logic,” said Ali Ustun in McKinsey’s “What is sovereign AI?”

The net effect is a shift from treating all AI workloads equally to evaluating each interaction based on what it actually requires. Instead of measuring AI spend by cost per token, companies can begin measuring cost per outcome — a more meaningful metric for demonstrating business value.

Routing decisions help organizations balance cost efficiency with control over where data lives and how it’s used.

The future of AI cost optimization

As AI spending grows, cost optimization is becoming a strategic discipline.

Organizations are moving through four increasingly connected stages: defining business outcomes, establishing governance and security foundations, optimizing infrastructure, and orchestrating models, agents, and applications through intelligent routing.

The emerging frontier is model orchestration. As enterprises deploy more models and AI-powered workflows, directing each request to the right resource is becoming a foundational capability.

Fortunately, the toolkit is expanding rapidly. Small language models, fine-tuned open-source models, agentic workflows, and intelligent routing platforms give IT leaders more options than ever to balance cost, performance, governance, and security. In many cases, the best model for a workload may not be a frontier model at all.

The organizations that manage this complexity successfully will be the ones that sustain AI investments and continuously demonstrate business value.

How SHI can help: Building a cost-optimized AI strategy

Most organizations have already deployed AI. The challenge now is managing costs while maximizing business outcomes.

SHI helps enterprises take a holistic approach to AI economics by evaluating data, infrastructure, AI platforms, governance, model strategy, and tokenomics. Rather than focusing solely on infrastructure or inference costs, SHI helps organizations align AI investments with measurable business objectives.

As a premier member of the Tokenomics Foundation, SHI helps organizations identify opportunities to improve utilization, optimize model selection and routing strategies, strengthen governance, and reduce unnecessary AI spending.

The goal is not simply to spend less on AI. It is to ensure every AI workload delivers the right balance of cost, performance, security, governance, and business value.

The enterprises that achieve the greatest success with AI will not necessarily be those that spend the most. They will be the ones that continuously optimize the value generated from every AI investment.

NEXT STEPS:

Speak with an SHI expert and let’s work together to solve AI cost optimization

AI tokenomics needs more than cost per token

 Check out our blog on FinOps for AI: How to stop chasing tokens and start measuring outcomes