10 Ways to Reduce Gen AI Costs: Insights from the Gartner® Report

Generative AI has rapidly moved from experimentation to execution and is now embedded across products, operations, and customer experiences. However, as enterprises scale adoption, a structural issue is emerging: AI usage is growing faster than the mechanisms required to control cost. What begins as a contained pilot quickly expands into multiple teams building independently, applications invoking multiple models, and agentic workflows executing multi-step reasoning. The result is not just higher spend, but increasingly unpredictable and compounding costs across the organization. 

This challenge is highlighted in Gartner “10 Best Practices for Optimizing Generative and Agentic AI Costs” , which examines how architectural decisions and lack of operational discipline drive cost overruns at scale. As the report notes, “Through 2028, at least 50% of GenAI projects will overrun their budgeted costs due to poor architectural choices and lack of operational know-how.” This is not a tooling problem—it is fundamentally an architectural and operating model failure. 

How we Believe Gartner Is Defining This Shift

This shift is explored in Gartner “10 Best Practices for Optimizing Generative and Agentic AI Costs” , which focuses on how enterprises must rethink cost, governance, and operational control as AI systems move into production. 

TrueFoundry is mentioned in this report in the context of AI gateways—an emerging control layer for managing cost, reliability, and governance across AI workloads. 

Read the full report here

Gartner highlights the scale of the challenge clearly: “Organizations transitioning from GenAI pilots to production experience a rude awakening when it comes to costs. Creating a production-ready GenAI system can be orders of magnitude more expensive than running a pilot.” This marks the inflection point—AI cost becomes a runtime problem, not a build-time concern, driven by how systems are orchestrated, governed, and operated at scale. 

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Why Generative AI Costs Escalate in Production 

To understand the problem, it is important to break down how AI systems behave at scale. 

1 Inference Becomes the Dominant Cost Layer 

Unlike traditional systems, AI incurs cost every time it is used. 

Gartner highlights this shift: 

“Through 2028, the aggregated costs of model inference will be at least 70% of the total model lifetime costs…” 

This fundamentally changes how cost must be managed. 

2 Agentic Workflows Multiply Cost per Request 

Modern AI systems are not single-step. 

A single request can trigger: 

• multiple model calls 
• tool interactions 
• chained reasoning 

This creates non-linear cost expansion.

3 Fragmented Adoption Drives Inefficiency 

In most enterprises:

• teams adopt models independently 
• no shared governance exists 
• usage patterns are inconsistent 

This leads to: 

• duplicated usage 
• poor model selection 
• unnecessary cost overhead 

4 Lack of Runtime Governance Leads to Cost Sprawl 

Without centralized control: 

• no quotas are enforced 
• no routing decisions are made 
• no cost visibility exists 

This is where cost becomes unmanageable at scale. 

The Architectural Shift: From Model Access to AI Control Plane 

The recommendations in the Gartner point to a clear shift.

This is not about better models. 

It is about controlling how models are used in production. 

Key practices include: 

1 Centralized Access to AI Systems 

A single control layer to manage all model and tool interactions. 

2 Intelligent Model Routing 

Selecting models dynamically based on cost, latency, and performance. 

3 Governance and Policy Enforcement 

Applying quotas, limits, and guardrails across all usage. 

4 End-to-End Observability 

Tracking usage, performance, and cost at a granular level. 

5 Cost Optimization Mechanisms 

Reducing redundant inference through caching and reuse. 

Gartner formalizes this shift: 

“A new category of tools called AI gateways can help control costs by enforcing policies… and by providing features such as caching and model routing to reduce costs.” 

This defines a new layer: 

the AI control plane 

Where TrueFoundry Fits 

We believe that the direction Gartner outlines points to a clear requirement: 

a centralized control layer that governs how AI is used across the enterprise. 

TrueFoundry has been mentioned in this report as part of this emerging AI gateway ecosystem. 

TrueFoundry operates at the layer where AI usage occurs—and where cost is generated. 

1 From Reactive Tracking to Proactive Control 

Instead of: 

• tracking cost after it happens 

TrueFoundry enables: 

• controlling usage before it scales

2 Dynamic Optimization at Runtime 

• Route requests across models based on cost-performance trade-offs 
• Apply budgets, quotas, and rate limits 
• Optimize usage through caching and reuse 

3 Full Visibility Across AI Systems 

• Token-level cost tracking 
• Request-level tracing 
• Team and application-level analytics 

4 Governance at Enterprise Scale 

• Centralized access control 
• Policy enforcement across all AI interactions 
• Guardrails for safe and compliant usage 

5 Enterprise-Ready Deployment 

• Works across cloud and on-prem environments 
• Supports multi-model, multi-provider strategies 
• Avoids vendor lock-in 

This shifts the operating model from: 

“What is our AI spend?” 

to 

“Are we using AI efficiently—and should this request even be executed?” 

Why This Matters for CXOs 

Generative AI is entering its second phase. 

The first phase was about access. 

The next phase is about control and economics. 

At the same time, pricing models are evolving: 

“By 2030, at least 40% of enterprise SaaS spend will shift toward usage-, agent- or outcome-based pricing.” This makes cost: 

• a financial decision ‍
• a governance problem ‍
• a strategic differentiator 

Organizations that introduce control at the runtime layer will: 

• improve cost predictability 
• reduce unnecessary spend 
• scale AI systems responsibly 

Final Perspective 

Gartner is defining generative AI cost as a systems-level challenge rooted in runtime behavior—not model selection. Because at scale: 

• every request carries cost 
• every workflow multiplies usage 
• every inefficiency compounds 

The enterprises that succeed will not be those that adopt AI faster. 

They will be the ones that introduce: 

control, governance, and economic discipline into how AI systems operate. 

The advantage will not come from access to models— 

but from control over how those models are used. 

Explore Further 

�� Read the full Gartner report 

�� Learn more about TrueFoundry: https://www.truefoundry.com 

Disclaimer

Gartner does not endorse any vendor, product or service depicted in its research publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner’s research organization and should not be construed as statements of fact. 

Gartner, 10 Best Practices for Optimizing Generative and Agentic AI Costs, By Arun Chandrasekaran et. al, 20 March 2026

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