About Digital Bridge Review

Independent analysis of the AI infrastructure economy.

We cover how enterprises build, buy, and optimise AI infrastructure — from cloud commitment economics to multi-provider LLM deployment and FinOps discipline at scale.

$180B

Enterprise AI spend

$2.4B

Credits expiring unused

64%

Enterprises over-provisioned

The Structural Shift

From IT line item to boardroom question.

Cloud stopped being a pure procurement question years ago. With hyperscaler commitments running into eight and nine figures, multi-year consumption agreements binding CFOs to specific providers, and GenAI workloads introducing entirely new cost curves, cloud is now a strategic economic decision.

The same shift is now playing out — faster and with higher stakes — for AI infrastructure specifically. Enterprises are committing to capacity they cannot yet model, deploying LLMs whose unit economics they don’t fully understand, and watching large credit balances expire unused against workloads they over-provisioned twelve months earlier.

“The AI infrastructure stack is reshaping enterprise budgets. Most companies are still buying it like 2019 cloud — long commitments, steady-state assumptions, and no mechanism for spiky, model-driven demand.”

What We Cover

Three pillars of AI infrastructure

Cloud Commitment Economics

Azure MACC, AWS EDP, and GCP committed-use agreements — how enterprises negotiate them, draw down against them, and the emerging secondary market for unused balances.

LLM Deployment & AI Adoption

Production LLMs across OpenAI, Anthropic, Azure OpenAI, and Vertex — model selection, routing strategy, latency and cost trade-offs, and multi-provider inference in practice.

Enterprise Automation & FinOps

Workflow automation, AI agents in production, observability for LLM-driven systems, and the FinOps operating model that keeps AI spend aligned with business value.

Our Readers

Built for the practitioners

CFOs & Finance Leaders

Executives overseeing enterprise cloud and AI spend, negotiating hyperscaler commitments, and answering to boards on infrastructure ROI.

FinOps Practitioners

Cloud cost analysts and FinOps leads now extending their discipline into token-level accounting for LLM workloads and inference pipelines.

Infrastructure Architects

Platform and infrastructure leaders designing the AI stack — model routing, observability, and the operational plumbing behind production AI.

Founders & Investors

AI-native startup founders, technical CEOs, and investors tracking the economics of cloud commitments, credit markets, and AI infrastructure at scale.

Get in touch

Independent intelligence for the professionals building the next generation of AI-native enterprise infrastructure. Questions, tips, or pitches welcome.