- AI requires massive investment in cloud infrastructure, skills, and compliance strategies
- Enterprises are balancing performance, cost, and risk as they deploy AI across cloud and on-premises environments
- New Fierce Network Research report covers how successful enterprises are deploying infrastructure for AI
Artificial intelligence looks like magic — but only to those who don't understand it.
To make the magic happen, AI requires enormous infrastructure spending, in compute, network, storage, real estate, power, coolant and so on, along with investment in skills to build all that infrastructure and keep it running.
Any sufficiently advanced technology is indistinguishable from magic. — Arthur C. Clarke
AI is critical for business success in today’s economy. Organizations that implement it effectively will prosper. But those that make poor AI infrastructure choices risk severe, potentially ruinous, cost overruns.
“All of the promise of AI is fraught with risk. It’s a paradox,” Kevin Cochrane, chief marketing officer at cloud provider Vultr told Fierce Network Research in an interview. “You could remake your business — or crater your entire IT operations and budget.”
Three approaches
We talked with Healthcare firm Medidex, which adopted a cloud-first model, relying exclusively on Vultr’s infrastructure and OpenAI’s language models to power pharmacist chat services. Simon Greenberg, Medidex’s operations director, said the move allowed the company to scale while controlling costs and ensuring compliance.
Biotech startup Athos Therapeutics opted for a multicloud strategy, combining Vultr, AWS, Azure, and Dell Technologies to process massive genomic datasets. The approach helps Athos optimize costs and performance while ensuring compliance. But complexity comes with trade-offs. “When we deploy AI models on the cloud, we have to think about how we optimize the whole pipeline, not just one part—data, model, inference pipeline, and utilizing the hardware,” said June Guo, Athos VP of AI and machine learning.
Meanwhile, Nature Fresh Farms, a large greenhouse grower, is staying on-premises. The company built a private cloud to power AI-driven agriculture operations, citing the need for low latency and control over physical infrastructure. Keith Bradley, VP of IT and security, said the in-house approach remains the most cost-effective path for their business.
While each strategy has merits, all three organizations share a common challenge: balancing AI’s transformative potential against unpredictable costs and scarce skills.
The report also explores evaluating tradeoffs among hyperscalers, neoclouds and independent providers; strategies for maximizing GPU efficiency and minimizing unnecessary costs, and why agentic AI raises the stakes for latency, bandwidth and data locality.
Download the Fierce Network Research report now: "Unlocking AI at scale: Cloud infrastructure strategies for healthcare, life sciences and agriculture."