Does our AI workload justify investment in our own compute infrastructure, or is cloud more cost-effective?

For most mid-market companies, cloud APIs are more cost-effective. Owned GPU infrastructure only makes sense for sustained high-volume inference that few non-AI-native organizations generate.

What is the environmental and energy cost of the AI computing we consume?

AI inference consumes significant energy. If sustainability matters to your organization, factor energy consumption into model selection — smaller, more efficient models have a lower footprint.

Are we prepared for the possibility that compute costs decrease significantly as new hardware enters the market?

GPU prices and cloud costs are trending down as competition increases. Avoid long-term infrastructure commitments that lock you into today's prices when next year's may be substantially lower.

The AI Industry

Data center infrastructure & GPU

By Mark Ziler · Last updated 2026-04-05

AI runs on specialized chips called GPUs housed in massive data centers. The supply and cost of this infrastructure directly affects what AI costs your business. When data centers are at capacity, AI service prices rise and response times slow. Understanding this helps you evaluate vendor pricing and plan for whether AI costs in your budget will go up or down over the next few years.

Go deeper

Your AI vendor promises 99.9% uptime and sub-second response times. But during your busiest period last quarter — open enrollment — response times tripled and two workflows timed out entirely. The vendor blamed 'unprecedented demand.' What actually happened: they're on shared GPU infrastructure, and when demand spikes across all their customers simultaneously, your workloads compete for the same chips. Your busiest period is also everyone else's busiest period.

The trap most companies fall into is treating AI infrastructure like traditional cloud services where capacity is essentially unlimited. GPU capacity is physically constrained — there are a finite number of chips, and demand currently exceeds supply. This means your AI vendor's performance promises are only as good as their infrastructure commitments. 'We use AWS' means nothing if they're on spot instances that get reclaimed during peak demand.

Questions to ask

Does our AI vendor have dedicated GPU capacity or are they on shared infrastructure that degrades during peak periods?
What happened to our vendor's performance during the last industry-wide demand spike, and do they have contractual SLAs with financial penalties?
If GPU costs drop 40% over the next two years as supply catches up, does our contract allow us to capture those savings or are we locked into current pricing?