Executive Summary

• The industry is at an inflection point where inference economics is needed with capacity planning as AI scaling turns out to be a fight for marginal efficiency.
• AI landscape is shifting drastically as focus pivots more to contious, live operational costs of inference and not one-off model training.
• To stay ahead of this shift, B2B leaders are ditching over-provisioning and adoping inference econicmics, where they leverage mixed custom hardware and automated, tiered SLAs to control cost-per-query.

Inference is on track to exceed training as the dominant data centre requirement, according to JLL’s 2026 Global Data Centre Outlook; they state that by 2030, AI could potentially represent half of all workloads and inference becomes the primary driver behind that.

The tide is turning rapidly this year as companies kick up their AI use to full-scale production, which is changing how we use AI, which then changes the bottlenecks as a result. Instead of raw training capacity being the primary constraint, it’s now inference economics, which is the live, operational cost of serving AI queries to real users.

Continuous OpEx vs one-time CapEx

The difference between OpEx and CapEx is shaping how enterprise CTOs and data centre operators plan for future capacity. Training is building a model that needs short-term bursts of a large quantity of computing power and quite a predictable capital expense; this can be scheduled when costs are low or in remote regions where power is more cost-effective. Inference, on the other hand, is perpetual because you’re running a model in production 24/7, 365 days a year. Every customer chatbot interaction, every automated workflow, draws live power.

Training presents a step upfront hurdle with its cost, but inference is about 8-9-% of the lifetime cost of an AI system, and with Gartner claming 55% of all AI optimised infrastructure spending will support inference workloads by the end of the year, it’s safe to say it will keep rising.

Inference Economics is rewriting capacity planning

There are three ways that inference economics is rewriting capacity planning:

• There is a shift back to metro data centres to reduce round-trip latency and network egress costs with inference, because inference can’t afford delays, whereas training can. Training infrastructure clusters have been built in remote fields across Europe and North America, where there’s lots of power and land, but this doesn’t work for inference infrastructure.
• The types of hardware used vary more, since inference doesn’t need the fastest, most expensive silicon; it’s way less forgiving and doesn’t need the fancy designer brand to function. There has been a huge wave of enterprise adoption of targeted, mid-tier, accelerator chips and custom silicon, which allows companies to host highly effective inference applications on hardware that costs a fraction of the price of the fancy stuff.
• B2B infrastructure teams are activating automated tiered service level agreements to protect operational margins against any traffic surges. Standard tiers are lower priority, asynchronous enterprise owrkloaws and they are routed to lower cost air cooled clusters, whereas the premium tiers tend to be healthcare financial tasks, needed in real-time, and they are automatically routed to liquid-cooling GPU clusters.

Designing for the bottom line

The infrastructure strategies that won the the fight of the first wave of the genAI boom won’t win the second wave; it’s no longer a viable strategty financialy to fall into overprovisioning for huge clusters so you don’t get FOMO. Enterprises are no longer at the experimental stage, not this far into the AI boom, it’s all about full-scale productions now.

As we head into mid-2026, the advantage belongs to operators who tailor cooling, hardware allocation and data centre location to cost-sensitive realities of inference workloads.