In AI model training, idle GPUs — not high prices — are the biggest driver of cost, with poor utilization quietly burning tens of thousands of dollars in wasted compute capacity.

The cost of AI training depends on the choice of venue and infrastructure utilization. This web tool — to accompany the Where to deploy AI training report — calculates workload infrastructure costs for colo, cloud and enterprise data centers.

Benchmarks may produce impressive energy-per-token metrics, but real-world AI workloads are bursty; when throughput drops and GPUs sit idle, joules per token can increase. Do not size AI infrastructure for lab conditions — plan for demand.

AI applications are becoming critical to enterprise operations, but service availability still varies sharply across providers. Inference services should be evaluated not only on model capability, but on operational maturity.

The cost of AI training varies depending on underlying infrastructure and the training approach adopted. This web tool calculates training times and workload infrastructure costs based on configurable data center, infrastructure and model attributes.

Choosing whether to train a model from scratch or fine-tune an existing one comes down to the use case and cost — with hardware utilization remaining an important cost factor.

Although cloud platforms often offer the lowest cost for AI inference, on-premises deployment may be preferable due to application architecture, data locality and control requirements.

The cost of AI inference varies widely depending on deployment model, utilization and hardware. This costing tool compares on-premises, colocation and managed AI platforms on a like-for-like basis.

As AI adoption spreads, most data centers will not host large training clusters — but many will need to operate specialized systems to run inferencing close to applications.

In 2026, enterprises will be more realistic about their use of generative AI, prioritizing simple use cases that deliver clear, timely value over those more innovative projects where returns — and successful outcomes — are less assured.

Cloud sovereignty is often treated as binary choice, but, in reality, it is a spectrum shaped by law, operations, technologies and supply chains. This framework explains the differences between sovereign public cloud options.

Simple arithmetic shows that newly constructed, large-scale private data centers with high occupancy rates can sometimes undercut colos on unit costs, but in most cases colos remain significantly cheaper.

The Azure outage highlights a blind spot in resiliency planning. It is not only cloud compute that can fail - shared global network services such as DNS and CDNs can disrupt access to systems anywhere, including on-premises.

Financial institutions are embracing public cloud for some mission-critical workloads, and using it as a launchpad for AI development.

A major outage at AWS's Virginia region took many global organizations offline. What can enterprises do to reduce or negate the impact of such widespread outages?