Operations | Monitoring | ITSM | DevOps | Cloud

For years, moving workloads to the cloud has been driven by scalability, flexibility and the desire to reduce the capital expenditure associated with running a data center. In 2026, however, there is another factor entering the conversation: the rising cost of buying and refreshing physical servers. The question is now whether rapidly increasing hardware prices are enough to push organizations that might previously have refreshed their on-premises infrastructure towards cloud alternatives.

Worldwide AI spending will reach $2.59 trillion in 2026, up 47% from 2025, according to Gartner. Yet only 37% of organizations report any earnings impact from AI, McKinsey finds. These AI statistics cover what companies spend, what AI costs to run, and the ROI they're actually getting. That gap between the two headline numbers is the story of AI in 2026.

CloudZero’s own AI Economics Pulse for September found the 75th percentile of its 430-company customer panel crossed 10% of its cloud bill on AI for the first time in August. Gartner’s latest survey found only 22% of organizations have scaled AI successfully and 11% don’t know what their own function spent on it last year. CJ Gustafson showed what that gap looks like on an actual invoice this week.

Blagomir Petrakiev, Data Centre Services Engineer in Maidenhead, made the move from automotive customer service to data centre operations after studying for his Cisco Certified Network Associate (CCNA) certification. In this Q&A, Blagomir shares what it’s like working behind the scenes at Pulsant, from infrastructure checks and remote hands tasks to learning new skills and building a career in digital infrastructure.

Internet Performance Monitoring detected the AWS outages before AWS acknowledged them. Learn why independent, outside-in monitoring is critical to faster incident response.

Consider a platform team that spends four months building a thorough migration plan. Its pilot, a stateless order-status API, runs on AWS within three weeks. Six months later, that API is still the only workload in the cloud. In this scenario, the blockers are not exotic technical failures.

Picture two teams running the same million input tokens through the same model. One team’s tokens are cache hits, queued through the batch API. The other team’s are fresh, sent live. On a current-generation OpenAI model, cached input runs about a tenth the price of a fresh token, and batch processing cuts whatever’s left in half. Stack the two: at a list rate of $2 per million tokens, one team’s bill comes to 10 cents, the other’s to two dollars.