Operations | Monitoring | ITSM | DevOps | Cloud

AI cost management is the practice of tracking, allocating, and optimizing the cloud infrastructure costs tied to building, running, and scaling AI workloads. It differs from traditional cloud cost optimization because AI infrastructure behaves differently at every layer of the stack. The biggest problem isn’t overspending. It’s that most organizations can’t see where their AI spending is going.

Most teams already know a lot about production. They have dashboards. They have traces. They have alerts. They have enough telemetry to explain what happened after an incident and enough graphs to argue about it for the rest of the week. Then they go to test a change and start from scratch. The integration tests hit a hand-written mock that returns {"status": "ok"}. The load tests replay a CSV somebody exported months ago. Staging is close enough to production right up until it matters.

On March 24, 2026, the AI open-source ecosystem was impacted by a critical supply chain attack involving the widely used Python package LiteLLM. Attackers compromised the LiteLLM PyPI distribution pipeline and published malicious versions (notably in the 1.82.7-1.82.8 range), embedding a multi-stage payload designed to steal credentials and execute remote code.

Ahead of his participation in techUK’s Digital Transformation from the Edge to the Cloud event, we sit down with Pulsant CTO Mike Hoy to ask him how distributed cloud and edge are reshaping the digital ambitions of UK businesses. Q: So Mike, what are the main issues firms face in designing/redesigning their digital infrastructure in 2026?

Every incident arrives with a payload, and that payload usually tells you far more than whether something broke. It points to which service is affected and how serious the issue looks. It also carries context about which customers are on the receiving end of that failure. The service name, severity, customer context — all of it can feed directly into routing decisions. This guide explores how to read those parts of the payload and use them to route incidents automatically.

Employee offboarding looks simple from the outside. Someone gives notice, HR processes the paperwork, and IT handles the rest. In practice, "the rest" is where things get complicated. A single departure can touch dozens of systems, and the handoffs between them (between HR and IT, between tools, between teams) are exactly where access stays on longer than it should, steps get missed, and audit findings show up months later.

In December 2025, we announced our intention to introduce pricing for self-hosted runners so we could provide stronger support and keep investing in new features and ongoing improvements. You’ve told us that having a free option is important. As a result, we’re introducing a new operating model that lets you continue using self‑hosted runners for free with the option to upgrade to a paid premium runners tier as your needs grow.