Operations | Monitoring | ITSM | DevOps | Cloud

Availability describes how often a system is operational and accessible when users need it. It answers a basic question: Can I access the service right now? Availability is often expressed as a percentage over a set time window.

I wanted to share a story of a recent engagement with a law firm to highlight the strategic importance of compliance in today’s legal sector. It started with a single email. A mid-sized law firm received a regulator’s request for evidence following a client complaint. The issue wasn’t malpractice; it was a missed filing deadline caused by a system slowdown. The firm had no audit trail to prove the delay was technical, not procedural.

The Problem: AI workloads are dynamic, unpredictable, and expensive. Data prep can choke your pipeline, training jobs hog GPUs without awareness, and inference, the most latency-sensitive phase, is notoriously hard to scale efficiently. Worse, traditional infrastructure tools treat GPU as a static commodity, ignoring model intent, workload shape, and sharing capabilities.

Most developers spend too much time digging through tabs and switching between tools, rather than actually writing code. According to an IDC survey, only 16% of their week goes to coding, while the rest is lost to what researchers call “organizational inefficiencies” – all those little things that slow teams down.

The concept of digital sovereignty has become increasingly important in today's technology-driven world. As organizations rely more heavily on cloud services and artificial intelligence (AI), they face new challenges in maintaining control over their data and IT resources. At Civo Navigate London, we brought together industry leaders to discuss the topic of digital sovereignty and its implications for the cloud industry.

Enterprises like yours have sunk considerable time and money into trying to stitch together automation. This pattern is always the same: someone buys a variety of IT tools, and a small team of specialists spends months wiring them together. One tool tries to understand human language while another launches a workflow. Between them sits a tangle of mappings, connectors, triggers, and “custom glue” that only one engineer understands. That’s not automation, though.