Operations | Monitoring | ITSM | DevOps | Cloud

For developers building AI applications, training models, or running inference pipelines, the GPU cloud market in 2026 has never offered more choice - or more complexity. Picking the wrong platform means overpaying, dealing with availability problems, or battling infrastructure that slows you down rather than accelerating your work.

This is a guest post from Laura Copeland. Key insights from a fireside chat with Chris Yates. Part 1. Choosing the right database monitoring vendor isn’t just a technical decision, it’s a strategic one that affects your teams, your estate, your growth plans, and the culture of your organisation. It’s also a personal one if you’re a DBA. Something as critical as your monitoring system will shape your day‑to‑day work, and, in many cases, how well you sleep at night.

Selecting the right observability platform has become a strategic priority for enterprises operating at scale.

AI implementation costs range from $5,000 for pilots to $500K+ for enterprise systems. Get a full breakdown of AI development, infrastructure, and operational costs for 2026.

Compare cloud GPU pricing across AWS, Azure, and GCP for AI workloads. See H100 and A100 costs per hour, hidden cost drivers, and how to track real GPU spend.

We look at the hidden economics of elastic scaling for AI inference, the scaling decisions that affect your cost per inference, and what you can do now to optimize your AI ROI.

Picture this: It's 2 a.m. Your phone is buzzing. A new feature just went out to your entire user base, and conversion rates are tanking. Your on-call engineer is digging through logs, your Slack channels are on fire, and you’re left wondering, Why didn't we just test this first? Every CTO has a version of this story. And most of them have quietly vowed never to repeat it.