Operations | Monitoring | ITSM | DevOps | Cloud

When Anthony Leung talks about FinOps, he’s speaking from operating at real scale — not theory. As VP of Engineering Platforms and Security Research at Forcepoint, he led a transformation that cut cloud spend in half while improving availability, and built a culture where engineers own their economics.

AI is the new frontier for FinOps maturity. It introduces fresh spend patterns and new opportunities for value. As GPUs, inference, and retraining reshape costs, FinOps maturity grows through visibility, forecasting, and shared mindset about how these workloads drive business impact. In this 2025 post, I gave my guidelines for implementing AI tagging to give business context and clarity to vague AI invoices. Now, I’m sharing the next level up: how to drive FinOps in AI with AI.

Here’s the uncomfortable truth: most companies do not have an AI problem. They have a delivery problem wearing an AI costume. MIT’s Project NANDA research has been widely cited for a brutal headline statistic: roughly 95% of corporate generative AI pilots fail to produce measurable business impact or returns, while only about 5% break through to meaningful outcomes. (Yahoo Finance) The models are impressive. The demos are dazzling. The budgets are real.

Deployments are one of the few moments where software development still feels risky. Teams may have tests, a staging environment, and careful review processes, yet the final step still carries uncertainty. Will this change behave the same way in production? Will it interact cleanly with existing data, traffic, and infrastructure? Will it introduce regressions no one anticipated? Preview environments exist to reduce that uncertainty.

AI assistants are quickly becoming a primary interface for how people interact with software. Developers ask them how to integrate APIs. Users ask them how products work. Buyers ask them how tools compare. Increasingly, the first explanation someone receives about your product does not come from your website, your documentation, or your sales team. It comes from an AI assistant. That shift has an important consequence that many organizations are only starting to notice.

Backstage alternatives fall into three real choices: build and own a framework, buy a fully managed IDP product, or choose a hybrid path that reduces maintenance but keeps Backstage at the core. The trade-off is not "free vs paid" but engineering headcount, governance maturity, time to value, and how actionable your portal is across CI/CD, IaC, and environments. The best commercial IDPs go beyond catalog and documentation.

We’ve all seen it happen. A DevOps initiative starts with high energy, but two years later, you’re left with a sprawl of "fragile agile" pipelines. Every team has built their own bespoke scripts, security checks are inconsistent (or non-existent), and maintaining the system feels like playing whack-a-mole. This is where the industry is shifting from simple DevOps execution to Platform Engineering.

Imagine pulling up to a gas pump, inserting your credit card, and having the display on the pump say “denied”. You call your credit card company, and they say, “Oh, we don’t know, maybe it’s the merchant’s fault, or the card reader is bad…, we can look into it and get back to you in a few weeks.” Most of us would be pretty upset with that response.

Cybersecurity refers to the processes and technology used to protect information technology networks, data, people, servers, endpoint devices and other IT-related systems from cyberattacks. The need for this protection has never been greater. All organizations (in both private and public sectors) now exist in a threat landscape that allows attacks against their IT infrastructure.

If you’ve ever onboarded a teammate at 4:57 PM on a Friday (or offboarded one at 4:58 PM… ), you know the pain: keeping notification contacts and groups up to date is work. With the Icinga Notifications REST API, you can automate that and avoid drift.