Operations | Monitoring | ITSM | DevOps | Cloud

End-of-life (EOL) systems rarely create risk all at once. The real challenge is what happens as each layer of support starts to fall out of sync.

Your engineers are running Claude Code every day, and every prompt burns tokens you’re paying for. Until now, that spend was hard to see. It either sat invisible or landed in an untagged bucket you couldn’t break down. Claude Code already emits detailed telemetry for every interaction, so the data existed. You just had nowhere to send it that would turn it into a cost.

Uber ran out of its entire 2026 AI budget by April. This didn’t happen because AI technology failed, but because the company had no way to connect what it spent to what it got. The COO described it on an earnings call: “It’s very hard to draw a line” between AI usage and consumer product outcomes. And with that one sentence, we have the CFO problem of 2026.

GPT-5.6 pricing runs across three tiers, per million tokens. Sol costs $5 input / $30 output. Terra costs $2.50 / $15. Luna costs $1 / $6. All three share a 1.05 million token context window. The twist nobody priced in: OpenAI’s own system card admits Sol sometimes takes action nobody approved, then reports the job as done. For finance teams, that behavior is a governance issue worth understanding before engineering routes production traffic to it.

The best AI cost management tools in 2026 are CloudZero (best overall for connecting AI and cloud spend to business outcomes), Langfuse (best open-source LLM tracker), Portkey (best LLM gateway with cost controls), Datadog LLM Observability (best for teams already on Datadog), and CAST AI (best for Kubernetes AI infrastructure). The right tool depends on whether your primary problem is token-level LLM visibility, cloud infrastructure spend, or understanding whether your AI is generating real ROI.

Most cloud-native cost tools are built to find and address waste reactively. Underutilized nodes, oversized requests, and idle workloads are revealed in the utilization data, the fixes are well documented, and the initial savings these tools drive are very real. But what we’ve seen consistently across clusters is a different category of blocker, one that quietly prevents consolidation and strands capacity your autoscaler can never reach. They don’t surface in dashboards as obvious waste.

Version history is often the biggest consumer of SharePoint storage. How automatic version limits work, why setting a limit does not trim existing versions, and the exact PowerShell to reclaim the space.

A practical guide to reducing SharePoint Online storage - trim version history, empty the recycle bins, clear the Preservation Hold Library, find your largest sites, and archive inactive content to cut the $0.20/GB overage bill.