Operations | Monitoring | ITSM | DevOps | Cloud

There's a version of cloud strategy that looks like a decision and a version that looks like drift. Most enterprises are living the second version. AWS won an early workload because the team knew it. Azure followed the Microsoft enterprise agreement. GCP came in through a data science team that preferred its ML tooling. Nobody chose concentration; it just accumulated.

How to recover deleted files from the SharePoint recycle bin - both stages, restore steps, PowerShell, the 93-day limit, and how to get back files that are already gone.

Until now, allocating by account name meant doing the work yourself: hand-building a custom dimension that mapped every twelve-digit AWS ID to a readable name, then maintaining it by hand in CostFormation. That mapping is fragile. Rename a definition or edit the wrong line and dashboards that depended on it quietly break. Now the account name is built in for accounts connected through AWS CUR 2.0.

Ask any engineering leader how AI has changed their team, and you'll get an optimistic answer. Ask them to prove it with a number, and most go quiet. That gap is the story of 2026. Individual engineers are faster than they've ever been. Teams, in a lot of cases, aren't. We talked to our own engineering team, the people who've spent the last year building and shipping Upsun Dispatch and living through exactly this shift, and the same tension came up again and again.

When we launched DNS provider sync with six providers, one name came up in nearly every "what about..." email: Amazon Route 53. That makes sense. Route 53 runs DNS for an enormous share of production infrastructure, and it does not support zone transfers — so until now, monitoring a Route 53 zone meant relying on autodiscovery's educated guesses. Today that gap closes.

How to find, search and retain the SharePoint audit log in Microsoft 365 - the Purview audit search, Search-UnifiedAuditLog PowerShell, retention limits, and where the native audit trail falls short.

When platform providers talk about autoscaling, they usually mean one thing: application autoscaling, adding or removing web instances based on traffic. The pattern is well understood. Define a metric, set a threshold, and let the platform act on it. Most platforms still leave database capacity as a separate, mostly manual problem. Teams either pre-provision for the worst hour of the worst day, upgrade plans on a calendar, or wire together their own scaling logic on Kubernetes or RDS.

For years, the cloud migration business case was framed around one comparison: “Will AWS be cheaper than our data center?” That question still matters, but it is no longer where the value is. The 2017-2024 wave of mass migration is largely complete. Most enterprise workloads are already in the cloud - often in a lift-and-shift state: oversized instances, commercial-OS BYOL, on-prem-shaped network designs, and legacy frameworks that block the next step.

A design partner told us the AI Explorer table needed a way to get data out so it could be saved and shared elsewhere. Now you export the whole view in one click, cost, tokens, cache, and model count all included. The values come through as clean numbers, not text you have to scrub. It’s the same pattern as Explorer, so there’s nothing new to learn.