Operations | Monitoring | ITSM | DevOps | Cloud

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.

The first half of 2026 reinforced a key idea about Cloud and SaaS reliability - dependency risk. IncidentHub tracked 30,246 outages across 1,082 providers between January and June 2026. May was the busiest month, with 6,070 incidents. Cloud providers led in the total number of outages (4,723), followed closely by developer tools (4,589).

What’s in this article? Modern enterprise workloads are demanding more from IT infrastructure than ever before. AI platforms, advanced analytics, virtualisation clusters and high-performance computing (HPC) environments all require significantly more compute power than traditional business applications. Many organisations are discovering that their existing on-premise facilities were never designed for these demands.

If you pull cost data from a CloudZero Snowflake Data Share, a request for just a few days of data could be surprisingly slow and expensive. Because of how the data was stored, Snowflake had to read far more than the days you asked for. You paid for that in query time and credits.

Hosting an MCP (Model Context Protocol) server on Upsun lets AI IDEs like Cursor, Codex, Claude Code and Windsurf reach real infrastructure context (database schemas, service logs, environment variables), closing the gap between local coding assistants and your cloud environment.

On July 16, 2026, AWS experienced a disruption in its CloudFront service, which affected a large number of websites and applications. The outage was caused by a configuration loading failure in CloudFront's VPC Origins feature. This was AWS's most widely-felt outage after last year's outage on October 20th, which caused widespread damage.

Managing cloud, AI, and SaaS costs means answering a steady stream of questions from finance, leadership, and engineering teams. What changed? Which team owns the spend? Was an increase expected? Are we still on track against the budget? When each answer requires moving between dashboards, filtering cost data by team or service, or manually correlating billing data with observability data, it can slow down investigations while costs continue to rise.