Operations | Monitoring | ITSM | DevOps | Cloud

Key takeaway: Hyperscaler pricing models often penalize e-commerce growth due to unpredictable egress fees and unbounded auto-scaling, but moving to a resource-based allocation model allows teams to treat infrastructure costs as a deliberate business decision rather than a post-campaign surprise. Ecommerce traffic doesn't grow linearly. It spikes, and every spike rewrites your cloud bill.

Every agent demo follows the same arc. The agent calls an API. A deployment triggers. A ticket gets created. The audience is impressed. Then someone asks a real question: "Which regions had the highest order failure rate this quarter, and are any of them linked to vendor SLA breaches?" That question crosses four entity types — orders, fulfillment records, vendors, SLA contracts.

Database changes are inherently complex: coordinating schema updates, managing risk, and avoiding downtime all require care. Even when teams improve how they deliver those changes, governance often remains inconsistent, manual, and reactive. In many environments, governance is treated as a separate layer around deployment. Policies are applied unevenly, approvals become bottlenecks, and audit evidence is assembled after the fact, creating gaps in enforcement and increasing operational risk.

This post is also available in German and in French. Tracking environmental impact can be fragmented, time-consuming, and disconnected from operational data. Beyond simply checking ESG reporting boxes or making sure your company is CSRD compliant, actively monitoring environmental impact is the foundation for building an effective sustainability strategy. At Upsun, we know that measuring progress is the first step toward improvement.

Application delivery has accelerated over the last decade. Modern CI/CD pipelines, automated testing, and cloud infrastructure have already raised the baseline. Now AI-assisted coding tools are compressing timelines further still - developers are writing and shipping code faster than ever.

This is a guest post from Adam Atreides. SQL Prompt's March releases focused on Prompt AI improvements and SSMS 22.4 compatibility.

Let me paint a picture. Your company has rolled out Claude or ChatGPT as the standard AI tool. You've connected MCPs to Stripe, your HRIS, Datadog, your CRM, and Slack. A senior engineer set this up because they needed to answer hard cross-system questions and it works beautifully. Now a marketing intern sits down, opens the same LLM harness with the same MCP config, and types "show me revenue by customer for the last 12 months." They get it.

Custom dashboards in Netdata have always let you pull charts together on-the-fly into a single view. That’s useful, but it’s also limited. In practice, when you’re running an incident or reviewing a service, you don’t just want charts. You want to see the output of top alongside your CPU metrics. You want slow query logs next to your database latency charts.