Operations | Monitoring | ITSM | DevOps | Cloud

An error budget is the acceptable amount of unreliability permitted by your SLO over a defined time window. It is not a target. It is not a stretch goal. It is a hard ceiling that, when breached, should trigger a pre-agreed organizational response — feature freezes, postmortems, or infrastructure investment. The formula is blunt: Error Budget = 1 - SLO Target Error Budget (time) = (1 - SLO Target) × Window Duration For a 30-day window: That last number should make you uncomfortable.

Salesforce's research found that 61% of customers would rather solve simple issues on their own than wait for a support agent. The same pattern shows up inside companies, where most IT teams report that password resets, software requests, and ticket status checks make up the bulk of their daily work. So, the demand is clear. The use cases are obvious. Yet most self-service portals quietly stop being used by month six. This guide walks through what's going wrong, and what works.

Welcome to this blog series which explores innovative uses of Ubuntu Core. Throughout this series, Canonical’s Engineers will show what you can build with this Core 26 release, highlighting the features and tools available to you.

Alert fatigue and blind spots develop together. Monitoring stacks that generate noise while missing critical issues may have incomplete coverage or poorly configured alerts. As they grow reactively and without structured coverage assessment, both issues worsen. Teams will often add monitors when something breaks and tune thresholds when alerts become unbearable, but rarely audit their overall setup to see if it works.

Without experiment infrastructure to help you test your LLM applications, every research session starts with the same questions: What have we tried previously? What were the numbers? Which prompt version produced that result? Why did we discard that approach? The answers live in scattered notes, terminal history, and half-remembered conversations. Each handoff between sessions loses context. In practice, iteration can slow down as teams get bogged down in testing and analysis.

NVIDIA H100 GPU cost in 2026: $25K–$40K new, $2–$8/hr cloud rental, used from $6K on eBay. Full buy vs. rent math, 9-provider cloud comparison, and H200/B200 context.

Mistral API pricing in 2026: Large 3 at $0.50/$1.50, Small 4 at $0.15/$0.60, Medium 3.5 at $1.50/$7.50 per MTok. Le Chat Pro $14.99/mo. Full comparison with GPT, Claude, and DeepSeek.

Last week, we launched a major update to Canvas, our investigation workspace. The new Canvas has evolved from an AI co-pilot you chat with to a place where your whole team, human and agent, can work the same problem on the same surface. Auto-investigations begin the moment a trigger, SLO, or anomaly fires. Custom skills encode your team's runbooks so every agent investigates with your team's expertise built in.

NVIDIA's infrastructure roadmap moves fast, and the next major milestone is already here. The NVIDIA Vera Rubin platform is the company's next-generation AI compute architecture, the successor to Blackwell, and it's shaping up to be one of the most significant leaps forward in AI infrastructure NVIDIA has ever shipped. Whether you're planning your next training cluster, scaling inference pipelines, or building the infrastructure to power autonomous agents, Vera Rubin is worth understanding now.

If you’ve been using Icinga 2 for a while, you probably know the built-in checks cover a lot of ground: disk space, CPU, memory, ping. But sooner or later you’ll run into something specific to your setup that no existing plugin handles. That’s where writing your own plugin comes in. The good news? It’s simpler than it sounds. Icinga 2 doesn’t care what language your plugin is written in. It just runs the script, reads the exit code, and displays the output. That’s it.