Operations | Monitoring | ITSM | DevOps | Cloud

If you've ever rolled out Grafana Cloud to a growing engineering organization, this pattern may sound familiar: Everything feels simple at first. You invite a few teammates, give them access, and dashboards start appearing. Then the team grows. Then the number of stacks grows. Over time, a model that once felt fast and empowering starts to feel risky, difficult to understand, and even harder to undo. This post is about avoiding that moment.

Mobile app users form opinions quickly. A slow or inconsistent startup experience can frustrate them before they reach the first screen, increasing the likelihood that they abandon the app or fail to complete key actions such as signing up or making a purchase. However, app teams often lack reliable signals that explain why startup performance varies, making it difficult to improve the user experience.

Modern incident response doesn’t fail due to a lack of alerts firing. It fails because teams are overwhelmed by the sheer volume and the lack of context around them. Today, most observability and monitoring platforms generate a flood of alerts. Each one is triggered independently, even when they are symptoms of the same issue. Engineers are left trying to reconstruct the full picture while jumping between dashboards, Slack messages, and tickets.

Software delivery is no longer a back-office function; it’s the heartbeat of the modern enterprise. While a 99.9% uptime SLA for essential software delivery services works for many, the acceleration of software velocity has made the “three-nines” benchmark a possible liability. For high performing software organizations, and those delivering critical services, nine hours of annual downtime represents a dangerous gap in productivity and security.

We’re excited to announce new native Bindplane destinations for the VictoriaMetrics ecosystem. It’s now easier to collect, process, and route OpenTelemetry metrics, traces, and logs at scale. You can directly connect VictoriaMetrics’ high-performance storage engines to Bindplane’s vendor-neutral, OpenTelemetry-native telemetry pipeline.

I recently wrote about a project I created on AWS Aurora PostgreSQL where I'm capturing APRS data from a radio. I focused on the ease of use, getting a database, some Lambda Functions, and a few schedulers working together with a web page. It was easy. However, I'd like to focus on a slightly different area now, performance.

At MWC Barcelona 2026, coRAN Labs and Canonical are presenting a working demonstration of a cloud-native, quantum-safe telecom platform for 5G and beyond 5G networks. This is not a conceptual exercise. It is a full 5G System (5GS) deployment with post-quantum cryptography embedded across the stack – from radio access to core, from transport interfaces to orchestration and public key infrastructure (PKI).

When a major incident strikes, every second becomes a decision point. Service degradations accelerate. Customers feel the impact. Revenue and reputation hang in the balance. In these moments, IT teams do not need abstractions or probabilistic guesses. They need guidance they can validate and decision paths they can explain with confidence long after the incident is resolved. Hybrid environments are too complex for intuition, and the repercussions of an incorrect action are significant.

In my time at Ivanti, I've witnessed firsthand how AI acts as a force multiplier across enterprise organizations. When deployed strategically, AI accelerates decision-making and operational execution at scale in a way that teams simply can't sustain manually. However, without clear and enforceable AI guardrails, implementing AI opens organizations up to serious new risks.

This post is also available in German and in French. The 2026 AI landscape has shifted from "Can we build it?" to "How much will it cost to run it?" For CTOs and engineering leaders, the challenge is no longer just model performance: it is the underlying infrastructure sprawl that silently erodes margins. When AI workloads scale, they often inherit the inefficiencies of legacy cloud models: over-provisioned instances, fragmented data pipelines, and a lack of unified context.