Operations | Monitoring | ITSM | DevOps | Cloud

Leaders in every industry are investing heavily in AI. Shocking, I know. Operations teams are modernizing infrastructure and automating workflows while boards are asking for faster returns. And yet, for all the investment, one question still lingers: where’s the value? The truth is that most enterprises have a translation problem, not necessarily ‘just’ a visibility problem. Executives see AI as a growth strategy, but IT sees it as operational complexity.

In the era of cloud-native architectures, Site Reliability Engineering (SRE) has matured from a discipline focused purely on uptime to a sophisticated practice of efficient reliability. The key driver for this evolution is an undeniable truth: cloud spend has become intrinsically linked to system stability.

We benchmarked Diskless Kafka (KIP-1150) with 1 GiB/s in, 3 GiB/s out workload across three AZs. The cluster ran on just six m8g.4xlarge machines, sitting at <30% CPU, delivering ~1.6 seconds P99 end-to-end latency - all while cutting infra spend from ≈$3.32 M a year to under $288k a year. That’s a >94% cloud cost reduction. Extending Apache Kafka does come with an explicit tax.

On December 4th, 2025, we discovered an issue where the person shown as on-call on the dashboard didn’t match who was scheduled in the calendar. When we started investigating, we learnt that this only affected schedules with weekly rotations or weekly layers combined with custom timings.

Instead of giving you yet another chatbot, we built AI straight into the parts of Sentry where teams lose time, turning your existing data into instant context — and it’s now available to all Sentry users.

Modern systems generate enormous volumes of operational data. Yet, most incident workflows still treat every outage like a one‑off fire drill: an alert fires, responders scramble, the issue is resolved, the status page goes green—and the organization learns almost nothing from the experience. Meanwhile, the same patterns quietly repeat in code releases, logs, traces, and support tickets until they erupt into the next ‘unexpected’ incident.

Modern web applications rely on seamless user experiences, fast load times, and reliable performance across every device and region. Browser monitoring tools make these features possible by tracking how real web browsers interact with your site revealing issues long before users notice them. To ensure your monitoring setup captures everything that matters, here are the five essential metrics every browser monitoring solution must track.

Unlock peak load balancing performance with HAProxy! In this blog post, we'll explore how HAProxy intelligently harnesses the power of modern multi-core CPUs while navigating challenging architectural complexities like NUMA. Discover how HAProxy leverages optimized multithreading and provides automatic CPU binding to deliver both unparalleled efficiency and speed, ensuring your load balancing is faster than ever.

November was another big month for Harness AI, with new capabilities that deepen our work with AWS, bring AI-native automation to the database, and keep our model stack on the cutting edge across the SDLC.

AI speeds delivery but expands risk. Teams need context, verification, behavior detection, and learning to stay secure by default. Software delivery has been accelerating for more than a decade, and the arrival of AI has pushed us into an entirely new velocity class. Code generation, configuration scaffolding, infrastructure suggestions, remediation hints, and deployment decisions now involve AI. It participates in every stage of the delivery pipeline. On the surface, this feels like progress.