Operations | Monitoring | ITSM | DevOps | Cloud

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.

Ask a SaaS team how they measure cloud efficiency, and you’ll hear familiar things. Total cloud spend. Average cost per customer. Maybe a breakdown of spend by service. All useful, but rather wobbly. Now ask, “What does it cost you to serve one more customer?” That’s when the room goes quiet. And that’s often where cloud economics gets really wobbly. Because that number, your marginal cost, is what actually determines your margins. Not your total cloud bill.

Some log sources bundle multiple events into a single record before shipping them. This is common with VPC flow logs, CloudWatch exports, and certain Windows endpoint collectors. While this batching approach is efficient for transport, it creates challenges when you need to filter, search, or correlate individual events. When a log record contains an array of 47 events, your analytics tool sees one entry instead of 47 distinct records.