Operations | Monitoring | ITSM | DevOps | Cloud

AI Anomaly Detection: Catch AI Cost Surprises Before They Kill Margins

Consider this: traditional cloud cost monitoring was like checking your fuel gauge once a month — after the trip was already over. That model worked when infrastructure scaled slowly. You provisioned resources predictably and paid for stable, linear usage. AI breaks that model. Today, AI costs behave like a high-performance engine with a hypersensitive throttle. A small input, like a prompt change or a single power user, can dramatically increase your fuel burn in seconds.

A cleaner, customizable Bitbucket navigation is here

Last month we shared that a new navigation system is coming to Bitbucket, and we know many of you have been eager to see what it looks like. Today, we’re happy to share that the new navigation is available for to all Bitbucket users. This article covers what’s changing in Bitbucket, when it’s happening, and how you can share feedback with us.

SRE Report 2026: What surprised us, what didn't, and why the gaps matter most

This is the eighth edition of the SRE Report. Eight years of tracing reliability's arc, from uptime obsession to experience, from toil to intelligence, from systems to people. This year's report is also the first since Catchpoint joined LogicMonitor. We want to acknowledge their support in keeping this work going. They get what this report means to the reliability community, and that matters. We made a deliberate choice this year to say less.

The SRE Report 2026: Defensible Ns

You shouldn’t have to understand the care behind this report, unless it’s missing. For the past eight years, this research has focused on all things related to reliability and resilience. How systems behave under stress. How teams respond when things break. And how the practices continue to evolve. Reaching the eighth edition of The SRE Report attests to that and gives me pause. You can read the full report here and you can find a summary of the key findings here.

An introduction to GPU time-slicing

GPUs are no longer a niche component. Gamers know them for immersive graphics, workstation users rely on them for balanced performance, and in the age of AI, GPUs have become one of the most in-demand resources in modern infrastructure. They are also expensive. That reality creates two immediate constraints, for individuals and enterprises alike: GPU-backed instances should be provisioned deliberately, and once provisioned, they should be used efficiently.

Observability That Works: Understand System Failures and Drive Better Business Outcomes

Modern systems don't fail because engineers lack skills; they fail because teams can't see why systems are failing at all or can’t see why they’re failing fast enough. Often, the problem isn't a lack of tools — it's a lack of clear, connected visibility across data, teams, and systems. This is where observability transforms how organizations operate. It's no longer just about keeping systems running.

Top Distributed Tracing Tools in 2025: Updated Market Review with Cost Comparison

The distributed tracing landscape has evolved from “observability add-on” to core production infrastructure. In 2026, distributed tracing is no longer optional for engineering teams operating microservices, Kubernetes, or AI-driven workloads. It is now tightly coupled with incident response, cost optimization, and AI-assisted debugging.

Why Infrastructure Stability Is Critical for Reliable DevOps Pipelines

Automation in DevOps helps teams move code from a commit to production faster. But it only works when the infrastructure is reliable and consistent. If servers fail, configurations drift, or scaling behaves unexpectedly, even a well-built pipeline can break. Stable infrastructure is what lets teams deploy many times a day with confidence instead of spending hours fixing failed releases. Often, the biggest difference between strong DevOps teams and struggling ones is how dependable their infrastructure is for continuous delivery.

How to Identify and Eliminate Wasted Ad Spend Using Performance Signals

Efficient ad spending is a critical component of any business's marketing strategy. As digital marketing grows increasingly complex, advertisers must navigate multiple platforms and channels to reach their target audience. With this complexity comes the risk of wasted ad spend-money spent on campaigns that fail to reach the right people or generate meaningful results. By understanding and leveraging performance signals, businesses can optimise their advertising, reduce waste, and maximise ROI.