Operations | Monitoring | ITSM | DevOps | Cloud

We’ve seen the same story play out time and time again in numerous SaaS companies: The problem often begins when engineers — with a technical understanding of cloud costs and a deep understanding of how to build robust products — struggle to communicate the actual business impact of their efforts to company leaders.

Fall is here again! These headlines might not be as good as a maple-sugar doughnut and a pumpkin-spiced coffee, but they’re excellent news for FinOps practitioners nonetheless (and they won’t put you into a sugar coma).

Your dashboard shows a latency spike. To investigate it, you copy the query, open logs in a new tab, paste and modify the query, lose your dashboard filters, and repeat for traces. By the time you find the issue, you have 15 tabs open. Starting today, you can click any panel and investigate right there. All your filters and variables carry over. No more tab juggling.

When your database stops accepting connections or your mail server becomes unreachable during business hours, the impact is immediate and costly. Fortunately, the right monitoring strategy can help you detect these TCP connection failures early on, and prevent them from impacting the user experience.

If you’ve been following my public journey with LLMs this year, it probably won’t surprise you to learn that this blog post is an announcement about the general availability of Honeycomb’s hosted MCP server. I want to share a few updates about what’s new in the GA release, discuss some interesting learnings from building it, and share examples of how we’re using MCP internally. First: if you're still in the dark about MCP and AI agents, go read the earlier blogs I linked.

AI agents for SREs were supposed to slash mean time to resolution and eliminate alert fatigue. Instead, most teams got expensive, unreliable tools that burn through tokens without delivering insights. But what if the problem isn't the AI models themselves? Recent benchmarking reveals the real bottleneck: context engineering. When we tested our context engineering approach against conventional methods, the results were dramatic: Scroll down for our benchmark results to see the full comparison.

Uptime Institute’s 2025 Global Data Center Survey shows that capacity planning remains a top challenge for operators. Nearly one-third of vendors identify forecasting future capacity requirements as their customers’ single biggest issue, more than any other concern. Modern data centers face new complexities as digital services expand and hybrid IT architectures shift workloads across on-premises, colocation, and cloud environments.

What’s so attractive about moving to the cloud?

For years, organizations have relied on Application Performance Monitoring (APM) as the backbone of their observability strategy. The idea was simple: collect as many logs, metrics, and traces as possible, then sift through the data to uncover insights. But as applications have shifted to the cloud and become increasingly API-driven, that model has broken down.

The surge of interest in artificial intelligence has opened exciting new doors, but many CIOs are finding themselves in the same bind: lots of promising pilots, but very few at-scale results. Intelligent agents can interpret requests, classify tickets, and even recommend fixes, but unless they are connected into broader workflows, these efforts remain isolated experiments.