Operations | Monitoring | ITSM | DevOps | Cloud

Two days in Minneapolis with the OpenTelemetry community, talking about where telemetry pipelines are headed and what the AI wave is doing to them. Two topics dominated everything: AI and cost reduction. Not as separate conversations, either. The more the community talked about AI telemetry, the more the cost question followed right behind it. I joined Diana Todea from VictoriaMetrics and Antonio Jimenez Martinez from Cisco ThousandEyes on the Telemetry That Matters panel.

By benchmarking five GCP machine types across both Logstash and Kafka, LivePerson's observability team found that infrastructure selection (not just pipeline configuration) is one of the highest-leverage cost optimization decisions at scale.

I keep hearing the phrase "code is cheap." I don't know who came up with it. Whoever it was clearly has not seen an Anthropic bill. I get what they mean. The cost of writing a line of code has cratered, AI does most of the typing, you know the rest. Fine. But the phrase is combative in a way that doesn't help anyone, especially the engineers in the room. "Code is accessible" lands better. Less swagger, more honesty. Either way, here's the line my friend Guillaume gave me that finally cracked it open.

When distributed workloads stall because nodes cannot exchange small messages quickly and consistently, the network is the limiting factor. How do you solve that problem? InfiniBand offers one solution. InfiniBand is an interconnect, meaning the end-to-end communication system that links compute, storage, and accelerator nodes. It is implemented as a purpose-built network fabric, the switching and transport layer engineered to deliver high bandwidth and low, predictable latency between those nodes.

"Blackwell sales are off the charts, and cloud GPUs are sold out. Compute demand keeps accelerating and compounding across training and inference, each growing exponentially. We've entered the virtuous cycle of AI." Jensen Huang, CEO, NVIDIA When NVIDIA's CEO makes that statement in a quarterly earnings release, it is not marketing language.

How much of service desk work is problem solving and how much is repeat work that continues every day? Most service desks follow the same pattern daily. Password resets, access requests, software installs, approvals, and routine fixes keep coming in. These tasks are simple on their own, yet together they take most of the team’s time and push important incidents further down the queue. The main challenge is the constant flow of repeat work that reduces time for focused tasks.

You'd think that working at an observability company means everyone knows exactly where to find everything in the data. It doesn't. Especially not on the support team. We're the ones who get the tickets. We're in the telemetry every day trying to figure out what went wrong for a customer, and we do that by pointing Honeycomb at itself. Here's how that actually works, and how it's changed.