Operations | Monitoring | ITSM | DevOps | Cloud

I was looking at our Claude Code spend in the Anthropic console the other day. Aggregate cost, aggregate tokens — no breakdown by developer, no breakdown by session. I knew my Hackathon team had been using it heavily on building out new features for the OpenTelemetry Distro Builder. But heavily how? I had no idea. Turns out Claude Code has been emitting OpenTelemetry signals the whole time. Per-session cost, token counts, every tool call it makes on your codebase.

We’re excited to announce new native Bindplane destinations for the VictoriaMetrics ecosystem. It’s now easier to collect, process, and route OpenTelemetry metrics, traces, and logs at scale. You can directly connect VictoriaMetrics’ high-performance storage engines to Bindplane’s vendor-neutral, OpenTelemetry-native telemetry pipeline.

Modern incident response doesn’t fail due to a lack of alerts firing. It fails because teams are overwhelmed by the sheer volume and the lack of context around them. Today, most observability and monitoring platforms generate a flood of alerts. Each one is triggered independently, even when they are symptoms of the same issue. Engineers are left trying to reconstruct the full picture while jumping between dashboards, Slack messages, and tickets.

The Tier 2 SOC Analyst or the Incident Responder (often hailed as the "Sherlock Holmes of the network") faces an increasingly complex and relentless digital landscape. In a world where analysts are being overwhelmed by alerts, held back by fragmented, manual tooling and inefficient workflows, incident responders are charged with the critical task of identifying, analyzing, and mitigating security threats.

In today’s highly interconnected systems, the subtle relationships between services are rarely obvious. Modern, complex architectures generate telemetry that functions less as “flashing signs” and more as faint “breadcrumbs” to be followed across a vast network of signals. In 2025, about two-thirds of outages involved third-party systems like cloud platforms and APIs.

Model Context Protocol (MCP) servers act as adapter layers between clients and AI based workloads. MCP installation into an IDE, such as Cursor, brings a wealth of information directly into the developers primary tool, minimizing context switching and, especially in the world of observability, bringing telemetry closer to the code. MCP is not without its limits. These limits initially seem trivial, but in time, some of the inherent limitations to a basic MCP implementation become apparent.

Observability data arrives as a flood of signals, full of potential, but rarely consistent. Error messages and debug logs can reveal what businesses care about: reliability, customer experience, and revenue. The challenge is turning raw technical events into information the whole organization can act on. Many observability systems store data first and structure it later, forcing teams to rebuild context in dashboards and queries, often duplicating logic across services.