Operations | Monitoring | ITSM | DevOps | Cloud

LLM-powered apps are growing fast, and frameworks like the Vercel AI SDK make it easy to build them. But with AI comes complexity. Latency issues, unpredictable outputs, and opaque failures can impact user experience. That’s why monitoring is essential. By using OpenTelemetry for standard instrumentation and SigNoz for observability, you can track performance, detect errors, and gain insights into your AI app’s behavior with minimal setup.

When you export OpenTelemetry metrics to Prometheus, resource fields like service.name or deployment.environment don’t show up as metric labels. Prometheus drops them. To use them in queries, you’d have to join with target_info: This makes filtering and grouping more difficult than necessary. Prometheus 3.0 changes that. It supports resource attribute promotion—automatically converting OpenTelemetry resource fields into Prometheus labels.

Deploying a new service shouldn’t break dashboards. But it happens, usually because metric names or labels aren’t consistent across teams. You end up with traces that don’t link, metrics that don’t align, and queries that take hours to debug, not because the system is complex, but because the telemetry is fragmented. OTel Weaver addresses this by enforcing OpenTelemetry semantic conventions at the source.

You have nested JSON in your logs. When you query body.event.type = "user_action", it fails. Here's why and how to fix it.

Let’s bring that back. Today you’ll learn how to configure high availability for the OpenTelemetry Collector so you don’t lose telemetry during node failures, rolling upgrades, or traffic spikes. The guide covers both Docker and Kubernetes samples with hands-on demos of configs. But first, let’s lay some groundwork.

NestJS applications require comprehensive monitoring to ensure optimal performance and rapid issue resolution. As your application grows—spanning multiple services, databases, and external APIs—understanding what's happening under the hood becomes critical. That's where OpenTelemetry comes in. OpenTelemetry provides vendor-agnostic observability for your NestJS applications through distributed tracing, metrics, and logs.

Modern systems generate enormous amounts of telemetry. The hurdle is collecting clean, connected traces without rewriting code or babysitting a fleet of language agents. That’s why Coralogix backed eBPF from the start. eBPF (extended Berkeley Packet Filter) executes sandboxed programs inside the Linux kernel, without modifying kernel source code. This method allows probes to see every request, at runtime with no instrumentation, and with near zero per‑request overhead.

Here at Grafana Labs, open source has always been core to what we do. So it should come as no surprise that we’re going all in on OpenTelemetry—an open source project that’s quickly becoming an industry standard for vendor-neutral telemetry.

At 3:47 AM, your NGINX logs show a 500 error. Around the same time, your APM flags a spike in API latency. But what's the root cause, and why is it so hard to correlate logs, traces, and metrics? When API response times cross 3 seconds, identifying whether the slowdown is at the NGINX layer, the application, or the database shouldn't require guesswork. That's where OpenTelemetry instrumentation for NGINX becomes essential.