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In this tutorial you'll learn how to use Uptrace and Grafana together. Uptrace exposes a Prometheus-compatible HTTP endpoint, so you can add it as a data source in Grafana and reuse your existing dashboards without changing metric names or rewriting queries.

Learn the two main ways to get data into Grafana Cloud. In this video, we break down how Grafana Cloud connects to over 150 external data sources (like Salesforce, Postgres, and CloudWatch) where your data stays in place, and how you can send raw telemetry into Grafana’s fully managed databases for logs, metrics, traces, and profiles.

Telemetry Talks explores how OpenTelemetry’s CNCF graduation arrives at a pivotal moment for AI-powered development. Together with Alex Marshalov, we dive into vibe coding, AI agents, and the growing need for observability in GenAI systems — from prompts and token usage to reasoning chains and distributed traces — using the VictoriaMetrics stack and OpenTelemetry as the foundation for understanding the next generation of autonomous software.

A semantic-convention rename (http.method → http.request.method) can silently break your RED metrics — no errors, just gaps in dashboards and alerts. The OpenTelemetry Collector's schema processor fixes it: put it first in your pipeline and it normalizes attribute names no matter what each service emits. Migration mode writes BOTH the old and new names, so you get zero-downtime upgrades while queries keep working.

Our distributed tracing journey from the inception of Tempo to 3.0. Can't comment in the chat? You may need to create a channel. Grafana Cloud is the easiest way to get started with Grafana dashboards, metrics, logs, traces, and profiles.

Everyone is vibe coding products right now. But most applications are missing one crucial thing: Observability. In this video, I talk about: You can literally start this weekend: If you are turning your vibe-coded app into a real startup, observability should not be an afterthought.

Stop editing raw YAML by hand. Discover how to build, validate, and scale complex OTel pipelines visually with the Visual Builder for Coralogix Fleet Management.

You shipped your AI app. But do you have all the answers? Do you actually know which model ran, how many tokens it consumed, or why it stopped? This is what LLM observability gives you, and most AI engineers are skipping it entirely. I built an SOS detection app and used OpenTelemetry to get full visibility into every single call. Token usage, model version, finish reason, and cost per call all in one place, standardised across any provider. Check out the OpenTelemetry GenAI docs in the link below; there is a lot more you can track than you think.