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OpenObserve vs Grafana: An Agent-Run Observability Bake-Off

· 3 min read
Simple Tech Guides

We’re building an observability test bed and using AI agents to stand up the tooling. This article is the plan and early results for a head-to-head comparison between OpenObserve and a Grafana-based stack.

The goal isn’t to debate feature checklists. It’s to measure how quickly a real team (or a real agent) can get from zero to useful signals.

The Demo App: BrewQueue​

To make the comparison realistic, we built a small app with multiple services and real data flow:

  • React frontend
  • FastAPI backend
  • Postgres database
  • Redis + Celery workers for async jobs
  • Kitchen dashboard workflow

That mix gives us synchronous requests, background tasks, and database activity. If an observability stack can’t make that feel clear, it’s not doing its job.

The Environment​

BrewQueue runs on a single-node K3s cluster with an ingress in front. The goal is a compact, repeatable setup that can be torn down and rebuilt fast.

We instrumented the app with OpenTelemetry so we can swap backends without changing code. This is the foundation for comparing tools fairly.

What We Mean by “OpenObserve vs Grafana”​

OpenObserve positions itself as a unified platform for logs, metrics, and traces. It ingests OTLP data directly, which fits our OpenTelemetry-first approach.

Grafana is the query and visualization layer. In practice, it’s usually paired with backends like Loki (logs) and Tempo (traces), plus a Prometheus-compatible metrics store. That means the comparison is:

  • Unified platform (OpenObserve)
  • Modular stack (Grafana + dedicated backends)

The difference isn’t just features. It’s operational complexity, setup time, and how much glue you need to get value.

The Agent Test​

We’ll ask an agent to do the full setup from scratch and score each stack on:

  • Time to first usable trace
  • Time to first useful log query
  • How many manual steps were unclear or missing
  • Whether traces connect frontend → API → DB → worker cleanly
  • How easy it is to jump from a trace to the logs and metrics that explain it
  • How many components must be running before the stack feels useful

This is a real-world adoption test. Most teams are under time pressure with incomplete context. If an agent can’t do it quickly, it’s a sign the docs or defaults need work.

OpenObserve Signup Notes (Feb 3, 2026)​

We successfully created a new OpenObserve Cloud account during setup. A few surprises worth noting:

  • The “Create Account” path is hidden behind the email login flow. You have to click “Continue with Email,” then find “Create Account” on the login form.
  • Signup requires an email OTP. The token field is read-only until you click “Get OTP,” which is easy to miss if you’re automating the flow.

These aren’t blockers, but they do add a couple of extra steps an agent (or a human) has to detect and handle.

What We’re Looking For​

This comparison is about friction and clarity, not just raw capability:

  • Do you get a correct setup on the first try?
  • Are the defaults safe for small teams?
  • Does the UI guide you toward answers or just raw data?
  • How much tuning is needed before it feels “production ready”?

Next Steps​

We’ll run the agent setup for both stacks, document the steps, and publish the time-to-value results. If you’re evaluating observability tools, this bake-off should map more closely to your first week than a polished demo does.