Hey folks! 👋 As someone who’s been knee-deep in Langfuse for tracking LLM experiments and traces, I’ve been thinking a lot about how we can get a *complete* observability picture when running it on Kubernetes. Langfuse gives us fantastic visibility into prompts, traces, and evaluations, but what about the underlying infrastructure, resource consumption, and the health of the supporting services (like Postgres and Redis)? For that, we need a robust, open-source observability stack that integrates seamlessly with K8s.
After testing a few combinations over the last several months, I keep coming back to a stack built around **Grafana, Prometheus, and Loki**. Here’s why I think it’s the best fit for most Langfuse-on-K8s deployments:
* **Unified Dashboarding:** Grafana lets you build dashboards that combine Langfuse metrics (via its API or exported metrics if you instrument it) with system metrics from your nodes, pods, and the databases. You can see a spike in token usage right alongside a spike in Postgres CPU.
* **Metrics Collection:** Prometheus is the de facto standard for K8s metrics. It’s trivial to set up with the Prometheus Operator, and it scrapes everything. You can define custom alerts for when your Langfuse queue length grows too high or when Redis memory usage is critical.
* **Log Aggregation:** Loki is a game-changer for logs. It’s purpose-built for K8s and is much more resource-efficient than traditional ELK for this use case. You can correlate Langfuse application logs (e.g., "trace ingestion slowed") with the k8s pod logs and events.
* **The Ecosystem:** It’s a mature, well-integrated suite. Tools like `kube-state-metrics` and various exporters (for Redis, Postgres, etc.) mean you can monitor every layer of your stack without jumping through hoops.
Here’s a quick snippet of a Prometheus `ScrapeConfig` to get Langfuse application metrics if you expose a `/metrics` endpoint (you'd need to add instrumentation, but it's a common pattern):
```yaml
- job_name: 'langfuse-app'
kubernetes_sd_configs:
- role: pod
relabel_configs:
- source_labels: [__meta_kubernetes_pod_label_app_kubernetes_io_name]
action: keep
regex: langfuse
- source_labels: [__address__, __meta_kubernetes_pod_annotation_prometheus_io_port]
action: replace
regex: ([^:]+)(?::d+)?;(d+)
replacement: $1:$2
target_label: __address__
```
The main pitfall to avoid is thinking of Langfuse observability in isolation. The real power comes from linking Langfuse-specific events (a degradation in trace quality, latency in span ingestion) with platform-level signals (node pressure, network latency between pods). This stack lets you do that natively.
Has anyone else tried a different open-source combo, like OpenTelemetry Collector with Jaeger and Tempo? I’d love to compare notes on the setup complexity and the kind of correlated views you can build. Especially interested if you’ve managed to pipe Langfuse traces directly into Jaeger!
bw
Automate all the things.
I'm a platform lead at a mid-sized AI consultancy, and we've been running Langfuse in production on our own Kubernetes cluster for about a year to track client LLM projects. Our stack includes the Langfuse Helm chart, Postgres, Redis, and the observability suite I'll mention.
Here's my breakdown based on running both SigNoz and the Grafana stack in this context:
* **Deployment Effort:** The Grafana/Prometheus/Loki (GPL) stack, especially via the kube-prometheus-stack Helm chart, is a known quantity. You can have it collecting pod metrics in an afternoon. SigNoz, while also Helm-based, felt like a more integrated but newer package; its one-click install was easier, but I spent extra time tweaming the OpenTelemetry collector configuration to get Langfuse spans where I wanted them.
* **Cost at Scale:** Both are open-source, but your real cost is hosting and maintenance. For us, the GPL stack adds about 20-25% more memory overhead across the monitoring namespae. SigNoz was lighter (closer to 15%) but required us to run an external ClickHouse cluster for long-term storage, which changed the math. If you're under 50GB of metrics/logs daily, SigNoz's bundled storage is fine.
* **Integration Nuance:** Prometheus automatically scrapes your Langfuse pods. To get Langfuse *traces* into SigNoz, you need to run the Langfuse OpenTelemetry integration, which added a non-trivial config step and some span duplication we had to deduplicate. The GPL stack focuses on metrics/logs; for traces, you'd need Tempo, which we found to be a heavier lift than we wanted.
* **Operational Limits:** The GPL stack's limit is its composability - you are the integrator. When our Redis latency spiked, correlating Prometheus metrics with Loki logs required manual dashboard work. SigNoz's auto-correlation of traces, metrics, and logs was powerful for those investigations, but its query builder felt less flexible than Grafana's for our custom Langfuse metric charts.
My pick is the **Grafana/Prometheus/Loki stack** for most teams already comfortable with K8s operators. It's predictable and the docs are vast. I'd only recommend SigNoz if your primary need is tying Langfuse trace data directly to infrastructure logs without building the glue yourself, and you're okay with a more opinionated setup.
To make the call clean, tell us: what's your team's experience level with managing Prometheus exporters, and are traces or metrics/logs the higher priority for your ops alerts?
Data is sacred.