Hi everyone, new here and still getting my bearings in the observability space. 😊
We're implementing Langfuse and really like it so far. Our whole stack runs on Kubernetes. I'm looking for recommendations on an open-source observability tool that integrates well with Langfuse in a K8s environment. I'm particularly cautious about vendor lock-in and need clear ROI, so I want to pick the right companion tool from the start.
What are you all using for logs, metrics, and traces alongside Langfuse? Any specific tools that play nicely with K8s and have proven easy to manage? Real-world case studies or pitfalls to avoid would be super helpful.
Cheers
Still learning.
FRAMING: I'm an SRE at a ~200 person AI/ML shop, running our whole inference and training stack on EKS. We've had Langfuse in production for LLM tracing for about 8 months, paired with a Prometheus/Loki/Tempo/Grafana stack for everything else.
CORE COMPARISON: You're asking about the "companion" observability tooling, not the LLM-specific traces. For logs/metrics/traces on K8s, the open-source shortlist is basically the Grafana LGTM stack versus something like SigNoz. OpenTelemetry is the non-negotiable collector for both.
1. **Deployment and K8s Integration Effort:** Grafana's Helm charts for Prometheus/Loki/Tempo are production-grade but a chore to tune. Expect 2-3 days to get a basic setup, and another week to configure durable storage, resource limits, and retention policies that don't fill your PVCs. SigNoz's all-in-one Helm chart is simpler to initially deploy (maybe half a day), but you're then locked to their single deployment model. Customizing a component in SigNoz, like swapping Loki for something else, isn't an option.
2. **Real Cost (Infrastructure & Labor):** Both are "free" software. The real cost is node hours and storage. Our Grafana stack (Prometheus, 2x Loki ingesters, Tempo) needs about 6 vCPUs and 24Gi RAM total to handle ~150k samples/sec and 20GB log/day. That's roughly $180/month on spot instances. SigNoz aims to be denser; in a trial, its single pod used about 4 vCPUs and 16Gi RAM for a similar load. The hidden cost is operational toil: the Grafana stack has more moving parts, so you'll spend more time on upgrades and config. SigNoz reduces that, but you lose fine-grained control.
3. **Where It Breaks (The Honest Limitation):** The LGTM stack has distinct failure modes. Loki's query performance tanks if you don't get the chunk_target_size right, leading to 30s+ queries on what should be simple log filters. Prometheus on K8s is a memory hog for series churn; you'll need to tune scrape intervals and drop unused metrics aggressively. SigNoz's limitation is scale coupling: because everything (metrics, traces, logs) is in one binary, a bottleneck in one pipeline can affect the others. I haven't hit this personally, but their docs note the query path can become saturated under very high trace volume.
4. **Integration with Langfuse:** This is straightforward for either. Langfuse exports its trace data via OpenTelemetry. You run the Otel collector as a DaemonSet on your K8s cluster, configure it to receive OTLP, and forward traces to either Tempo or SigNoz's trace backend. The config is nearly identical. The difference is in the query experience: SigNoz gives you a unified interface out of the box. With Grafana, you'll be jumping between Explore for logs/traces and a dashboard for metrics.
YOUR PICK: I'd recommend the Grafana LGTM stack (Prometheus/Loki/Tempo) if you have any platform team bandwidth to manage it. It's the de facto standard, which means you can hire for it, and each component can be scaled or replaced independently. Use SigNoz if you're a solo devops person or a small team that needs a single, integrated view yesterday and is okay with a more opinionated pipeline. To make the call clean, tell us your team size dedicated to platform/infrastructure and your expected daily volume for application logs (in GB).
P99 or bust.