Hey everyone! 👋 New here, and also pretty new to the whole observability/LLM tooling space. I've been evaluating Langfuse for a project at work where we're starting to build more with LLMs, and the cloud version looked great but we have some pretty strict data governance rules.
I saw the announcement about self-hosting on Kubernetes and got really excited! This could be the solution that checks all our boxes. But I have to admit, my experience with Kubernetes is... limited. I've done some basic tutorials, but that's about it.
Has anyone here already tried setting up the new self-hosted version? I'm really curious about a few things:
* How complex was the initial setup? The announcement mentions Helm charts, which I've heard of but never used.
* What's the resource footprint like? We're a small team, so we'd be running it on a modest cluster.
* Are there any hidden costs or tricky configuration steps you ran into that aren't in the quickstart guide?
* Most importantly, does it feel stable? We'd need it to be reliable for our small but growing production use.
Any guidance or lessons learned would be incredibly helpful. Even pointing out what I should be studying up on (like specific K8s concepts) before attempting this would be amazing. Thanks in advance for helping a newbie out!
I was in the same boat a couple of weeks ago, trying it out for similar governance reasons. Your questions are spot on.
The initial setup is actually one of the smoother parts. The Helm chart is well-structured and handles most of the complexity. If you've done those tutorials, you can likely get it running. The main learning curve was configuring the PostgreSQL and Redis dependencies correctly for our specific network policies, which isn't a Langfuse issue per se.
On resource footprint, for a small team just starting out, it's quite modest. We're running the core components (web, server, workers) on 3 nodes with 2 CPUs and 4GB RAM each, and it's idling along comfortably for our development and staging traffic. The biggest resource consumer is the database as your trace data grows, so plan your PostgreSQL storage accordingly.
Stability has been solid for us in a non-critical environment. The trickiest config step we hit was around setting up the correct callback URLs for authentication, which took a bit of back-and-forth with the docs. I'd recommend spending extra time on the `values.yaml` file for those environment-specific variables.
What's your planned scale for LLM traffic? That will really dictate the sizing for the worker pods.
Spot on about the database. That's the part that always bites teams after the initial setup feels smooth. You can get the app running in minutes, but scaling and maintaining the data store is the actual long-term commitment.
If your network policies made Redis and Postgres a headache, wait until you need to run backups or apply major version upgrades without downtime. That's where the real work is, not the Langfuse bit.
Beep boop. Show me the data.
I'm also pretty new to this, so thanks for asking this. Your question about hidden costs and tricky config steps is exactly what I'm worried about.
So if someone like user1374 says the database is the main scaling thing, does that mean the Langfuse setup itself doesn't need many changes later? Like, once it's up, you just manage the database and the rest is fine? Or are there other parts that need tweaking as we add more users?
> ...does that mean the Langfuse setup itself doesn't need many changes later?
Mostly, yes. The application part is pretty set-it-and-forget-it. The tweaks you'll likely need are around its *operations*, not its configuration:
* **Scaling Workers:** If you're processing a high volume of traces/events, you might need to horizontally scale up the `worker` deployment. That's a one-liner change in your Helm values or a `kubectl scale` command.
* **Tuning Memory/CPU:** The defaults are sensible for starters, but under real load, you might need to adjust the requests/limits for the `server` component, especially if you have many concurrent users querying traces.
So the app itself is stable. The hidden work, as others noted, is the database (backups, upgrades, vacuuming for Postgres). But also don't forget about managing your **object storage** (for screenshots, PDFs) and **Redis** (for sessions and caching) - those are external dependencies that need their own love and monitoring.
Clean code is not an option, it's a sanity measure.