I'll be honest: we started with Langfuse for the shiny observability dashboards and the promise of tracing every LLM call. But after a few months? The main thing keeping me from migrating is the surprisingly solid data export.
The dashboards are fine for a quick glance, but for any real analysis or custom reporting, I need the raw data in our warehouse. Their export to S3 (and then into Snowflake for us) just works. It's become the backbone for our actual performance reviews and cost attribution.
* **The Good:** Schema is sensible and well-documented. Incremental exports are reliable. It handles our volume without fuss.
* **The Ironic:** We're paying for a whole monitoring platform, but the killer feature feels like a glorified, reliable ETL pipeline out of their system.
Anyone else in the same boat? Using the fancy UI as a occasional diagnostic tool, but fundamentally relying on the exported data to do the real work in BI tools or custom apps? Curious if this is a common pattern or if we're just weirdly data-pipeline obsessed.
Totally get what you mean! We're new to it, and honestly, the dashboards feel a bit overwhelming for our small team right now. But just knowing the data export is there and reliable is a huge relief for future planning.
> the killer feature feels like a glorified, reliable ETL pipeline
That made me laugh, it's so true. I think it's smart to build on something solid like that, even if the main UI isn't your daily driver yet. Did you have to do much transformation after loading into Snowflake, or was it pretty much analysis-ready?
That's a very practical approach. The data being "analysis-ready" after export depends heavily on what you're trying to measure.
The schema is indeed clean, but you'll almost certainly need to build some views or transformation layers in Snowflake for meaningful analysis. The exported tables are normalized, so joining `observations`, `traces`, and `scores` is a prerequisite for most queries. For cost attribution, for instance, we had to join trace data with our internal usage logs to map a Langfuse trace to a specific end-user and internal project code. The export gives you the foundational blocks, but the real business logic sits in your warehouse transformations.
What's your planned analysis use case? That usually dictates the transformation complexity.
Data over dogma
Yeah, that reliable ETL is the only part that's critical. Dashboards are a toy. The minute you need a real business metric, you're back in the warehouse anyway.
>overwhelming for our small team
That's the other half of it. You end up paying for a ton of UI you'll never use. As long as the export keeps working, you can ignore the rest.
You'll definitely need transformations, like user759 said. But building them yourself with clean data beats fighting a pre-built dashboard that doesn't answer your questions.
Keep it simple
That's a really interesting way to frame it, and I think I get it. The promise of the dashboard is what gets you in the door, but the reliable pipeline is what keeps you from leaving. It reminds me of some SEO tools we've used, where the real value is in the raw log file export, not the pretty graphs.
>fundamentally relying on the exported data to do the real work
That's us, for sure. Our marketing automation platform has a similar dynamic. We have a HubSpot integration where the dashboards are nice for a high-level check, but all our actual campaign performance reviews and attribution modeling happen after we've pulled the raw data into our own warehouse. It's reassuring to hear Langfuse's export holds up under real use.
Since you're using the data for performance reviews and cost attribution, are you finding you're doing most of that analysis directly in Snowflake with SQL, or are you building a separate BI layer on top?
That's a great parallel with the SEO tools, it's the same pattern. You need the platform to collect the data cleanly, but you do the thinking in your own space.
>are you doing most of that analysis directly in Snowflake with SQL, or are you building a separate BI layer on top?
For us, it's both. Our core transformations, like those joins for cost attribution, are SQL views in Snowflake. That's our "source of truth" dataset. But for the actual reviews and reporting, most teams pull that cleaned data into Looker or simple tools they're already comfortable with. They don't write raw SQL against the base export tables.
The key is that initial transformation layer inside the warehouse. Once you've built it, the export pipeline lets you treat Langfuse as just another data source feeding your company's normal workflows.
ship early, test often
You're definitely not weird for focusing on the pipeline. We ended up in a similar spot - the UI is great for debugging a specific trace when something's broken, but the daily and weekly metrics that actually drive decisions come from our warehouse.
One thing that solidified it for us was building a simple dashboard off the exported data that mirrored a team's core workflow, which the native UI didn't support. The fact that the export is so dependable meant we could just set it and forget it, treating Langfuse like a black-box data collector.
It's a solid foundation, even if you end up building most of the house yourself.
ship early, test often
Completely get this. We ended up in a similar spot with a marketing automation platform - you buy it for the UI, but you stay for the clean data pipelines.
Your point about it being a glorified ETL pipeline is spot-on, but I'd actually argue that's the *smart* way for them to build it. A reliable, well-documented data export means you can never truly get locked in. That's a good kind of vendor relationship.
For us, the real value unlocked was blending Langfuse export data with our marketing campaign logs in the warehouse. Seeing cost-per-trace alongside actual user conversion data from our ESP? That's when it got powerful. The native dashboards could never show that cross-system view.
So yeah, you're not alone. The UI is the showroom, but the export is the engine.
Always A/B test.
>it's pretty much analysis-ready
That's optimistic. As user759 pointed out, you'll be joining tables immediately. The schema is clean, but it's built for their system, not your reporting. If you think you're just querying a single table for "performance reviews," you're going to have a bad time.
The fact that a reliable export exists at all is the real feature. It lets you treat their entire platform as a collector and build what you actually need, because you're right - their dashboards won't scale with your questions.
- Nina