We're a 10-person team building internal LLM tools and some customer-facing features. Currently evaluating observability platforms. Need to decide between Traceloop and Langfuse.
Key requirements:
* Primarily use OpenAI and Anthropic APIs.
* Need to track costs, token usage, and latency per user/feature.
* Must support fine-grained evaluation (RAGAS, custom scorers).
* Python SDK is a must; simple local dev setup is a plus.
Initial tests show Langfuse's UI is more polished for analytics, but Traceloop's OpenTelemetry-native approach seems cleaner for integration into our existing Grafana/Prometheus stack.
Has anyone run both in production at our scale? I'm particularly concerned about:
* Data export options if we want to switch later.
* SDK overhead in high-throughput scenarios.
* The actual setup complexity for running the OTel collector.
Our current proof-of-concept for Traceloop looks like this:
```python
from traceloop.sdk import Traceloop
Traceloop.init(app_name="our_llm_app")
# Auto-instrumentation picks up OpenAI calls
```
Langfuse required more manual instrumentation. Which approach caused less friction long-term?
—cp
Hey David, and team. I'm a platform lead at a fintech company of about 15 engineers; we've been running Langfuse in production for our agent-based financial assistant for about eight months. We chose it after a head-to-head POC with Traceloop.
**Core Comparison**
**Integration Effort:** Traceloop's auto-instrumentation is easier for the initial POC. Langfuse's more manual SDK (2-3 lines per chain) took us a day to instrument fully. However, that explicit control became an advantage long-term; we avoided some "magic" debugging when our pipelines got complex.
**Data Portability:** Langfuse wins here for me. You can export all trace data as JSON/CSV via the API or UI with one click. Traceloop, being OTel-native, funnels data to your chosen backend (e.g., Tempo, Jaeger), so your export options depend on that tool, which can be more restrictive.
**Observability Stack Fit:** If you're already deep in Grafana/Prometheus and have OTel expertise, Traceloop's approach is architecturally cleaner. For our team, which lacked that, the operational overhead of managing the OTel collector properly (not just locally) was a real cost. Langfuse's managed hosted option saved us time.
**Cost Tracking Granularity:** Both handle per-request token/cost tracking. Langfuse's UI lets you slice that cost by any tag (like user ID or feature flag) in two clicks, which our product team uses weekly. With Traceloop, you'd need to build those dashboards yourself in Grafana, which is more powerful but requires more work.
**Your Pick**
I'd recommend Langfuse for your team, primarily because of your 10-person size and the need for polished, actionable analytics without building internal tools. If your team has a dedicated SRE already managing Grafana who wants absolute control, then Traceloop. To make the call clean, tell us: 1) Do you have someone on staff comfortable owning the OTel collector config? and 2) Is your primary user of this data an engineer, or are PMs/analysts also looking at costs and eval scores?
Stay curious, stay critical.
Exporting via UI with one click isn't a feature, it's a crutch. If your data's locked behind a proprietary UI, you've already lost. OTel backends like Tempo have straightforward APIs for extraction, and you own the data format from the start.
You mention avoiding "magic" debugging with manual instrumentation, but then praise a managed service. Pick a side. If you want control, own the pipeline. If you want convenience, accept the black box.
Langfuse's managed option saves time until you need to correlate traces with infra metrics during an incident. Then you're staring at two screens, wishing you had one OTLP stream.
Don't panic, have a rollback plan.
The OTLP dream is great, but most 10-person teams building LLM features aren't running a production-grade Tempo stack with retention policies and automated exports. They're trying to ship.
> If your data's locked behind a proprietary UI, you've already lost.
It's locked either way. With OTel, it's locked in your object storage buckets in a proprietary wire format you need to parse. With a managed service, it's behind their API. Both are exportable; one just requires more pipeline code to make usable.
Your point about correlation during an incident is valid. That's the real trade-off. But for the stated goal of tracking costs and latency per feature, a separate UI you can actually query is often faster than building your own Grafana dashboards from raw spans.
shift left or go home
Your POC code snippet highlights the core tradeoff. Traceloop's auto-instrumentation is undeniably less friction *to start*. For a 10-person team, that's tempting.
However, your requirement for fine-grained evaluation with RAGAS and custom scorers changes the calculus. Manual instrumentation, like Langfuse requires, becomes an advantage here. You explicitly define the spans and traces that matter for your evaluations, rather than relying on auto-instrumentation to guess your evaluation boundaries. That explicit control reduces friction long-term when you're debugging why a particular scorer returned a low value; you know exactly what context was captured.
On setup complexity: running the OTel collector isn't the hard part. The complexity is in configuring and maintaining the entire pipeline - the collector, the backend storage (e.g., Tempo), and the dashboards for your specific analytics. If you already have Grafana/Prometheus, you're halfway there. But ask your team: do you want to write queries against OTLP span attributes to calculate cost per feature, or click a pre-built dashboard?
For SDK overhead, both are negligible. The network I/O to the collector or Langfuse's API is the bottleneck, not the SDK code.
SQL is not dead.
> The network I/O to the collector or Langfuse's API is the bottleneck
It is, but it's trivial. You're measuring in ms. The real cost is the data storage.
If you're halfway to a Grafana stack, the incremental cost to finish is minimal. The query side is what matters. Writing PromQL to sum token counts by `feature_name` is a one-time cost.
But you're correct about the evaluation boundaries. Auto instrumentation will create spans you don't care about, muddying your scores. You'll spend more time filtering noise than you saved in initial setup.
Choose based on who you want writing queries: your engineers against OTel spans, or your PMs against a Langfuse dashboard.
cost per transaction is the only metric
You're absolutely right about the clarity manual instrumentation brings to evaluations. It forces you to define the logical unit of scoring up front, which becomes invaluable.
I'd add a small caveat about your last sentence. The overhead isn't negligible when you're debugging a *flawed* trace structure. If auto instrumentation creates nested spans you didn't intend, untangling that during a production issue can add more latency to your investigation than the network I/O ever could. So the real cost is in developer time, not milliseconds.
— isabel