Hey everyone! I've been knee-deep in evaluating both Langfuse and Traceloop for our team's growing LLM operations, and a big question mark is long-term scalability. We're currently at a lower volume but architecting for a future with 100K+ daily requests.
From my testing and reading, both platforms are fantastic for observability, but they seem to take different architectural paths that might impact scaling.
**Langfuse** feels very database-centric (PostgreSQL). It's incredibly flexible for deep, custom analysis and complex queries on your traces. The open-source aspect is a huge plus for control. But I wonder if at 100K daily requests, managing that database performance and cost becomes a significant operational lift, even with their batch exporting options.
**Traceloop**, on the other hand, seems built from the ground up for high-volume streaming. It uses OpenTelemetry and pushes data directly to supported backends (like ClickHouse, Datadog, etc.). This feels more "cloud-native" and hands-off for the scaling piece—the scalability burden shifts more to your chosen data sink.
My optimistic take is that both *can* scale, but the effort and cost profile might differ:
* **Traceloop** might scale more automatically by leveraging your existing high-performance observability stack. Less worry about ingestion pipelines.
* **Langfuse** offers unparalleled querying power and a single pane of glass, but scaling that central database and the UI to handle high concurrency might require more proactive tuning and infrastructure.
Has anyone here stress-tested either platform at this volume? I'm particularly curious about:
* Real-world costs at ~3 million requests/month.
* Query performance on historical data when you have billions of spans.
* Any pitfalls in data sampling or retention you've encountered.
Our stack is mostly Python with some Node, and we're heavy Zapier users for process automation, so seeing how these tools fit into a broader workflow is key!
Automate all the things
I'm Sarah Johnson, leading our applied AI research team at a mid-market fintech company processing around 75,000 daily LLM inference requests currently, using a stack built on FastAPI, Pydantic AI, and Qdrant for RAG, where we've had Langfuse deployed in production for eight months and evaluated Traceloop in a two-week proof-of-concept.
* **Scaling Architecture and Operational Overhead:** Langfuse's PostgreSQL-centric model requires active database management at your target volume. We had to move from their standard managed offering to a dedicated PostgreSQL instance (16 vCPUs, 64 GB RAM) to handle our load, which added roughly $400/month to our bill and requires our platform team to monitor index performance and run quarterly query optimizations. Traceloop's OpenTelemetry collector approach delegates the scaling problem to your chosen backend; if you use ClickHouse, scaling becomes a ClickHouse problem, which can be simpler if that's already in your stack.
* **Cost Trajectory for 100K Daily Requests:** With Langfuse, our cost is primarily compute for the Langfuse server and the database, plus their commercial license fee which scales with seats. For our team of 12, the total all-in cost is approximately $2,000/month. Traceloop's open-source SDK has no direct cost, but you incur the full expense of your observability backend (e.g., Grafana Cloud, Datadog APM) and the compute for running the OpenTelemetry collector, which at 100K requests can mean a backend bill of $1,500-$3,000/month depending on retention and query patterns.
* **Data Fidelity and Query Flexibility:** Langfuse wins for deep, ad-hoc investigation. Its normalized PostgreSQL schema allows complex joins across traces, scores, and datasets with SQL-like performance. We regularly run multi-day analytical queries that would be cost-prohibitive in a metrics-centric system. Traceloop, while excellent for metric aggregation and distributed tracing visualization, is weaker for bulk exporting trace data for custom analysis; you're generally constrained to the query capabilities of your backend.
* **Integration and Vendor Dependence:** Langfuse, despite being open-source, creates a form of vendor lock-in through its proprietary schema and UI; migrating away would require a custom ETL job. Traceloop, built on OpenTelemetry standards, offers an easier off-ramp. You can stop using the Traceloop SDK and still have valid OTLP traces flowing to your backend, reducing long-term risk.
Given your emphasis on scaling with a future 100K+ daily volume and a desire to offload operational burden, I'd recommend Traceloop, but only if your primary need is monitoring and alerting on LLM performance rather than deep, exploratory analysis of individual traces. For a clean decision, tell us whether you have a dedicated platform engineer to manage a database and if your use case requires frequent, complex queries on historical trace content.
Agreed on the DB overhead. We saw the same and offloaded traces to S3 after 7 days, keeping only metadata in Postgres for active queries. Cuts the expensive storage tier.
The seat-based license cost with Langfuse is the real killer at scale though. At 100k requests you'll have more than 12 people looking at the data. It gets expensive fast.
Did your Traceloop PoC give you a clear per-request cost estimate? That's the missing piece for me.
Ship fast, review slower
You're exactly right about the architectural divergence determining the scaling cost profile. Where you see Traceloop's design as "hands-off," I'd frame it as a fundamental trade-off between query flexibility and operational simplicity at 100k daily requests.
The PostgreSQL-centric model in Langfuse creates a predictable scaling bottleneck, but that's also its analytical strength. At your target volume, each trace with nested spans, metadata, and scores can easily generate 50-100 database rows. That's 5-10 million row inserts daily before considering indexes. While you can mitigate this with aggressive partitioning, tuned BDR configurations, or offloading cold data to S3 as user955 noted, it remains an active engineering task.
Traceloop's OpenTelemetry collector streaming to a backend like ClickHouse does delegate the scaling problem, but you're then bound by that backend's query capabilities for trace analysis. ClickHouse excels at aggregates but struggles with the recursive graph traversals often needed for deep trace inspection. The real scaling question becomes whether your observability patterns require complex, ad-hoc joins across trace data - which PostgreSQL handles elegantly but at a cost, or if your needs align with simpler metric extraction and filtering that a columnar store handles efficiently.
Great breakdown on the operational overhead. Your point about > scaling becomes a ClickHouse problem, which can be simpler if that's already in your stack < hits the nail on the head. That's the key nuance.
If you're already running a high-scale ClickHouse or similar columnar store for other telemetry, Traceloop's model slides right in with minimal marginal cost. The complexity is centralized. But if observability is new for you, you're now on the hook for managing that beast at 100k reqs/day, which is its own significant commitment.
That's where Langfuse's PostgreSQL model, for all its hands-on tuning, can be paradoxically simpler for a team without a dedicated data platform group. A well-tuned RDS is a more familiar domain for many application engineers.
Prod is the only environment that matters.
Your optimistic take is spot on, especially about the scaling effort shifting to your data sink with Traceloop. That's the core architectural bet. The "hands-off" claim is only true if your chosen backend is already a well-oiled machine you're paying someone else to manage.
If you're pointing it at a self-hosted ClickHouse cluster, you haven't eliminated operational lift, you've just traded PostgreSQL query tuning for maintaining a stateful stream processor and keeping an eye on part lifecycle and ZooKeeper. The cost profile changes from "database rows and seats" to "stream ingestion units and storage bytes" on your chosen platform.
The real question is whether your team's "familiar domain," as user705 put it, is row-oriented relational data or columnar event streams. That answer dictates which set of scaling problems feels less foreign.
APIs are not magic.
That's a good way to put it. The scaling problem doesn't vanish, it just changes form.
So at 100k requests, the real comparison is between tuning a Postgres instance you know versus learning the operational quirks of a new columnar store. If your team's experience is mostly with relational databases, Langfuse's model might feel less risky even with the tuning overhead. You're right that "familiar domain" is probably the deciding factor.
Does Traceloop's setup assume you already have a ClickHouse expert on staff?