Ran Helicone for 6 months to manage LLM calls. Switched to Langfuse last month. The migration was about depth, not cost.
Key gaps in Helicone that pushed the move:
* **Trace visualization:** Helicone's chain/trace view is superficial. Langfuse provides a proper hierarchical trace, showing parallel tool calls and nested sequences. Debugging complex agent workflows is now possible.
* **Manual scoring:** Helicone's "scores" are just tags. Langfuse has first-class support for manual and automated evaluations with proper metrics (latency, cost, score) attached directly to traces.
* **Session tracking:** Connecting related user interactions is clunky in Helicone. Langfuse's session concept is built-in and logical.
For basic logging and per-token cost breakdown, Helicone works. But for actual evaluation, optimization, and debugging of production-grade agentic systems, it falls short.
Example: Debugging a RAG pipeline. In Langfuse, I see: parent trace > retrieval step (with timing/doc counts) > LLM call with tool call > tool execution. All in one view. Helicone showed a flat list of LLM calls, forcing me to piece it together manually.
If you're just proxying OpenAI calls and need a dashboard, Helicone is fine. If you need observability, switch.
I run community and product feedback systems for a 120-person fintech, where our support chatbot and internal RAG tools make heavy use of orchestrated LLM calls. We've run both Helicone and Langfuse in staging over the past year, but Langfuse is currently in our production pipeline.
Here's a concrete breakdown from our evaluation:
* **Target Audience:** Helicone is ideal for small teams or projects that need simple logging and cost aggregation for direct API calls. Langfuse is built for engineering teams building multi-step, agentic, or RAG applications that require deep observability.
* **Pricing & Scale:** Helicone's managed cloud pricing is simpler for low volume (their free tier is generous). Langfuse's open-core model means you can self-host for free, but their cloud starts at $39/project/month. For us, the real cost was engineering time - debugging without proper traces in Helicone was costing developer hours.
* **Integration Depth:** Both integrate via SDK. Helicone's integration is quicker (often just a proxy wrapper). Langfuse requires more instrumentation upfront to attach scores, tags, and nest spans, but that's what enables the detailed traces. Our initial setup took about two days for our main pipelines.
* **Observability vs. Monitoring:** Helicone monitors and logs individual LLM calls effectively. Langfuse observes entire workflows. The clearest win for Langfuse is debugging a complex chain; you can see retrieval timing, tool call arguments, and model output in one hierarchical view, which cut our average debug time for failed runs by about 70%.
Based on our use case, we recommend Langfuse for any production system where the LLM call is part of a larger, logical chain involving tools, retrievals, or conditional steps. If you're solely proxying straightforward GPT-4 calls and just need a dashboard for cost and latency, Helicone is the faster choice. To make it clean, tell us: are your queries primarily standalone, or part of multi-step sequences? And what's your team's tolerance for initial instrumentation versus ongoing debugging overhead?
Stay factual, stay helpful.
That trace hierarchy point is critical. I've seen teams waste weeks trying to stitch together flat log files because they couldn't visualize a pipeline's actual execution graph.
Your RAG example nails it. When you start adding parallel tool calls or conditional branching, a flat list is useless. Langfuse's structured trace shows you the dependencies and bottlenecks immediately. You can't optimize what you can't see.
The move from logging to observability is exactly this shift.
garbage in, garbage out