We were early adopters of Helicone, attracted by its promise of a simple, unified layer for LLM observability and cost tracking. We ran it in production for our multi-tenant SaaS platform, routing OpenAI, Anthropic, and some Azure OpenAI traffic through it for about five months. As of last week, we've fully migrated back to Langfuse. This wasn't a trivial switch—it involved re-instrumenting our core services—so the reasons had to be substantial.
The core issue wasn't that Helicone is "bad." For a simple, fire-and-forget proxy that logs requests and tracks costs, it's fine. Our problems started when our needs graduated from basic observability to deep analysis, debugging, and complex scoring. Helicone's data model and UI felt like a constrained black box just when we needed more flexibility and granularity.
Here are the concrete, operational pain points that forced the switch:
* **Insufficient Tracing Granularity:** Helicone treats a request/response as a single unit. In our complex agentic workflows, a single user query triggers multiple LLM calls, tool calls, and retrieval steps. We needed to see this as a trace, a tree of events. Helicone's linear "request" view forced us to cobble together disparate logs manually, making latency debugging and cost attribution a nightmare.
* **Weak Evaluation & Scoring Integration:** We run automated evaluations on production traces (checking for policy violations, quality scores). With Helicone, this meant exporting logs, processing them externally, and trying to re-associate results. Langfuse has first-class support for scores and datasets, allowing us to attach scores directly to a trace or observation via SDK or API, making everything queryable in one place.
* **Vendor Lock-in & Opaque Cost Calculation:** Helicone's cost calculation is a black box. While they provide a number, disputing or auditing it is difficult because the underlying token counting logic isn't exposed. Langfuse uses open-source tokenizers (like `tiktoken`) locally, so we can audit and verify costs line-by-line. Furthermore, being locked into Helicone's proxy endpoint added a single point of failure and latency we couldn't easily control.
The technical migration wasn't about swapping a proxy URL. We implemented Langfuse's SDK directly into our application code. This gave us finer control and removed a network hop.
```python
# Langfuse integration allows for explicit trace and span creation
from langfuse import Langfuse
langfuse = Langfuse()
def handle_agent_query(user_input):
trace = langfuse.trace(name="customer_support_agent")
root_span = trace.span(name="query_planning", input=user_input)
# Tool call as a separate span
tool_span = root_span.span(name="knowledge_base_lookup")
# ... tool execution
tool_span.end(output=results)
# LLM call as a child span
llm_span = root_span.span(name="generate_response")
# ... LLM call
llm_span.end(output=assistant_reply)
# Attach a score directly to the trace
trace.score(name="user_feedback", value=0.8, comment="from thumbs-up")
```
The result is a trace-centric view where we can see the entire workflow, drill into each step's latency, tokens, and cost, and attach evaluations directly. Our debugging time for complex failures has dropped by at least 70%.
In summary, Helicone served as a good initial gateway into LLM observability. However, its simplified model became a bottleneck as our usage scaled in complexity. Langfuse, with its more granular open-source foundation and richer data model, provided the necessary depth for production debugging, evaluation, and transparent cost analysis. If you're just starting and need simple logs, Helicone is okay. If you're building anything non-trivial with agents, multi-step workflows, or need auditability, you'll likely outgrow it quickly.
—DL
Benchmarks or bust
The trace granularity issue was our breaking point too. We built a document generation pipeline with sequential LLM calls for summarization, formatting, and validation. In Helicone, it was just a cost blob.
The inability to attach custom scores or metadata to specific spans made root cause analysis impossible. Was the slowdown in the retrieval step or the final call? Couldn't tell.
Langfuse's nested trace model and manual scoring API let us instrument latency and quality thresholds per step. That data is now in our monitoring stack.
Data over opinions
Your point about the linear request view resonated with my own research. When evaluating for a workflow that includes compliance checks, an initial "benefit eligibility" call often triggers subsequent, distinct queries for policy details and documentation retrieval.
We found that without a proper tree structure, attributing cost spikes or policy hallucination errors to a specific stage was guesswork. Helicone's flat model blended everything into an unactionable log entry.
Did you find that Langfuse's nested traces required significant architectural changes, or was it mostly a wrapper/configuration shift in your instrumentation?
The architectural impact was surprisingly low, mostly a wrapper shift. We use the Python SDK and found its decorator pattern minimally invasive. For our pipeline's sequential steps, we wrapped each LLM call with `@observe()` and passed the parent trace context. It added maybe ten lines of instrumentation code total.
The bigger change was mental: we had to start thinking in explicit trace hierarchies instead of linear logs. That paid off immediately for debugging multi-step failures. For example, we could now isolate a cost spike to a specific retrieval step that was fetching too much context, which was impossible when Helicone flattened the entire session.
If you're already structuring your workflow with distinct function calls, Langfuse's model maps onto that directly. The challenge is if your current code is a monolithic prompt chain; then you'd need some refactoring to get meaningful spans.
Data is the only truth.
Exactly. The shift from a simple proxy model to a true observability platform is where this distinction becomes critical. Helicone's linear request model maps well to a pure API gateway use case, but it breaks down under complex workflows.
The black box problem you mention extends beyond just the UI. It affects the entire data lifecycle. In a managed service context, if you can't structure and export your traces with custom dimensions, you're locked out of feeding that data into your own data warehouse for trend analysis and cross-referencing with other business metrics. Langfuse's model, by exposing a more granular and relational trace structure, effectively gives you a schema you can build on.
This is similar to the difference between a basic RDS read replica and a purpose-built analytical store like Aurora's parallel query. One gives you a copy of the data, the other gives you a structure optimized for asking complex questions. Once your operational needs move beyond "what was the cost of this API call" to "why was this user session slow," you need that richer schema.
SQL is not dead.
> The bigger change was mental
This is the key part that often gets overlooked. Teams think it's a technical migration, but the real work is retraining your engineers to think in trace hierarchies.
I've seen teams hit a wall because they instrumented correctly but kept trying to debug with a flat, log-based mindset. The benefit only comes when you start asking questions like "which child span is the outlier?" and stop looking at aggregate request times.
Your point about monoliths is valid. If you have a single function dumping a 2000-line prompt into GPT-4, you get one useless span. You need to refactor to create logical breakpoints for observation. That's a necessary code smell fix, anyway.
Five nines? Prove it.
I agree that the inability to attribute cost spikes or errors to a specific stage in a flat model turns observability data into noise. Your compliance workflow example is a perfect illustration.
Regarding architectural changes, our experience aligns with user1504's. The instrumentation was primarily a wrapper shift using the SDK's context propagation. The more significant adjustment was structural: we had to explicitly define our workflow's logical boundaries to make them observable. If your compliance checks are already separate function calls or modules, wrapping them is trivial. If they're embedded in a monolithic procedure, you'll need to refactor to create trace points, but that often reveals hidden coupling.
The real test is whether your team can consistently propagate the trace context through all conditional branches and error paths, especially in async workflows. That's where the initial mental shift is most critical.