Capability · Comparison
Helicone vs Langfuse
Helicone and Langfuse both do LLM observability but start from different places. Helicone routes your calls through its proxy (change the base URL) and captures everything server-side, so zero SDK changes. Langfuse relies on an SDK or OpenTelemetry, trading setup for deeper structured traces and excellent self-hosted deployment. The decision is usually about how much instrumentation you're comfortable adding.
Side-by-side
| Criterion | Helicone | Langfuse |
|---|---|---|
| Integration model | HTTP proxy — swap base URL | SDK + OpenTelemetry |
| Time to first trace | Minutes — change one URL | 15-30 minutes — add SDK, instrument |
| Structured span data | Good — extracted from proxy logs | Excellent — first-class spans, nested |
| Self-hosting | Docker self-host available | First-class self-host (most popular self-hosted option) |
| Eval / dataset workflows | Supported | Rich — datasets, experiments, scorers |
| Prompt management | Prompt versioning | Prompt versioning + dataset-linked experiments |
| Cost tracking | Built-in across providers | Built-in with detailed per-user/session rollups |
| Best fit stack | Simple OpenAI / provider SDK usage | Any stack, especially with existing OTel |
Verdict
For teams that want observability yesterday and don't want to rewrite code paths, Helicone's proxy model wins — you can be logging and tracking cost inside an hour. For teams that care about deep structured traces, want to self-host without vendor friction, or are already invested in OpenTelemetry, Langfuse is the stronger long-term bet. Both have healthy open-source cores and credible cloud offerings in 2026.
When to choose each
Choose Helicone if…
- You want the fastest possible time to first dashboard.
- You don't want to add a tracing SDK.
- Your stack is mostly direct OpenAI/Anthropic SDK usage.
- Proxy latency is acceptable for your workload.
Choose Langfuse if…
- You want deep structured traces with nested spans.
- Self-hosting is a hard requirement for compliance.
- You already emit OpenTelemetry and want to plug straight in.
- You run eval workflows and want dataset-linked experiments.
Frequently asked questions
Does Helicone add latency?
Yes — every request goes through the proxy. For most workloads it's a few milliseconds, but latency-sensitive applications should benchmark on their region.
Is Langfuse harder to self-host?
No — it's probably the easiest modern LLM observability platform to self-host, with an official Docker Compose and Kubernetes setup.
Can I use both?
Technically yes, but you'll double-log. Pick the one that matches your integration philosophy and standardise.