Langfuse vs Helicone: which LLM observability platform wins in 2026?
Langfuse wins on evals + dataset workflows + open-source self-host. Helicone wins on simplest setup (one proxy URL change) and dashboard polish. Pick Langfuse for full eval pipelines, Helicone for fast monitoring + cost visibility.
At a glance
| Dimension | Langfuse | Helicone |
|---|---|---|
| Setup | SDK wrap calls or OpenTelemetry | Change base URL to proxy (one line)WIN |
| Tracing | Detailed multi-step + sessionsWIN | Per-request logs + sessions |
| Evals | First-class — datasets, scorers, runsWIN | Basic eval scoring |
| Datasets / golden sets | Built-inWIN | Limited |
| Prompt management | Versioned prompts + experiments | Versioned prompts |
| Cost attribution | Per-user / session / project | Per-user / API key |
| Open source / self-host | Yes (MIT)WIN | Partial (self-host beta) |
| Pricing | Free tier + usage-based + enterprise | Free tier + usage-based |
| Best for | Full eval / dataset / observability pipeline | Quick monitoring + cost visibility |
Verdict
Langfuse is the right pick for teams running full evaluation pipelines — datasets, scorers, regression tests, A/B prompt experiments — with open-source self-host as a hard requirement. Helicone is the right pick for teams who want zero-touch monitoring — one URL change adds cost + latency + error dashboards. Many teams use both — Helicone for prod monitoring + cost, Langfuse for dev / eval / prompt iteration.
When to pick which
Pick Langfuse
Eval pipelines, dataset workflows, prompt experiments, open-source self-host.
Pick Helicone
Fastest setup (proxy URL change), cost visibility, monitoring polish.
FAQ
Easiest LLM observability setup?
Helicone — change your OpenAI base URL to the Helicone proxy, done.
Best for evals?
Langfuse — first-class dataset + scorer + experiment workflows.
Self-host?
Langfuse (MIT, full self-host) leads; Helicone has a partial self-host beta.
Last updated: 2026-06-01.