Helicone vs Langfuse (2026): LLM Observability Compared, Honestly

Helicone and Langfuse are the two names that come up first when a team decides it needs to see what its LLM app is doing. They look similar on a landing page. Under the hood they made opposite bets: one wraps your calls at the proxy and gives you a fast, flat view; the other traces every span and treats evaluation as a first-class product. In 2026 there is also a third factor that changes the recommendation, and it is not a feature.

TL;DR: Langfuse is the safer default for most teams in 2026: it is OTel-native, traces multi-step agents properly, and has a real self-host path plus prompt management and evals. Helicone is still the fastest thing to integrate and the cheapest at low volume, but it entered maintenance mode after being acquired, so no new features are coming. Neither is built primarily for cost attribution, which is the gap RelayPlane fills at the proxy layer.

What is the core difference between Helicone and Langfuse?

Helicone is proxy-first. You point your base URL at Helicone, and it logs every request that passes through. That makes integration nearly free, one line, and it doubles as a gateway with caching and rate limiting. The tradeoff is that its view is flat: request-level, not span-level. For a single call, that is plenty. For a ten-step agent, a flat log does not show you the tree.

Langfuse is trace-first and OpenTelemetry-native. It was built for nested, multi-step pipelines, so it captures spans and the structure between them. On top of that it ships prompt management with versioning, datasets, LLM-as-judge evals, human review queues, and a playground that pulls live production prompts. It is less a logger and more an LLMOps platform.

Helicone answers “what did this call do.” Langfuse answers “what did this whole agent run do, and was it any good.”

Helicone vs Langfuse: feature and pricing comparison

Does the Helicone acquisition change the decision?

It should factor in. Helicone was acquired by Mintlify in March 2026 and moved into maintenance mode. Maintenance mode here means the service stays live and keeps getting security patches, new model support, and bug fixes, but no new features are being built. At the time of the acquisition Helicone had processed over 14 trillion tokens for around 16,000 organizations, so this is not a shutdown. It is a freeze.

If you value a five-minute setup and are fine with the feature set as it stands today, Helicone still works. If you expect the platform to grow with you, a frozen roadmap is a real consideration. Langfuse, by contrast, is still shipping.

Which one should I choose?

Choose Helicone if you are early, your chains are simple, your spend is modest, and you want observability live in an afternoon with a gateway thrown in. It is the cheapest and fastest option at low volume, with the caveat above about the roadmap.

Choose Langfuse if you run multi-step agents, want proper trace trees, care about evals and prompt management, and want a self-host path you can actually grow into. For the median team scaling past simple chains in 2026, it is the more durable default.

Where does cost attribution fit?

Here is the honest gap. Both tools show you cost as a reported number after the fact, next to the traces. Neither is designed to stop spend. If your real question is “what did this run cost, and how do I keep a runaway agent from spending my budget,” observability alone answers half of it. You see the damage; you do not prevent it.

RelayPlane sits at the proxy layer and does the other half. It is a free, MIT-licensed npm proxy that runs on your own machine, prices every request into a local ledger for per-run cost attribution, enforces a hard daily cap, and has a kill switch. It is not an observability suite: no eval framework, no prompt playground, no trace tree, and it supports 11 providers rather than logging every call from every SDK. It does one thing, cost, and it acts on it instead of only charting it.

npm install -g @relayplane/proxy
relayplane init
relayplane start

A sensible stack: Langfuse or Helicone for traces and evals, RelayPlane in front for the hard cost ceiling. For the how-to on that ceiling, see how to cap AI agent spend, and for tracking cost per call in Node, see per-request LLM cost tracking.

Bottom line

For simple apps and tight budgets, Helicone is still the fastest way to get eyes on your LLM calls, as long as a frozen roadmap does not bother you. For agents and teams that expect to grow, Langfuse is the more future-proof choice with real tracing and evals. And if the pressing problem is spend rather than visibility, pair either with a proxy that enforces a hard cap.

Side-by-side details: RelayPlane vs Helicone and RelayPlane vs Langfuse. Still picking a gateway underneath? See the LiteLLM vs OpenRouter comparison.

For a hard cost ceiling that acts instead of just charts, start with the free proxy quickstart.

Competitor facts verified 2026-09-06 against Helicone, Langfuse, and Mintlify public announcements and third-party comparisons. Pricing and status change; check each vendor for current terms. RelayPlane is open source: @relayplane/proxy on npm, source at github.com/RelayPlane/proxy, 11 providers.