Langfuse vs Helicone (2026): LLM Observability Compared — and Where Kosmoy Fits

Three shapes on the same ten axes. Langfuse (orange) and Helicone (violet) both peak on Observability & FinOps, and both stay low on inventory, guardrails, containment and compliance. The instructive difference is on Gateway & Policy Control, where Helicone reaches higher because it actually sits in the request path, while Langfuse observes from beside the app. Kosmoy (blue) trades raw tracing depth for reach across inventory, gateway, compliance and agent containment. Read it as area: the two observability tools compete on one spoke; the suite covers the web.

Actively developed, and backed. Langfuse ships regularly under ClickHouse ownership — a CLI (February 2026), Monitors & Alerts GA (June 2026), an Assistant beta. Helicone has been in maintenance mode since the March 2026 Mintlify acquisition, with feature development ended.

Deeper evaluation tooling. Langfuse offers LLM-as-a-judge, code evaluators, first-class Experiments with CI/CD quality gates and annotation queues; Helicone's evaluation is prompt-iteration-oriented (spreadsheet-like experiments, scoring, fine-tuning datasets) rather than a full eval suite.

Adoption, EU heritage and air-gap. ~31k GitHub stars, a Berlin-based company with an EU-default cloud region, and networking docs stating it does not require internet access — an air-gap story Helicone does not document.

It sits in the request path. Helicone ships an active AI gateway — an OSS Rust proxy and a cloud gateway with 100+ models, intelligent/model-cost routing, load balancing, automatic failover, caching and per-user/team rate limits by requests, tokens or dollars. Langfuse is SDK/OTel instrumentation only and never enters the request path, so it can observe but not route or enforce.

Zero-markup passthrough billing. Helicone's cloud gateway can bill model usage at provider cost with automatic fallback to BYOK — an unusual, developer-friendly monetization model.

Low-friction developer onboarding. Transparent, simple pricing and a one-line integration made Helicone a fast adopt for developer-led teams. The caveat still applies: it is frozen since the March 2026 Mintlify acquisition, so this advantage benefits existing users more than new ones.

Both Langfuse and Helicone answer “how do we log, trace and understand our LLM traffic?” — one purely from beside the app, one partly in the request path. Neither answers “what AI are we running across the organization, is it compliant, and what happens when an agent misbehaves?” Those are different questions, and in a regulated enterprise they arrive together.

Kosmoy delivers the same operational observability both products do — usage, latency, cost and quality in an Insights Dashboard — but wraps it in the layers an observability tool leaves out: a risk-tiered inventory of every model, MCP server and agent; one OpenAI-compatible gateway that, unlike Helicone's developer-oriented proxy, enforces guardrails, RBAC and budgets with enterprise policy; EU AI Act, ISO 42001 (aligned) and NIST AI RMF evidence; and kernel-enforced Action Capsule containment for agents that act.

The honest framing is not “Kosmoy beats Langfuse and Helicone at observability” — it does not. Kosmoy has no dataset, LLM-as-judge or experiment tooling, and its evals score is a 4; the specialists own the tracing and eval spoke. It is that observability is one spoke. If the requirement is the whole web — inventory, gateway, compliance and containment in one self-hosted platform, actively supported — that is a suite decision, not an observability decision.

For a team whose problem genuinely is observability, the choice depends on architecture and timing. Langfuse is the forward-looking open-source pick for new adoption; Helicone makes sense mainly where its gateway is already in place, given that the product is frozen since the March 2026 Mintlify acquisition. Both instrument LLM traffic, so either can run alongside a broader platform.

For an enterprise that has to prove control over all of its AI — not just observe it — the choice is not between these two tools but between a point tool and a suite. Kosmoy sits comfortably next to either: keep Langfuse for eval depth, or keep an existing Helicone deployment running, while Kosmoy holds the inventory, enterprise gateway enforcement, compliance evidence and containment for what reaches production.