Langfuse vs Arize (2026): LLM Observability Compared — and Where Kosmoy Fits
Langfuse and Arize are two of the most credible open-source-friendly LLM observability platforms — one an MIT-core tool you fully own, the other an eval-rigor specialist with runtime guards and an Elastic-licensed core. Here is how they differ, and where each stops being an observability question.
Langfuse and Arize AI both give engineering teams open-source-friendly LLM observability with real evaluation tooling — tracing, datasets, judges, experiments. The difference is philosophy and reach. Langfuse is an MIT-core platform for tracing, evaluation and prompt management, self-hostable as the exact same codebase as its cloud and acquired by ClickHouse in January 2026. Arize pairs its enterprise Arize AX platform with the open-source Phoenix project (Elastic License 2.0), and reaches further into eval governance, runtime guardrails and voice-agent observability.
This page compares the two on the capability axes that matter, with every claim cited to each vendor's own documentation. It then asks what happens when the requirement grows past the trace — org-wide inventory, runtime enforcement across all traffic, regulatory evidence, agent containment — which is where a full AI management platform like Kosmoy enters the frame.
Who each product is for
Langfuse
Langfuse speaks to AI/ML and platform teams that want to own their observability: an MIT-licensed core (~31k stars) for tracing, evaluation and prompt management, self-hostable via Docker Compose or Kubernetes, running the exact same codebase as Langfuse Cloud and documented to operate without internet access.
It is the default for EU and regulated teams that want open-source self-hosting; ClickHouse acquired the company in January 2026 with public commitments that the MIT licence, self-hosting and cloud continue unchanged, and development has stayed active through 2026 (CLI, Monitors & Alerts GA, Assistant beta).
Arize AI
Arize AI speaks to AI engineering and ML platform teams running LLM and agent apps in production who need tracing plus rigorous evaluation. Its enterprise Arize AX platform adds the Evaluator Hub (commit-versioned LLM-as-a-judge evaluators), Signal continuous production trace review, native voice-agent observability, and runtime Guards that block, default or regenerate responses.
OSS-first teams start with Phoenix (Elastic License 2.0, ~10.6k stars); Arize is well funded ($70M Series C, February 2025) with enterprise customers including Uber, Duolingo and PepsiCo.
Langfuse vs Arize AI vs Kosmoy — the capability radar
Three shapes on the same ten axes. Langfuse (orange) and Arize (violet) both peak on Observability & FinOps and reach high on Testing & Evals — the category's two defining axes. Arize pulls ahead on Guardrails & Runtime Safety, because its Guards can block or regenerate responses, while Langfuse delegates runtime blocking to third-party libraries. Kosmoy (blue) trades raw tracing and eval 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.
- Langfuse
- Arize AI
- Kosmoy
| Capability (0–10) | Langfuse | Arize AI | Kosmoy |
|---|---|---|---|
| AI Inventory & Discovery | 0 | 1 | 9 |
| Security & Shadow AI | 1 | 3 | 8 |
| Observability & FinOps | 9 | 9 | 7 |
| Gateway & Policy Control | 0 | 1 | 8 |
| Guardrails & Runtime Safety | 1 | 6 | 8 |
| Agent Containment | 0 | 1 | 9 |
| Compliance & Audit | 3 | 3 | 9 |
| Testing, Evals & Red-teaming | 8 | 9 | 4 |
| Agent Building | 1 | 2 | 6 |
| Deployment Sovereignty | 9 | 8 | 10 |
Bold marks the highest score on each row. 10 is reserved for categorical architectural facts; specialists are expected to outscore platforms on their own spoke.
Where Langfuse wins
Genuinely open-source core. Langfuse's core is MIT-licensed (OSI-approved) and unlimited when self-hosted, running the exact same codebase as its cloud with no scalability limits. Arize's Phoenix is under the Elastic License 2.0, which is not OSI-approved open source and restricts offering it as a managed service.
Air-gap and ownership. Langfuse's networking docs state it does not require internet access, with VPC/on-prem guidance and graceful offline degradation — an explicit air-gap story that Arize does not document.
Cost, adoption and EU heritage. Self-hosting is free under MIT; ~31k GitHub stars; a Berlin-based company with an EU-default cloud region — attractive to budget-constrained and EU-data-residency teams.
Where Arize AI wins
Runtime guardrails that act. Arize Guards inspect user input and LLM output and can block, substitute a default response, or regenerate. Langfuse's own docs delegate runtime blocking (prompt injection, PII, toxicity) to third-party libraries and evaluate their effectiveness after the fact — it observes rather than enforces.
Eval governance and modality breadth. The Evaluator Hub versions evaluators at commit level, Signal reviews production traces continuously, and Arize ships native voice-agent observability with audio session replay — depth Langfuse's strong-but-leaner eval suite does not match on those fronts.
Enterprise polish. GA role-based access control, the Alyx AI engineering agent, and named enterprise customers (Uber, Duolingo, PepsiCo, Booking.com) reflect an enterprise track record; AX Enterprise also self-hosts Kubernetes-first.
Where Kosmoy fits
The specialist owns its spoke; the platform holds the frontier
Both Langfuse and Arize answer “how do we trace, evaluate and improve our LLM apps and agents?” 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 across every AI interaction 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 enforces guardrails, RBAC and budgets in the request path across all traffic; 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 Arize at observability or evals” — it does not. Kosmoy has no dataset, LLM-as-judge or experiment tooling, and its evals score is a 4; the specialists own that spoke and should keep it. It is that observability is one spoke. If the requirement is the whole web — inventory, gateway, compliance and containment in one self-hosted platform — that is a suite decision, not an observability decision.
| Capability | Capability | Langfuse | Arize | Kosmoy |
|---|---|---|---|---|
| LLM / agent tracing & observability | ✓ | ✓ | ✓ | |
| Datasets, LLM-as-judge & experiments (evals) | ✓ | ✓ | Limited — no dataset/judge suite | |
| Runtime guardrails (block / redact in path) | Delegated to third parties | Guards (block / regenerate) | ✓ | |
| Org-wide AI inventory (beyond the tool) | — | — | ✓ | |
| OpenAI-compatible gateway in the request path | — | — | ✓ | |
| Kernel-enforced agent containment | — | — | ✓ | |
| EU AI Act / ISO 42001 / NIST evidence | — | — | ✓ | |
| Open-source core | MIT core (OSI-approved) | Phoenix (Elastic License 2.0) | — | |
| Self-hosted / air-gapped | Yes (no internet required) | AX Enterprise (K8s); air-gap undocumented | ✓ | |
| Pricing model | Free (OSS); Cloud tiers; EE licence | Free tier; Pro; Enterprise quote | Enterprise subscription |
Last verified July 16, 2026 against each vendor's public documentation.
Which should you choose?
For a team whose problem genuinely is tracing and evaluation, pick on the axis that matters: Langfuse for an MIT-core platform you fully own and can air-gap, Arize for eval rigor with runtime guards and a Phoenix on-ramp. Both instrument via SDK and OpenTelemetry, so either can run alongside a gateway or a broader platform.
For an enterprise that has to prove control over all of its AI — not just observe and evaluate 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 or Arize for eval depth while Kosmoy holds the inventory, gateway enforcement, compliance evidence and containment for what reaches production.
Questions buyers ask
Is Langfuse or Arize better?
Neither is universally better. Langfuse is the cleaner open-source choice: an MIT (OSI-approved) core, unlimited self-hosting as the same codebase as its cloud, documented air-gapped operation, and EU heritage. Arize goes deeper on eval governance (the commit-versioned Evaluator Hub), runtime Guards that can block or regenerate responses, and enterprise features like voice-agent observability. Choose Langfuse for ownership and open-source purity; choose Arize for eval rigor and runtime enforcement of outputs.
Is Langfuse or Arize more open-source?
Langfuse. Its core is MIT-licensed — OSI-approved open source — and unlimited when self-hosted. Arize's open-source project, Phoenix, is under the Elastic License 2.0, which is a source-available license but not OSI-approved and which restricts offering Phoenix as a managed service. Both let you self-host; the difference is licensing freedom, and it matters most to teams with strict open-source policies or plans to build a service on top.
Which can block bad outputs at runtime, Langfuse or Arize?
Arize. Its Guards inspect user input and LLM output and can block, return a default response, or regenerate — real runtime enforcement on model inputs and outputs. Langfuse deliberately delegates runtime blocking (prompt injection, PII, toxicity) to third-party libraries such as LLM Guard or NeMo Guardrails, and positions itself as ex-post evaluation of their effectiveness. If in-path blocking matters, Arize is the stronger of the two — and a dedicated gateway is stronger still.
Do Langfuse or Arize handle EU AI Act compliance?
Not as products. Both offer platform certifications (SOC 2, and HIPAA in some tiers) with audit-oriented retention, but neither documents EU AI Act, ISO 42001 or NIST AI RMF evidence generation or AI risk classification as of July 15, 2026. That evidence layer is a governance-platform capability — Kosmoy generates it from its registries and gateway logs.
Where does Kosmoy fit against Langfuse and Arize?
Kosmoy includes operational observability but is deliberately modest on tracing and eval depth — no datasets, judges or experiments, and an evals score of 4. Its role is a full AI management platform: org-wide inventory, a self-hosted gateway that enforces guardrails and budgets, agent containment with a kill switch, and audit evidence. If your requirement is deep evaluation, Langfuse or Arize is the answer; if it is proving control over all your AI in your own infrastructure, that is a suite decision.
Sources
Every factual claim about another vendor on this page traces to that vendor's own published material or a named third-party source below.
- Langfuse GitHub repository (MIT core) — accessed July 15, 2026
- Langfuse networking / air-gap docs — accessed July 15, 2026
- Arize AX guardrails docs — accessed July 15, 2026
- Arize Phoenix GitHub repository — accessed July 15, 2026
- Kosmoy Insights Dashboard — accessed July 15, 2026
- Self-hosting overview (same codebase as Cloud) — accessed July 15, 2026
- Enterprise license key (EE feature list, MIT core unlimited) — accessed July 15, 2026
- Telemetry (EE license telemetry cannot be disabled) — accessed July 15, 2026
- Security & guardrails doc (runtime blocking delegated to third parties) — accessed July 15, 2026
- Langfuse joins ClickHouse (acquisition announcement) — accessed July 15, 2026
- Langfuse pricing — accessed July 15, 2026
- Changelog (Experiments, CI/CD gates, Monitors & Alerts) — accessed July 15, 2026
- Arize AX self-hosting docs — accessed July 15, 2026
- Observe 2026 / Arize AX launches (blog) — accessed July 15, 2026
- Arize AX release notes — accessed July 15, 2026
- Arize pricing — accessed July 15, 2026
- Series C press release ($70M) — accessed July 15, 2026
One suite instead of two point tools
Kosmoy puts an inventory, a policy gateway, compliance evidence and a containment sandbox around every AI your teams run — in your own Kubernetes.
Or email sales@kosmoy.com.