The Langfuse alternative your whole team can run.
Multi-turn simulations, voice agent testing, and evals that product experts drive themselves.
Langfuse is a strong open-source platform for tracing and evaluating LLM apps. Teams reach for a Langfuse alternative when eval scores pile up with nobody acting on them: when the next step is simulating whole conversations before release, letting domain experts run evals without filing a ticket, and gating merges on the result.
Join thousands of AI developers shipping reliable agents with LangWatch.
How LangWatch compares to Langfuse.
Five things teams care about when picking a quality layer for agents. Each row shows what Langfuse ships today and what LangWatch gives you on day one.
Scenario, the open-source simulation framework, runs simulated users against your agent over full multi-turn conversations, tool calls and voice included, with a judge scoring the outcome.
The Langfuse docs show multi-turn simulation by wiring a library like OpenEvals to drive the conversation, then scoring results with its evals. It works, and you assemble and maintain it yourself.
Scenarios and evaluators are created in the UI or in code against the same source of truth, so product managers and domain experts change thresholds and add cases without a ticket.
Annotation queues and UI experiments exist, and the core loop of instrumenting traces, building datasets, and shipping evaluators runs through engineers.
Scenario tests run in pytest and vitest, so whole-conversation tests gate merges in the CI you already have. Teams we work with block merges when agent scores dip under 0.85.
Langfuse documents CI/CD experiments that block deploys on dataset regressions. Trace-level scoring is well covered; conversation-level testing is the part you build.
The LangWatch core is Apache 2.0, which includes an express patent grant, with enterprise features such as SSO and RBAC in a separately licensed ee folder.
Everything outside /ee is MIT. SCIM, audit logging, data retention policies, and UI customization sit in /ee and need a license key when self-hosting. Check the split against the features you need.
LangWatch Cloud runs in EU data centers, ISO 27001 certified and GDPR compliant, built by an EU company. Self-host when data cannot leave your boundary.
Langfuse Cloud offers an EU region (Ireland) next to US and Japan, with SOC 2 Type II and ISO 27001. On residency, both tools have you covered.
Langfuse capabilities above were checked against langfuse.com docs and the langfuse/langfuse repository in July 2026. Spot something outdated? Tell us and we will fix it.
Why teams pick a Langfuse alternative.
Three patterns from our sales conversations, anonymized and recurring.
The eval platform nobody opens
An engineer sets up tracing and a few judge evals, the dashboards fill up, and months later nobody has changed a threshold or added a test case. The tool runs in the background and nobody acts on it. Usually the person who knows what a good answer looks like, the support lead or the domain expert, never had a way to work with it. LangWatch puts scenario creation and quality gates in the UI so the people who own the answers run the evals.
Conversations fail where single traces pass
Scoring individual traces answers whether one completion was fine. It cannot answer whether the agent holds up across a whole conversation: does it keep the constraint from turn two when the user changes their mind at turn nine, does the voice agent recover after an interruption. Simulations run those cases with a virtual user pushing on exactly the behavior you are worried about, voice included.
Quality gates that block bad merges
Teams want conversation-level results to stop regressions before deploy. Scenario tests are pytest and vitest tests, so they drop into the CI pipeline you already run. One team blocks merges whenever their agent’s eval score dips under 0.85; the gate runs on every pull request like any other test.
Three reasons agent teams choose LangWatch.
Scenario drives a simulated user through the whole conversation, tool calls and all. Single-trace scoring tells you one turn looked fine; a simulation tells you the agent skipped the policy check by turn four.
AI-powered Ask finds errors and anomalies for you, across flame charts, span lists, topology and graph views, waterfall traces with a full audit trail, and your own saved lenses.
Domain experts create scenarios and edit quality gates in the UI. Engineers work in code against the same source of truth. When the owner of the prompt can change the threshold, the scores get acted on.
Who should pick Langfuse.
Langfuse is a strong open-source platform, and for plenty of teams it is the right one. Here is the split as we see it in real evaluations, so you can make the call in one read.
- You want a battle-tested open-source tracing stack with a large community (30k+ GitHub stars) and an MIT core.
- Your engineers own quality end to end, and a code-first loop of traces, datasets, and judge evals fits how they work.
- You mainly need observability, prompt management, and a playground, self-hosted with nearly everything free.
- Product managers and domain experts need to create and run evals and simulations themselves, in the UI.
- You test agents as conversations: multi-turn, tool-using, voice included, before they reach users.
- You gate merges on conversation-level tests, running as pytest or vitest in the CI you already have.
Langfuse alternative questions, answered.
- Is Langfuse open source?
- Yes. The Langfuse core is MIT licensed; code in its /ee directories is under a separate commercial license and needs a license key when self-hosting, which as of mid 2026 covers features like SCIM, audit logging, data retention policies, and UI customization. LangWatch follows a similar open core model with an Apache 2.0 core.
- What is the difference between Langfuse and LangWatch?
- Both are open core platforms for tracing and evaluating LLM applications, and both can be self-hosted. Langfuse centers on observability: tracing, prompt management, datasets, and judge evals in a workflow that engineers instrument and own. LangWatch adds a first-party agent simulation framework (Scenario), voice agent testing, and a UI where product managers and domain experts create and run evals themselves.
- Can product managers use LangWatch without writing code?
- Yes. Scenarios, evaluators, and quality gates can be created and edited in the LangWatch UI, so product people and domain experts run and iterate on tests themselves, while engineers work against the same setup from code.
- Does Langfuse support multi-turn conversation testing?
- Langfuse documents a cookbook approach: you wire an external library such as OpenEvals to simulate the conversation, then score the result with a Langfuse LLM-as-a-judge evaluator. LangWatch ships this as a product: Scenario runs simulated users against your agent, multi-turn and voice included, from the UI or as pytest and vitest tests.
- Can I self-host Langfuse and LangWatch?
- Yes, both. Langfuse self-hosting includes most features under its MIT core, with a short list of enterprise add-ons behind a license key. LangWatch self-hosts with docker compose on your own infrastructure under its Apache 2.0 core, with enterprise features such as SSO and RBAC licensed separately. If you embed either into your own product, read both licenses and check which of the features you need sit in the commercial folders.
- Do Langfuse and LangWatch offer EU hosting?
- Both do. Langfuse Cloud offers an EU region (Ireland) alongside US and Japan regions, with SOC 2 Type II and ISO 27001. LangWatch Cloud runs in EU data centers, is ISO 27001 certified and GDPR compliant, and is built by an EU company. Both platforms can also self-host inside your own boundary if data cannot leave at all.
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