LangWatch is live · free plan, paid from €29/core-seat/mo · last verified 30 Oct 2026 · viability healthy
LangWatch
LangWatch is an open-source LLMOps platform for agent testing, LLM evaluation and OpenTelemetry-native LLM observability.
LangWatch earns its keep when agent reliability, not dashboards, is the actual problem. Simulated users in text and voice, judge verdicts at any turn, tool-call verification across long dialogues and Crescendo red-teaming go deeper than tracing-first rivals, and Instant Evals turns a production failure into a test in minutes. Budget-wise it's honest: €0 Developer, €29 per core-seat, then €5 per 100k events. Skip it if you only want basic LLM observability.
Verdict generatedVerified 3h ago · liveness 87/100 · cite: rightaichoice.com/tools/langwatch
- AI platform teams running agent simulations as continuous testing in CI
- Voice AI teams simulating callers, latency and interruptions before launch
- Teams needing on-demand Instant Evals across all stored production conversations
- Regulated enterprises requiring self-hosting, audit logs, SSO and EU residency
- Solo developers who only want a generous free tracing tier
- Teams that just need basic LLM observability with no testing or evals
- Buyers who want flat usage pricing with no per-core-seat component
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Skip LangWatch if you want cheap trace storage and nothing else — the €29 per core-seat per month Growth plan only pays off if your team will actually write and maintain scenarios that gate merges in CI.
Events past the 200k Growth allowance cost €5 per 100k, so a busy production agent can add a meaningful line to a €29 per core-seat per month bill.
The €0 Developer tier fits one or two engineers kicking the tyres, and Growth at €29 per core-seat per month with 200k events included fits a small product team running simulation tests in CI. Langfuse and other tracing-first tools can look cheaper if all you store is traces, because you are not paying for the test loop; teams that need agent simulations and governance at scale end up in the Enterprise tier, priced custom and billed through AWS or Google Marketplace.
In short
LangWatch — LangWatch is an open-source LLMOps platform for agent testing, LLM evaluation and OpenTelemetry-native LLM observability. Best for AI platform teams running agent simulations as continuous testing in CI, Voice AI teams simulating callers, latency and interruptions before launch, Teams needing on-demand Instant Evals across all stored production conversations. Free to start; paid plans from €29/core-seat/mo.
No head-to-head comparison for LangWatch yet — the alternatives below are the closest matches we list.
What is LangWatch?
LangWatch — LangWatch is an open-source LLMOps platform for agent testing, LLM evaluation and OpenTelemetry-native LLM observability. Best for AI platform teams running agent simulations as continuous testing in CI, Voice AI teams simulating callers, latency and interruptions before launch, Teams needing on-demand Instant Evals across all stored production conversations. Free to start; paid plans from €29/core-seat/mo.
LangWatch is an open-source LLMOps platform built for AI platform teams that need to trace, test, evaluate, route and govern every LLM call in the company — from the agents they build to the coding assistants their engineers run. The premise is blunt: an agent can take a hundred paths to the same goal, and hand-testing catches only a few, so simulations should run as continuous testing on every pull request. The testing surface is the differentiator. LLM-powered simulated users hold multi-turn conversations with your agent in text or voice, success criteria are written in plain language per scenario, and a judge agent reads the whole trace with each step expanded to return a pass/fail verdict plus its reasoning at any turn. Every tool call, skill and MCP server is traced and can be mocked or fixtured for deterministic runs, and adversarial red-teaming covers Crescendo escalation, refusal detection and backtracking. Voice testing runs end-to-end against ElevenLabs, OpenAI Realtime, Twilio, Pipecat and Gemini Live with TTFB and p50/p95 latency plus background-noise and interruption injection. On the eval side you get offline experiments from SDK, notebook or UI, online evaluations and monitors on production traffic, and built-in scorers including RAGAS, hallucination, toxicity, PII and LLM-as-a-judge, with multimodal scoring for images. Instant Evals, shipped September 2026, queries your entire stored production history on demand and returns matches in minutes — ask a question no filter can answer. Observability is OpenTelemetry-native with full GenAI spec support: waterfall, flame graph, topology and sequence views, Cmd+K navigation, topic clustering, and token, cost and cache accounting per span including Claude Code, Codex, Copilot and opencode. Langy, the vendor's automated AI engineer, turns a PM's plain-English goal into a scenario test plan, runs it in parallel, scores it and drafts prompt revisions as pull requests. The Apache 2.0 core self-hosts via
Key features
- Agent simulations with LLM-powered simulated users in text and voice
- Judge agent returns pass/fail verdict with reasoning at any turn
- Natural-language success criteria defined per scenario
- Scenario SDKs for Python and TypeScript, run in pytest/vitest and CI
- Tool-call, skill and MCP server verification across long dialogues
- Mockable and fixtured tools for deterministic simulation runs
- Voice agent testing with ElevenLabs, OpenAI Realtime, Twilio, Pipecat, Gemini Live
- Voice latency metrics (TTFB, p50/p95) plus noise and interruption injection
- Adversarial red-teaming: Crescendo escalation, refusal detection, backtracking
- Instant Evals: query entire stored production history on demand
- Online evaluations and monitors on production traffic with alerts
- Built-in evals: RAGAS, hallucination, toxicity, PII, LLM-as-a-judge
- Multimodal evaluations with images and files in datasets
- OpenTelemetry-native tracing with full GenAI spec support
- Waterfall, flame graph, topology and sequence trace views
Behind the verdict
We'd reach for LangWatch the moment a team admits its agents are still tested by hand. That's the wedge. Tracing tells you what broke; simulations tell you whether the fix survives the next hundred paths the agent might take. The Scenario SDK runs in pytest and vitest and in CI, so a failing simulation can gate a merge the same way a unit test does — and the judge agent expands each step of the trace before scoring, which is more useful than a bare pass/fail number when you're arguing about a regression at 6pm. Instant Evals is the release that changed the workflow for us. Instead of configuring a run first, you ask a question of every conversation you've stored and get the matches back in minutes. That closes the loop from production failure to a reproducible test, and the September 2026 addition of file and image uploads into dataset cells means the same surface handles multimodal cases. Where it bites: the depth has a learning curve. You have to actually write scenarios with real success criteria — treat LangWatch as a trace store and the per-seat math gets hard to defend. Governance essentials (custom SSO/RBAC, audit logs, SLAs, custom retention) sit on Enterprise, and the €29 core-seat plus €5 per 100k events structure means chatty production traffic can outgrow a small budget quickly. Against Langfuse, the split is philosophical rather than feature-count. Langfuse is a strong, widely adopted tracing and prompt-management layer; LangWatch is the same foundation with simulation, voice testing and red-teaming stacked on top. If your stack is already heavy on Langfuse traces, migration cost is real and probably not worth it for a single evals use case. Self-hosting is a genuine option, not a checkbox — Docker, Helm/Kubernetes, VPC or a hybrid split with the data
Models under the hood
as of 2026-10-22
Limitations
- The free Developer tier is deliberately narrow: 50k events per month, 14-day data access, 2 users, and caps of 3 scenarios, 3 simulations and 3 custom evals — not enough for a team's real test suite.
- Costs beyond the plan are usage-based: €5 per 100k events over the 200k Growth allowance and €3 per GB for data kept past the included 30-day retention, on top of €29 per core-seat per month.
- Governance essentials — custom SSO/RBAC, audit logs, SLAs, ISO 27001 reports and custom DPAs — are Enterprise-only, so mid-size regulated teams cannot get them on Growth.
as of 2026-10-10
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Is LangWatch still active?
LangWatch is live · free plan, paid from €29/core-seat/mo · last verified 30 Oct 2026 · viability healthy.
Viability score
How well maintained and how widely used is LangWatch? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this
Last calculated: October 2026
How we score →What's new in LangWatch
Checked todayAcross the latest 5 updates: 3 feature updates, 1 launch and 1 changelog entry.
3.18.0 Instant Evals in the Trace Explorer, and Files in Datasets
You can now ask the Trace Explorer a question no filter can answer, and upload images and files directly into dataset cells for evaluation.
Introducing Instant Evals: evaluate your entire production history
Instant Evals queries all stored production conversations on demand and returns matches in minutes, instead of requiring a pre-configured evaluation run.
Instant Evals: Search Your Entire Production History
Ask a question of every conversation you have stored and get the matches back in minutes, closing the loop from production failure to evaluation.
Go SDK 1.0 and Search in Datasets
Go SDK reaches 1.0, the dataset editor gains search, and agents are discovered from connected providers.
3.17.0 Agent Testing v2 and Prompt Optimization in the Workbench
Simulations become Agent Testing, comparison runs arrive, and Langy can optimise prompts directly inside the workbench.
What do users say about LangWatch?
What people actually say about LangWatch — is it worth it?
Our community sentiment research written Jul 31, 2026 is more than 90 days old, so it is being re-reviewed against current data and is not shown.
How we verified this
Every listed fact about LangWatch— pricing, features, links, status — is re-read from the vendor's own pages by our verification pipeline and each change is logged with a date; the last pass ran on 30 Oct 2026. Pages mix model-assisted synthesis with human review; see how we verify.
Verification history
We have re-verified LangWatch 21 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.
- — changed SSL certificate
- — changed pricing page
- — changed features, integrations, who should skip it, who it suits, description, our verdict, pricing tiers, summary
- — changed SSL certificate, pricing page, limitations
- — changed switching in, switching out, use cases, summary, description, who should skip it, our verdict, setup time, pricing fit, features, pricing tiers, hidden costs, limitations
- — changed SSL certificate, pricing page
Showing the 6 most recent of 21 verification passes.
Free to cite with attribution — this page re-verifies continuously.
How much does LangWatch cost in 2026?
LangWatch: Free to start; paid plans from €29/core-seat/mo. Pricing last verified 30 Oct 2026.
Plans compared
Every published LangWatch tier, its price as the vendor publishes it, who it fits, and what it adds over the tier below. The 12-month cost projection follows.
| Plan | Price | Billing | Who it's for |
|---|---|---|---|
| Developer | Free | — | A solo engineer or two-person team evaluating whether simulation-based agent testing fits their workflow, with no credit card and no time limit.Adds: Starting free tier: 50k events per month, 14-day data access, 2 users, and 3 scenarios, 3 simulations and 3 custom evals. |
| Growth | €29/core-seat/mo | monthly, per seat | A product team running agent simulations in CI on production traffic, where more than two people need access and eval suites exceed a handful of tests.Adds: Adds 200k events included, 30-day retention, unlimited lite-users, and unlimited simulations, evals and prompts for €29 per core-seat per month. |
| Enterprise | Custom | quote | Regulated organisations that need custom SSO/RBAC, audit logs, SLAs, ISO 27001 reports and a choice of hybrid, self-hosted or on-premise deployment.Adds: Adds hybrid/on-prem deployment, custom retention, custom SSO/RBAC, audit logs and SLAs, a Forward Deployed Engineer and AWS or Google Marketplace billing. |
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
Where the pricing makes sense
The company stage and team size where LangWatch's pricing actually pencils out — and where peers do it cheaper.
The €0 Developer tier fits one or two engineers kicking the tyres, and Growth at €29 per core-seat per month with 200k events included fits a small product team running simulation tests in CI. Langfuse and other tracing-first tools can look cheaper if all you store is traces, because you are not paying for the test loop; teams that need agent simulations and governance at scale end up in the Enterprise tier, priced custom and billed through AWS or Google Marketplace.
Has LangWatch's pricing changed?
No plan price for LangWatch has moved since we began logging in Aug 2026; the pricing page is re-read on every verification pass.
Who should pick LangWatch?
Pick LangWatch if you are AI platform teams running agent simulations as continuous testing in CI, Voice AI teams simulating callers, latency and interruptions before launch, Teams needing on-demand Instant Evals across all stored production conversations, Regulated enterprises requiring self-hosting, audit logs, SSO and EU residency.
Look elsewhere if you are Solo developers who only want a generous free tracing tier, Teams that just need basic LLM observability with no testing or evals, Buyers who want flat usage pricing with no per-core-seat component, Companies wanting a completely unmanaged, SaaS-free deployment without Docker or Kubernetes work.
Skip it if Skip LangWatch if you want cheap trace storage and nothing else — the €29 per core-seat per month Growth plan only pays off if your team will actually write and maintain scenarios that gate merges in CI.
GeneratedReal-world workflow fit
Concrete scenarios for the personas LangWatch actually fits — and what changes day-one when you adopt it.
Instrument the agent with the OpenTelemetry SDK, describe the behaviours that must hold in plain language, and let Scenario generate the tests. Run them in pytest locally, then add the same scenarios to the pull request pipeline so a prompt edit cannot merge if scores regress.
Outcome: A prompt or tool-routing change that would have reached customers silently is caught pre-merge, and the failing conversation is replayed step by step in the simulation visualizer.
Wire the voice stack through the ElevenLabs or OpenAI Realtime adapter, simulate callers that interrupt mid-sentence and speak over background noise, and read TTFB plus p50/p95 latency next to the judge verdict on each call.
Outcome: Latency regressions and unsafe handling of interruptions are found before a single real caller experiences them, with a scored record to show stakeholders.
Route every LLM call through the AI Gateway using existing OpenAI, Anthropic, Azure or Bedrock keys, issue virtual keys per team and per customer with spend limits, and leave coding agents captured so Claude Code and Codex token spend lands in the same trace store.
Outcome: One place to see spend and behaviour for every AI call in the company, including the assistants engineers use, with budgets enforcing themselves instead of relying on a monthly spreadsheet review.
Use cases
- Run hundreds of synthetic multi-turn conversations against your support agent before it reaches customers
- Gate pull requests in CI so a prompt or agent change cannot merge if eval scores regress
- Simulate voice agent calls end to end with interruption and background-noise injection, scoring latency alongside quality
- Turn a production trace into a scenario test to reproduce a bug and prove the fix
- Centralise every LLM call behind one gateway endpoint with per-caller virtual keys and budgets
- Track token spend per session and per team across Claude Code, Codex and other coding agents
- Run online monitors on production traffic and alert when quality or cost drifts
- Ask a question of your entire stored production history with Instant Evals and get matches in minutes
Setup time & first value
How long it actually takes to get something useful out of LangWatch — broken out by persona, not the marketing-page minute.
A single engineer can get traces flowing in minutes with OpenTelemetry instrument-in-minutes setup, and the docs recommend LangWatch Skills through a coding assistant for a first scenario. Writing a scenario suite worth gating merges on is days, not hours. Expect a working simulation inside a coding agent session; expect a real CI gate within a sprint for a small team.
Switching to or from LangWatch
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From Langfuse: instrument via OpenTelemetry and both tools ingest the same GenAI spans, so you can run them side by side and move the simulation and eval work over first.
- →From a homegrown tracing setup: point the existing OTel exporter at LangWatch, then convert recurring production failures in the trace explorer into scenario tests.
- →From LangSmith: re-express datasets as SDK, notebook or UI experiments, then rebuild grader logic as custom evaluators or the built-in RAGAS, hallucination, toxicity and PII scorers.
- →From manual QA scripts: write the same behaviours as natural-language success criteria per scenario, then run them in pytest/vitest locally and in CI with no separate setup.
- ↗To Langfuse: because tracing is OpenTelemetry-native with full GenAI spec support, you can repoint the OTel exporter, though simulation scenarios do not travel with it.
- ↗To a plain OTel backend: traces move cleanly on the GenAI spec, but you rebuild judges, red-teaming and dataset tooling yourself.
- ↗To an in-house test harness: the open-source Scenario SDK (Python and TypeScript) is Apache 2.0, so the simulation layer can be lifted even if you leave the platform.
Integrations
Resources & guides
- Documentationlangwatch.ai
The Complete LLMOps Platform
Accelerate your agent development lifecycle with comprehensive observability, evaluations and agent simulations. Open-source platform, with over 3k stars on GitHub.
- Resourcelangwatch.ai
LangWatch Blog: AI Agent Testing and LLM Development Insights
Expert insights on AI agent testing, LLM monitoring, DSPy optimization, and evaluation frameworks. Technical guides for AI developers and engineers.
- Documentationlangwatch.ai
Observability & Tracing
Monitor, debug, and optimize your LLM applications with comprehensive observability and tracing capabilities
- Documentationlangwatch.ai
Evaluations Overview
Ensure quality and safety for your LLM applications with experiments, online evaluation, guardrails, and evaluators.
- Documentationlangwatch.ai
Overview
Organize, version, and optimize your AI prompts with LangWatch's comprehensive prompt management system
- Documentationlangwatch.ai
Self-Hosting Overview
Deploy LangWatch on your own infrastructure for full data control
- Documentationlangwatch.ai
Overview
Search, retrieve, and share LangWatch traces via the REST API. Traces capture the full execution of your LLM pipelines including all spans, evaluations, and metadata.
- Resourcelangwatch.ai
LangWatch Blog: AI Agent Testing and LLM Development Insights
Expert insights on AI agent testing, LLM monitoring, DSPy optimization, and evaluation frameworks. Technical guides for AI developers and engineers.
Tutorials & Learning
LangWatch LLM Optimization Studio
LangWatch
Getting started with LangWatch Optimization Studio (2/4): Datasets
LangWatch
YouTube returned 6 videos for “LangWatch”, and we withheld 4: 4 could not be judged, because “LangWatch” is a single word that other videos use for other things. Showing the 2 we can prove are about LangWatch.
Official links
What are the best LangWatch alternatives?
The closest alternatives to LangWatch we list are Langfuse, LangSmith and Phoenix. Ranked by direct product-type match, not category overlap.
| Alternative | Pricing | Starts at | Last verified |
|---|---|---|---|
| Langfuse | Freemium | Pricing not published | — |
| LangSmith | Freemium | Pricing not published | — |
| Phoenix | Freemium | Pricing not published | — |
Alternatives to LangWatch
View all
Tools that pair well with LangWatch
Common stack mates teams adopt alongside LangWatch, with the specific reason each pairing earns its keep.
Langfuse
Open-source LLM observability: trace agents, manage prompts, and run evals on one MIT-licensed platform.
LangSmith
LangSmith is agent and LLM observability from the LangChain team: trace, monitor, evaluate, and deploy AI agents.
Phoenix
Open-source LLM observability and agent evaluation you can trace, self-host, and eval end to end.
Frequently asked questions
Best-of guides
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