LangSmith vs LangWatch

A side-by-side look at pricing, capabilities, pros, cons, and our editorial scores.

	LangSmith Evaluation	LangWatch Evaluation
Tagline	LangChain's eval + observability platform.	Simulation-based testing, evaluation, and observability for LLM agents
Category	Evaluation	Evaluation
Pricing	Freemium· Developer: $0 / seat · Plus: $39 / seat · Enterprise: Custom pricing	Freemium· Developer: Free forever (50k events/mo, 14-day retention, 2 users) / Growth: EUR 29 per core-seat/mo (200k events, then EUR 5 per 100k) / Enterprise: custom (hybrid, self-hosted, on-prem, SSO/RBAC, SLAs)
Model	Platform (any LLM)	Model-agnostic; supports OpenAI, Anthropic, AWS Bedrock, Azure OpenAI, Vertex AI, and any OpenTelemetry-instrumented LLM
Editorial score	8.7 / 10	—
Use cases	LLM tracingevalsLangChain integration	LLM agent regression testing in CIRAG answer-quality evaluationVoice-agent conversation simulationPrompt versioning and A/B testingProduction trace observability and cost trackingRed-team and jailbreak probingMulti-turn chatbot evaluationGuardrail and safety scoringDataset creation from production tracesLLM-as-a-judge scorecards
Pros	Tight LangChain integration Strong tracing UX Mature dataset/eval flows Reasonable per-seat pricing	Apache 2 open source with self-hosted and on-prem deployment options for regulated teams OpenTelemetry-native tracing works with virtually any framework or custom stack Combines observability, evaluation, simulation, and prompt management in one product instead of stitching four tools First-class multi-turn conversation simulation (text and voice), not just single-shot eval LLM-as-a-judge scoring can run on single outputs or entire conversations, including multimodal inputs SDKs in Python, TypeScript, and Go plus deep integrations with LangGraph, CrewAI, DSPy, Bedrock, Vertex, and Azure Generous free tier (50k events/mo) with no credit card, so teams can prove value before buying
Cons	Best value if you're on LangChain UI can feel dense	Feature surface is broad; smaller teams may find the UI heavier than a focused tracer like Langfuse or Phoenix LLM-judge evaluations add their own token cost that stacks on top of your agent's inference bill Growth plan bills per core-seat and per 100k events, which can escalate quickly on chatty production agents Self-hosting the full stack (Postgres, ClickHouse, workers) is non-trivial versus SaaS-only competitors Simulation quality depends heavily on how well you author scenarios; poorly written cases give false confidence
Website	www.langchain.com	langwatch.ai

Pick LangSmith if

✅ Tight LangChain integration
✅ Strong tracing UX
✅ Mature dataset/eval flows
✅ Reasonable per-seat pricing

Pick LangWatch if

✅ Apache 2 open source with self-hosted and on-prem deployment options for regulated teams
✅ OpenTelemetry-native tracing works with virtually any framework or custom stack
✅ Combines observability, evaluation, simulation, and prompt management in one product instead of stitching four tools
✅ First-class multi-turn conversation simulation (text and voice), not just single-shot eval

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