LangWatch vs Weights & Biases

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

	LangWatch Evaluation	Weights & Biases Evaluation
Tagline	Simulation-based testing, evaluation, and observability for LLM agents	The ML experiment tracker, now with LLM eval features.
Category	Evaluation	Evaluation
Pricing	Freemium· Developer: €0 · Growth: €29/ core-seat / month · Enterprise: Custom	Freemium· Free: $0/mo · Pro: Starts at $60/month · Enterprise: Custom plans · Personal: $0/mo · Advanced Enterprise: Custom plan
Model	Model-agnostic; supports OpenAI, Anthropic, AWS Bedrock, Azure OpenAI, Vertex AI, and any OpenTelemetry-instrumented LLM	Platform (any LLM)
Editorial score	—	8.4 / 10
Use cases	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	ML experimentsLLM evalWeave
Pros	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	Industry-standard for ML tracking Weave adds LLM-native eval Mature, reliable Strong enterprise features
Cons	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	Heavier UX than LLM-native tools LLM features still catching up
Website	langwatch.ai	wandb.ai

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

Pick Weights & Biases if

✅ Industry-standard for ML tracking
✅ Weave adds LLM-native eval
✅ Mature, reliable
✅ Strong enterprise features

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