Arthur vs Weights & Biases

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

	Arthur Evaluation	Weights & Biases Evaluation
Tagline	Open-source toolkit for testing, tracing, and monitoring production AI agents.	The ML experiment tracker, now with LLM eval features.
Category	Evaluation	Evaluation
Pricing	Freemium· Open-source (MIT) + free SaaS tier; paid/enterprise plans on request	Freemium· Free personal; team from $50/mo per seat
Model	Multi-model	Platform (any LLM)
Editorial score	—	8.4 / 10
Use cases	agent-evaluationprompt-managementllm-tracinghallucination-detectionprompt-injection-defense	ML experimentsLLM evalWeave
Pros	MIT-licensed and self-hostable via Docker, Helm, or CloudFormation Built on OpenTelemetry so traces flow into existing observability stacks Framework-agnostic: works with LangChain, LlamaIndex, OpenAI, Anthropic, Vercel AI SDK Covers full lifecycle: prompt versioning, A/B testing, tracing, and online evals Includes guardrail evaluators for PII and prompt injection out of the box	Industry-standard for ML tracking Weave adds LLM-native eval Mature, reliable Strong enterprise features
Cons	Agent toolkit is newer than the company's ML-monitoring heritage; ecosystem still maturing Self-hosting Helm/CloudFormation deployment expects real DevOps capacity Paid SaaS pricing is gated behind sales contact	Heavier UX than LLM-native tools LLM features still catching up
Website	arthur.ai	wandb.ai

Pick Arthur if

✅ MIT-licensed and self-hostable via Docker, Helm, or CloudFormation
✅ Built on OpenTelemetry so traces flow into existing observability stacks
✅ Framework-agnostic: works with LangChain, LlamaIndex, OpenAI, Anthropic, Vercel AI SDK
✅ Covers full lifecycle: prompt versioning, A/B testing, tracing, and online evals

Pick Weights & Biases if