Comet vs LangWatch

Comet is an AI developer platform covering two connected needs: traditional ML experiment tracking and management, and LLM and agent observability through its Opik product. On the MLOps side, it lets data scientists track and compare training runs, version models and datasets, and monitor production models, with support for frameworks like PyTorch, TensorFlow, Hugging Face, and scikit-learn. Opik, Comet's LLM observability and evaluation platform, adds tracing across 60+ integrations, automatic error detection with an AI assistant called Ollie that recommends fixes, test suites with LLM-as-a-judge evaluation, and production monitoring dashboards, including cost tracking for coding agents like Claude Code. Opik's core feature set is available as a free, self-hostable open-source download in addition to Comet's hosted cloud plans.

LangWatch is an LLM engineering platform built around AI agent testing, evaluation, and observability. It combines simulation-based agent testing with an automated evaluation loop and production-grade trace analysis, helping teams turn unpredictable agents into reliable systems. The platform is trusted by engineering teams at Backbase, PagBank, Visma, Deloitte, and others shipping mission-critical AI, and it positions itself as the loop-engineering layer between agent development and production confidence.

• Covers both classic ML experiment tracking and modern LLM and agent observability under one company.
• Opik's open-source option gives teams a genuinely free, self-hosted path with the full feature set.
• Broad framework support (PyTorch, TensorFlow, Hugging Face, scikit-learn) for the MLOps side.
• Cost intelligence for coding agents like Claude Code is a distinctive feature for teams managing AI spend.

• Simulation-driven testing with realistic text and voice user personas plus red teaming
• Closes the loop automatically: PM goal → plan → run → JudgeAgent score → PR via Langy
• OpenTelemetry-native observability with deep traces, token/cost telemetry, and topic clustering
• Flexible deployment with cloud, self-hosted, hybrid, VPC, plus enterprise security and compliance

• Having two related but distinct products, classic MLOps and Opik, can be confusing when first evaluating the platform.
• Free cloud tiers cap data volume, such as 25k spans/month, requiring a paid plan for production-scale usage.
• Enterprise features like SSO and compliance certifications are reserved for the custom-priced Enterprise tier.

• Pricing details are not listed on the landing page, so teams likely need to consult sales for enterprise or self-hosted plans.
• Self-hosted and hybrid deployment options require familiarity with Docker, Kubernetes/Helm, or VPC infrastructure.
• As a relatively newer platform, its community ecosystem and third-party resources are smaller than some more established LLMOps alternatives.
• AI-generated scenarios and rubrics from Langy still need human review to ensure they truly match real production requirements.