
LangWatch
8.2#4 in ai agent observability toolsFrom €29 a monthFree planWebAPILinuxmacOS
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| Fact | LangWatch | MLflow Tracing |
|---|---|---|
| Company founded | LinkedIn lists LangWatch as founded in 2023. | Databricks says it was founded in 2013 by the creators of Apache Spark, Delta Lake, MLflow, and Unity Catalog. |
| Company headquarters | LinkedIn lists headquarters at Herengracht 551, Amsterdam, Netherlands. | Databricks says it is headquartered in San Francisco. |
| Documentation | Documentation covers introduction, integrations, CLI, MCP, agent testing, observability, evaluations, datasets, prompt management, self-hosting and API reference. | MLflow documentation has separate sections for LLM and agent development, tracing, and evaluation, and for traditional machine-learning workflows. |
| Integrations | Official integrations cover OpenAI, Anthropic, Google Gemini, Bedrock, Azure OpenAI, Vertex AI, Groq, Ollama and LiteLLM. | The integrations directory organizes automatic tracing support across frameworks, coding agents, and model providers. |
| Licence | The core platform is Apache 2.0; enterprise modules require a commercial production licence and the SDKs are MIT licensed. | The public MLflow repository identifies its licence as Apache-2.0. |
| Rating on G2 | 0.0 out of 5 from 0 reviews | No product rating or review count for MLflow Tracing is stated on the opened G2 MLflow marketplace page. |
| Self-hosting | Self-hosting is available with a Node.js command for local use and Docker, Kubernetes/Helm or VPC deployment options. | The repository's setup instructions show starting an MLflow server locally and connecting tracing to it. |
| What it does | LangWatch is an AI-agent engineering platform for testing, evaluating, observing, governing and improving agents across development and production. | MLflow Tracing records inputs, outputs, and metadata for intermediate steps in LLM applications and AI agents so teams can inspect behavior and investigate issues. |
LangWatch suits AI teams shipping agents, including developers, product owners, QA, and domain experts working on evaluation workflows. Teams can choose managed cloud, self-hosted, or hybrid deployment.
MLflow Tracing suits teams building LLM applications or agents that need visibility into request steps, tool calls, latency or token costs. It supports both automatic integrations and manual instrumentation, with trace data hosted on their infrastructure.