Langfuse vs Windmill
Side-by-side comparison of two AI agent tools
Langfuseopen-source
🪢 Open source LLM engineering platform: LLM Observability, metrics, evals, prompt management, playground, datasets. Integrates with OpenTelemetry, Langchain, OpenAI SDK, LiteLLM, and more. 🍊YC W23
Windmillopen-source
Open-source developer platform to power your entire infra and turn scripts into webhooks, workflows and UIs. Fastest workflow engine (13x vs Airflow). Open-source alternative to Retool and Temporal.
Metrics
| Langfuse | Windmill | |
|---|---|---|
| Stars | 35.2k | 18.1k |
| Star velocity /mo | 1.8k | 317.80748663101605 |
| Commits (90d) | 2.0k | 1.2k |
| Releases (6m) | 10 | 10 |
| Overall score | 0.9350831133601574 | 0.8726503315063926 |
Pros
- +Open source with MIT license allowing full customization and transparency, plus active community support
- +Comprehensive feature set combining observability, prompt management, evaluations, and datasets in one platform
- +Extensive integrations with major LLM frameworks and tools including OpenTelemetry, LangChain, and OpenAI SDK
- +Multi-language support with automatic UI generation from scripts in Python, TypeScript, Go, Bash, SQL, and more
- +High performance workflow engine claiming 13x faster execution than Airflow
- +Self-hostable open-source solution with AGPLv3 license providing full control and customization
Cons
- -May require significant setup and configuration for self-hosted deployments
- -Could be overwhelming for simple use cases that only need basic LLM monitoring
- -Self-hosting requires technical expertise and infrastructure resources
- -AGPLv3 license may restrict some commercial use cases and require careful compliance consideration
- -Being a comprehensive platform may introduce complexity for simple automation tasks
- -Self-hosting requires infrastructure management and maintenance overhead
Use Cases
- •Production LLM application monitoring to track performance, costs, and identify issues in real-time
- •Prompt engineering and management for teams collaborating on optimizing model prompts and tracking versions
- •LLM evaluation and testing to measure model performance across different datasets and use cases
- •Building internal APIs and webhooks from existing scripts without additional infrastructure
- •Creating automated workflows for background jobs and data processing pipelines
- •Developing low-code internal applications with custom UIs for non-technical team members