Dust vs Langfuse
Side-by-side comparison to help you choose the best tool.
Dust
paidDust is an AI workspace platform that lets teams build custom internal AI assistants connected to their company data sources such as Notion, Slack, GitHub, and Google Drive. It enables non-technical users to deploy context-aware AI agents that answer questions using live organisational knowledge. Dust focuses on enterprise-grade data privacy and access controls for secure internal deployments.
Langfuse
freemiumLangfuse is an open-source LLM engineering platform providing observability, prompt management, evaluations, and testing for LLM applications in production. It enables teams to trace LLM calls, manage prompt versions, run automated evaluations, and monitor costs and latency. Langfuse integrates with popular systems like LangChain, LlamaIndex, and OpenAI SDK.
| Feature | Dust | Langfuse |
|---|---|---|
| Pricing | paid | freemium |
| Category | - | - |
| Rating | 4.4 | 4.6 |
| Best For | Enterprise teams that need secure, context-aware AI assistants grounded in internal company knowledge. | Teams building and operating LLM applications who need full observability |
| Views | 4 | 6 |
Pros
- Deep integration with company data sources
- Strong privacy and access controls
- No-code assistant builder for non-technical teams
Cons
- Paid-only with no free tier
- Setup complexity for larger knowledge bases
Pros
- Comprehensive open-source observability
- Self-hostable for data privacy
- Rich integrations with LLM frameworks
Cons
- Self-hosting requires infrastructure knowledge
- UI can be complex for new users
- Custom AI assistant builder
- Connects to Notion, Slack, GitHub, Google Drive
- Role-based access controls
- Multi-agent workflows
- Enterprise SSO support
- LLM call tracing
- Prompt version management
- Automated evaluations
- Cost and latency monitoring
- Multi-framework integration