Your AI model might look great on paper—but do you really know what's happening beneath the surface?
Traditional monitoring might signal that something is wrong, but AI Observability helps you understand exactly why it happened and how to fix it.
Effective AI Observability means:
- Full Lifecycle Visibility: Spotting issues early, from initial training through real-world deployment.
- Cost Control: Tracking resources and expenses in real-time to avoid budget surprises.
- Rapid Troubleshooting: Quickly pinpointing and solving model failures.
- Clear Explainability: Understanding exactly why your AI makes the decisions it does, building trust with users.
We're currently using Langfuse (YC W23), which makes this process straightforward—tracking crucial details like latency, token usage, and execution paths, enabling continuous refinement and confident scaling.