This looks like a powerful step toward making prompt engineering more scalable and production-ready. The version control approach, along with staging environments and real-time analytics, seems particularly useful for teams handling high-volume AI workloads.

One question: How do you handle prompt drift over time? As models evolve, prompt effectiveness can degrade—do you provide any automated testing or monitoring to detect when a deployed prompt needs adjustment?

Looking forward to exploring Portkey’s capabilities.