There are definitely some places where Portkey can be improved. Overall, the experience has been positive, but the first area is analytics and reporting. While the observability feature is excellent, we would like to have richer historical analytics and customizable dashboards. For example, it would be useful to see trends by applications and teams or features over long periods without exporting data to an external BI tool. Another area is governance for large organizations. As AI adoption grows, enterprises need more granular, role-based access control. We would like to see more advanced AI evaluation capability built into the platform itself, including features such as prompt versioning and automated quality scoring and regression testing. Finally, while the platform is supported with multiple providers, we would welcome even more intelligent routing capabilities, such as automatically selecting the best model based on latency, cost, and task complexity using configurable policies. These are not major pain points, but they are enhancements that would make an already strong platform even more valuable for an organization that scales their AI workloads. Documentation and onboarding could be enhanced. Portkey is developer-friendly, but we need more end-to-end references, architecture, and implementation guides for common AI patterns. This would help teams adopt it even faster. I did not give Portkey a perfect score because while it has become a foundational component in our AI stack and solves several operational challenges, I would still like to see deeper analytics, stronger enterprise governance features, and more built-in AI evaluation capabilities, especially for prompt testing, regression analysis, and model benchmarking. I would be comfortable recommending Portkey to any organization that is building multiple AI applications or wants to manage a scalable way to handle LLM providers and produce AI traffic.
