What I like best about Portkey is that it brings structure to what is otherwise a very chaotic part of building AI products. When you're working with multiple LLMs, APIs, and edge cases, things break silently—and debugging becomes painful. Portkey acts as a unified gateway that gives you visibility, control, and reliability out of the box. The biggest win for me is observability + control. Having centralized logs, request tracking, cost insights, and performance metrics in one place makes a huge difference. Instead of guessing what went wrong, I can actually see how prompts behave, where latency spikes happen, and how much each request costs. It also simplifies multi-model integration. Rather than managing different APIs and retry logic across providers, everything runs through a single layer with built-in fallbacks, routing, and caching. That alone removes a lot of engineering overhead and lets me focus more on building features instead of infra. Another big plus is cost optimization. Features like caching, usage tracking, and model routing help avoid unnecessary LLM calls and keep spend predictable, which is critical when scaling.

We started using Portkey when our AI usage began getting messy across multiple models and providers. What stood out immediately was how it brings everything into one place without adding complexity. The biggest win for us has been visibility. You can actually see what’s happening with every request — cost, latency, failures — which makes debugging and optimization way faster. The observability layer alone is worth it, especially when you’re running multiple use cases in production.

The standout feature is the dashboard and analytics, which make observability and monitoring straightforward. Implementation is simple thanks to the available APIs, and there is a wide selection of LLM models and providers. It integrates smoothly into daily monitoring and troubleshooting routines.