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. Review collected by and hosted on G2.com.
What I dislike is that the platform can feel a bit complex initially. There’s a learning curve, especially if you’re new to LLMOps, and some areas like advanced analytics and documentation could be more polished. Review collected by and hosted on G2.com.
