The most valuable features are the unified AI gateway, observability, price calculations, routing controls, and guardrails. The universal API makes it easier to switch between and compare models without rewriting large parts of the application. For the organization, Portkey was providing two endpoints, one for the US and another for Europe, which was easily manageable. Some models were supported for the US, some not, some for the EU, and some for both. Switching between providers is very easy with Portkey. The fallback and retry capabilities are also useful because production LLM applications need resilience when a provider is slow, rate-limited, or temporarily unavailable. Cost and latency tracking are valuable because LLM usage can become difficult to manage across teams, but these can be easily managed through Portkey. Observability is the best feature because at the production grid application, the focus is usually on observability, such as how the end user is using it, what the latency is, how many errors occurred within time frames, which models were used, and how many tokens were consumed. These aspects are easy to manage in Portkey. Portkey has significantly improved the organization by streamlining the AI processes being followed. Portkey improves the workflow for centralizing LLM operations instead of every application team building its own logging, routing, fallback, and cost tracking logic. Portkey provides a shared layer for these capabilities so it can be synced between all teams. It helps reduce engineering efforts when experimenting with different providers and models. It also improves visibility into production behavior because request volumes, latency, cost, errors, and model usage can be seen in one place. For teams building multiple GenAI applications, the biggest improvement is standardization. Portkey makes it easier to enforce common practices across observability, reliability, and governance.
