LLM proxy vs AI gateway: what’s the difference and which one do you need?
Blog post from Portkey
As teams increasingly incorporate large language models (LLMs) into their operations, managing their use and scalability has given rise to two key infrastructure patterns: the LLM proxy and the AI gateway. An LLM proxy acts as a lightweight middleware that simplifies request routing and offers basic features like caching and logging, making it ideal for early-stage projects that prioritize flexibility over extensive governance. Conversely, an AI gateway provides a robust, production-grade infrastructure that offers centralized control, security, and observability, making it essential for organizations with complex needs involving multiple LLMs and teams. AI gateways support advanced capabilities such as access control, guardrails, audit logging, and multi-provider routing, treating LLMs as critical infrastructure requiring rigorous oversight. While LLM proxies are suitable for prototyping, AI gateways are indispensable as usage scales, offering governance and control to ensure compliance and cost efficiency in production environments.
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