API Gateway

An API gateway is a service that acts as the single entry point for AI API requests. It sits between client applications and backend AI services, handling cross-cutting concerns like authentication, authorisation, rate limiting, request routing, response caching, and monitoring. For AI applications, API gateways serve additional specialised functions: routing requests to different AI models based on the task or user tier, implementing fallback logic (switching to a backup model if the primary is unavailable), managing token budgets across teams or applications, and providing unified logging for all AI interactions. AI-specific API gateways like Portkey, LiteLLM, and cloud provider gateways add features tailored to LLM workloads: prompt caching, cost tracking by model and team, semantic caching (returning cached responses for semantically similar queries), and automatic retry with model fallback.

As AI adoption scales across an organisation, managing access to AI services becomes critical. An API gateway provides centralised control over who can access which AI capabilities, how much they can use, and what it costs. Without this control, AI costs can spiral, security can be compromised, and usage cannot be tracked. API gateways also enable multi-provider strategies. By abstracting the specific AI provider behind a unified API, organisations can switch between models (GPT, Claude, Gemini, open-source) without changing client applications. This reduces vendor lock-in and enables cost optimisation by routing requests to the most cost-effective model for each task. Reliability is another key benefit. API gateways can implement circuit breakers (stopping requests when a service is unhealthy), automatic failover (routing to backup models), and request queuing (smoothing traffic spikes). These patterns ensure that AI capabilities remain available even when individual providers experience issues.