Six AI Gateways Compared: Routing, Governance, Caching and Observability in 2026
An AI gateway acts as a single control plane that sits between application code and LLM providers, standardizing access, rate limiting, caching, and logging across all model calls. Without such a layer, teams end up managing provider keys, quotas, and audit records in separate, ungoverned silos that grow harder to control as more services adopt AI. The six gateways reviewed fall into two broad categories: infrastructure-first tools that extend existing API or Kubernetes platforms, and LLM-native tools built specifically around model routing and observability. Key evaluation criteria include provider coverage, per-team spend attribution, semantic caching, role-based access control, and durable audit logging. The right choice depends on whether the primary constraint is infrastructure governance, flexible model routing, or linking production traffic to measurable output quality.
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