When to Implement an AI Gateway

The article explains what an AI gateway is — a centralized layer between applications and LLM providers that handles routing, authentication, rate limiting, observability, cost tracking, and safety guardrails. It describes the common progression from direct SDK usage to simple proxies and finally to a full AI gateway as teams scale across multiple models and use cases. Triggers for adopting a gateway include multiple teams using different models, finance and compliance demands (e.g., HIPAA/GDPR/SOC 2), lack of cost visibility, and operational risk from provider outages. A production setup centralizes provider credentials, enforces per-team budgets and rate limits, logs prompts/responses/tokens/costs, applies PII filtering and prompt-injection checks, supports provider failover, and can run in VPC/on-prem. The author cites TrueFoundry as a practical example and notes performance claims (350+ RPS on a single vCPU with sub-3ms latency) and Gartner recognition of the category.