Every prompt your agent sends, every response it receives — it flows through Kong's AI Gateway before it reaches your LLM. That's true whether you're routing to OpenAI, Anthropic, a hosted model on Vertex AI or Bedrock, or a self-hosted model running on your own inference infrastructure.
This isn't middleware for middleware's sake. The AI Gateway does things your LLM provider can't:
PII masking before prompts leave your perimeter. If an agent is reasoning over customer data, the raw PII gets masked before the prompt hits the model. The model sees anonymized context. The original data never leaves your environment — not to a cloud API, not to any external endpoint.
Confidence threshold enforcement. If the model returns a low-confidence response below a threshold you set, Kong can route to a fallback model, trigger a human review workflow, or simply block the action. You decide what acceptable looks like — and that policy travels with you across providers and deployments.
Semantic caching. Agents ask similar questions repeatedly. Kong recognizes semantically equivalent requests and serves cached responses — reducing latency, cutting LLM costs, and reducing load on your inference endpoints regardless of which model or provider serves them.
Prompt injection detection. Adversarial inputs designed to manipulate agent behavior get caught at the gateway before they reach the model.
Cost attribution and metering. Every token consumed, by every agent, attributed to the right team or workflow. When AI costs become significant at scale — and they will — you need this data. Especially if you're running across multiple providers or model tiers.