AI Gateway High Margin Layer

AI Gateway matters because it lets Kong sell more software to the same customer without taking on model serving costs. The product sits on the same gateway runtime many customers already use for API traffic, then adds AI specific controls like token rate limits, prompt filtering, PII scrubbing, caching, routing, and observability. That means Kong can price AI governance as an added control layer while the expensive part, model inference, is still paid to OpenAI, Anthropic, or another model provider.

• Kong has built AI Gateway directly on top of Kong Gateway, not as a separate inference stack. Its own product materials describe the same core runtime and add low code controls for prompt management, PII sanitization, token rate limiting, traffic observability, caching, and multi model governance.
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• Konnect already sells and manages multiple gateway deployment modes, including self hosted data planes and Kong managed dedicated cloud gateways. That makes AI Gateway a feature layer inside an existing control plane and billing relationship, which is usually easier to attach to current enterprise contracts than selling a brand new infrastructure product.
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• Competitors show the same margin logic. Cloudflare passes through provider inference pricing with no markup and charges separately for surrounding services like logs and guardrails. Vercel also bases rates on provider list price and states no markup when customers bring their own key. The money is in control, routing, and workflow, not in running the model.
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The next step is for AI gateways to become the default policy checkpoint for enterprise model traffic, much like API gateways became the checkpoint for service traffic. If that shift continues, Kong can grow wallet share inside its installed base by turning every new AI app, agent, and model connection into another governed flow through software it already runs.