Kong AI Gateway on GCP Tackles Token Costs, MCP, and Agentic API Traffic

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Platform teams running Kong Gateway on Google Kubernetes Engine are facing new challenges as product teams ship LLM-backed features, RAG pipelines, and autonomous agents that behave very differently from standard REST traffic. Traditional API gateways struggle because LLM costs scale with token usage rather than request count, backends like GPT, Gemini, and Claude are not interchangeable, and agentic clients can autonomously chain dozens of tool calls. Kong's AI Gateway, available from version 3.6 onward, extends the core gateway with AI-specific plugins covering token-aware rate limiting, semantic caching, prompt compression, and Model Context Protocol support. On GCP, the recommended setup pairs Kong on GKE with Cloud Load Balancing, Vertex AI as a model backend, and Memorystore for Redis to manage distributed rate-limit state. The approach allows teams to govern LLM traffic — including cost controls and agentic workloads — without replacing their existing Kong infrastructure.

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