Infrastructure role: Kong AI Gateway is a unified API gateway and control plane for LLM traffic — a routing and governance layer rather than an LLM hosting or inference engine. Its primary backend value is centralized multi-provider routing, request governance, and observability for AI traffic (reducing vendor lock‑in, enforcing security/policy, and standardizing request/response handling across heterogeneous model providers).
Architectural Integration & Performance
Kong AI Gateway sits between application clients and disparate LLM providers (OpenAI, Anthropic, Google, AWS/Azure hosted models, Mistral, Llama variants). It does not execute model inference; instead it implements a control plane that routes, transforms, and policies traffic to upstream inference endpoints.
Routing and governance are implemented as API‑gateway primitives: upstream selection and failover, request/response transformation, policy enforcement (authentication, rate limits, schema checks), and telemetry capture. Performance characteristics (per‑request TTFT, tokens/sec, VRAM/GPU utilization) are determined by the selected upstream providers; Kong does not emit native inference throughput metrics because it does not run models.
Integration points focus on operational control: MCP (Model Context Protocol) traffic governance, semantic security filters, PII sanitization, automated RAG pipelines to reduce hallucinations, and standard gateway deployments (Docker/Kubernetes self‑hosted, Kong Konnect cloud, Konnect Dedicated Cloud Gateways SaaS). There are no host‑level GPU/quantization or low‑level inference optimizations within Kong itself.
Core Technical Capabilities
- Native MCP (Model Context Protocol) traffic governance — central policy plane for context propagation, provenance tagging, and routing decisions across model providers.
- Multi‑provider routing and dynamic upstream selection — route requests to OpenAI, Anthropic, Google, AWS, Azure, Mistral, and self‑hosted Llama endpoints with configurable failover and vendor‑level selection policies.
- Automated RAG pipelines — inline support for RAG workflow orchestration (retrieval injection and pipeline gating) to reduce hallucinations; acts as the control point for retrieval providers rather than performing document embedding/inference itself.
- PII sanitization and semantic security — language‑aware PII scrubbers (supporting 18 languages) and semantic filters applied at the gateway to reduce sensitive data exposure to upstream providers.
- Traffic shaping and load balancing — request shaping and upstream load distribution to balance traffic across providers or endpoint clusters; latency and error‑based routing policies supported at the gateway level.
- End‑to‑end observability and telemetry capture — centralized logging, tracing, and metric export for requests to upstream LLMs, enabling integration with external observability stacks (APM, SIEM) though specific vendor integrations depend on deployment.
- Deployment portability — container/Kubernetes deployment and SaaS options via Kong Konnect and Dedicated Cloud Gateways; acts as a control plane compatible with common infra patterns.
Security, Compliance & Ecosystem
Model ecosystem: routes to major public providers (OpenAI, Anthropic, Google, AWS/Azure hosted models), specialist vendors (Mistral), and Llama family endpoints. It provides governance and security controls but does not replace provider‑side safeguards.
Security features include PII sanitization in multiple languages and semantic security enforcement applied at the gateway. Kong AI Gateway provides centralized policy enforcement and observability that helps meet enterprise security controls, but there is no public confirmation in the referenced material of SOC2/HIPAA/ISO 27001 certifications or of a Zero Data Retention (ZDR) guarantee for Kong itself — those are matters for deployment configuration and service‑level agreements.
Operational deployment options: self‑hosted (Docker/Kubernetes) and managed SaaS (Kong Konnect and Konnect Dedicated Cloud Gateways). BYOC or serverless execution of inference is not performed by Kong — it must be paired with upstream inference platforms (vLLM, Together, Replicate, cloud vendor serving) when low‑level inference control, quantization, or GPU management is required.
The Verdict
Kong AI Gateway is the correct building block when the requirement is centralized routing, security, policy enforcement, and observability across multiple LLM providers. It is not a substitute for LLM serving platforms that provide per‑token cost control, GPU/quantization tuning, or raw inference metrics.
Compared with raw API calls or ad‑hoc DIY routing, Kong adds deterministic orchestration: centralized MCP governance, multi‑provider failover, PII/semantic filters, RAG pipeline controls, and gateway‑level telemetry. Compared with deploying a bespoke orchestration layer, Kong reduces integration surface and provides tested gateway primitives and managed deployment options.
Who should adopt it: DevOps and platform teams that need centralized control over multi‑vendor LLM traffic at scale (policy enforcement, vendor rotation, and observability); RAG engineers that require a policy and routing control plane to enforce retrieval gating and sanitizer policies across workflows; and enterprise architects wanting gateway‑level PII/semantic filtering before data reaches external providers. If the priority is low‑level inference efficiency, quantization, hardware sizing, or benchmarked TTFT/TPS, pair Kong with a dedicated LLM serving platform — Kong will manage and secure the traffic but will not alter inference characteristics.