AI Gateway Architecture Diagrams
Visualize AI gateway (LLM proxy) infrastructure — multi-model routing, fallback chains, semantic caching, rate limiting, observability, and PII redaction. Generate accurate AI gateway architecture diagrams from plain English in seconds, whether you're deploying LiteLLM, Portkey, Kong AI Gateway, or a custom LLM proxy.
What is an AI gateway?
An AI gateway is a centralized reverse proxy that sits between your application services and the LLM providers they call. It handles the cross-cutting concerns of LLM access in one place: authentication, routing requests to the right model, managing fallbacks when a model is unavailable, caching repeated prompts, enforcing rate limits and cost budgets, logging all LLM traffic for observability, and stripping PII before requests leave your network. Without an AI gateway, each service that calls an LLM has to implement all of these independently.
The pattern emerged as a standard in 2025–2026 as organizations began operating multiple LLM-powered services simultaneously and needed a unified control plane for their LLM traffic — analogous to what API gateways did for HTTP microservice traffic in the previous era.
Key components to diagram
Routing layer
The routing layer receives LLM requests from application services and selects the appropriate model based on routing rules. Rules can be based on: a model preference header set by the caller, the estimated complexity of the request, cost tier (cheap model for classification, expensive model for generation), or latency requirements. Show the routing layer as a decision component with labeled routing rules and arrows to each target provider.
Fallback chain
When a primary model is rate-limited, overloaded, or returns an error, the gateway automatically retries with the next model in the fallback chain. A typical chain might be: Claude Opus 4 → GPT-4o → Claude Sonnet 4 → Mistral Large, with configurable retry delay and maximum attempts. Show the fallback chain as an ordered list with arrows showing the fallback direction and the trigger condition (error type or latency threshold).
Semantic cache
A semantic cache stores the responses to recent LLM requests and returns a cached response when a new request is semantically similar (above a configurable cosine similarity threshold). This avoids token consumption for repeated or near-identical prompts — critical for high-volume use cases with predictable request patterns. The cache backend is typically a vector database (Redis with vector search, Qdrant, or pgvector) that stores request embeddings alongside cached responses.
Observability and cost tracking
Every request that flows through the gateway is logged with: the caller service identity, the model used, input/output token counts, latency, cost (computed from provider pricing), cache hit/miss status, and any errors. Aggregate these into per-service, per-model, and per-day cost dashboards. Show the observability pipeline in your diagram: gateway → metrics (Prometheus/Datadog) → visualization (Grafana) and gateway → log store (BigQuery/S3) → cost dashboard.
AI gateway products (2026)
- LiteLLM: Open-source Python proxy supporting 100+ providers, OpenAI-compatible API, Kubernetes-ready
- Portkey: Managed service with observability dashboard, guardrails, prompt versioning, and A/B testing
- Kong AI Gateway: AI plugins on top of the Kong API gateway — semantic cache, token-based rate limiting, AI model passthrough
- Helicone: Observability-first one-line proxy with spend tracking and prompt caching
- Azure AI Foundry Gateway: Managed Azure offering for load balancing across Azure OpenAI deployments
Example prompt
Frequently asked questions
Do I need an AI gateway if I only use one LLM provider?
Even with a single provider, an AI gateway provides value through centralized observability, rate limiting, and prompt caching. As your LLM usage scales, you'll want cost visibility and the ability to add a fallback provider without changing application code. Start simple — a lightweight proxy like Helicone or LiteLLM can be added with one line of configuration — and you'll be glad you have it when provider outages happen.
What is the difference between an AI gateway and a traditional API gateway?
A traditional API gateway (Kong, AWS API Gateway) handles HTTP routing and request-count rate limiting but knows nothing about tokens, model quality, or semantic similarity. An AI gateway adds token-level metering, model-aware routing and fallback logic, semantic caching, and LLM-specific observability. Many teams use both: an API gateway handles general HTTP traffic; the AI gateway sits behind it managing LLM-bound traffic specifically.
How does an AI gateway handle streaming responses?
LLM streaming responses use Server-Sent Events (SSE) — the model streams tokens progressively rather than waiting for the full response. A production AI gateway must proxy SSE streams without buffering the full response (which would defeat the purpose of streaming). LiteLLM and Portkey both support streaming passthrough. Note that semantic caching is incompatible with streaming for partial responses — the full response must be received before it can be stored in the cache.
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