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AI Gateway Architecture: The New Infrastructure Layer for LLM Apps (2026)

How to design and diagram an AI gateway architecture. Covers LLM routing, fallback chains, cost controls, observability, rate limiting, and prompt caching — with architecture diagrams and prompt templates.

R
Ryan·Senior AI Engineer
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An AI gateway (also called an LLM gateway or LLM proxy) is a centralized infrastructure layer that sits between your application services and the LLM providers they call. It handles routing, load balancing, fallbacks, authentication, rate limiting, cost tracking, caching, and observability — for all LLM traffic in one place. Just as an API gateway unified HTTP traffic management in the microservices era, the AI gateway has become the standard architectural pattern for production LLM applications in 2026.

Without an AI gateway, every service that calls an LLM has to implement retry logic, cost controls, model fallbacks, and observability independently. With one, those cross-cutting concerns move to a single managed layer, making your application services thinner and your LLM operations observable and controllable.

What an AI gateway does

  • Unified provider interface: Translates a single OpenAI-compatible API format to any provider — Anthropic, Google, Mistral, Azure OpenAI, Bedrock, local Ollama instances — so application code is provider-agnostic
  • Semantic routing: Routes requests to different models based on cost tier, task type, or request latency requirements (e.g., fast/cheap model for classification, large model for generation)
  • Fallback chains: When a primary model is overloaded or unavailable, automatically retries with a fallback model (e.g., GPT-4o → Claude claude-sonnet-4-6 → Mistral Large) with configurable backoff
  • Prompt caching: Caches semantically identical requests (exact-match or vector-match) so repeated prompts don't consume tokens — critical for high-volume applications with repeated system prompts
  • Rate limiting and quota management: Enforces per-user, per-team, or per-application rate limits and token budgets to prevent runaway costs
  • Observability: Centralized logging of all LLM requests — model used, token counts, latency, cost, user ID — exportable to Prometheus, Datadog, or custom dashboards
  • PII redaction: Strips sensitive data from prompts before they reach third-party providers — critical for GDPR compliance and enterprise security policies
  • A/B model testing: Routes a configurable percentage of traffic to an alternative model to compare response quality before committing to a model change

AI gateway vs. API gateway: what's different

A traditional API gateway (Kong, AWS API Gateway, Traefik) handles HTTP routing, auth, and rate limiting — but knows nothing about tokens, model quality, or semantic caching. An AI gateway adds:

  • Token-level metering (not just request count)
  • Model-aware routing and fallback logic
  • Semantic cache (vector similarity, not just URL cache)
  • Prompt/completion logging with LLM-specific metadata
  • Streaming response proxying with SSE passthrough
  • Provider credential rotation and multi-key pooling

Many organizations use both: an API gateway handles general HTTP traffic management; the AI gateway sits behind it, specifically managing all LLM-bound traffic.

AI gateway products in 2026

  • LiteLLM: Open-source Python proxy, 100+ provider support, OpenAI-compatible, most widely deployed self-hosted option
  • Portkey: Managed service with observability dashboard, prompt versioning, guardrails, and multi-provider routing
  • Kong AI Gateway: Extends the Kong API gateway with AI-specific plugins — semantic cache, rate limiting by tokens, AI model passthrough
  • Solo.io Agent Gateway: Built specifically for agentic traffic with MCP tool call routing and A2A protocol support
  • Helicone: Observability-first proxy with one-line integration, prompt caching, and spend tracking
  • Azure AI Foundry Gateway: Managed Azure offering with load balancing across Azure OpenAI deployments and PTU quota management

Prompt templates for AI gateway architecture diagrams

Standard multi-service AI gateway

"AI gateway architecture for a SaaS application with multiple LLM consumers. Three application services — a Customer Support chatbot (Next.js), a Document Summarizer (FastAPI), and a Code Review Agent (Node.js) — all send LLM requests to a central LiteLLM gateway deployed on Kubernetes. The gateway authenticates each service via API key, routes requests based on model preference header, and maintains per-service token budgets (Support: 100K tokens/day, Summarizer: 50K, Code Review: 200K). Routing config: Support uses GPT-4o mini with fallback to Claude Haiku; Summarizer uses Claude claude-sonnet-4-6 with no fallback; Code Review uses Claude claude-opus-4-8 with fallback to GPT-4o. A semantic cache (Redis vector store, cosine similarity 0.95 threshold) sits in front of provider calls. All request logs (model, tokens, latency, cost, service ID) are sent to Prometheus, visualized in Grafana. Provider credentials (Anthropic, OpenAI keys) are stored in AWS Secrets Manager. Show the gateway as a central component with incoming arrows from each service and outgoing arrows to each provider, with the cache and observability stack as side components."

Enterprise AI gateway with compliance controls

"Enterprise AI gateway with compliance controls. Employee-facing applications (HR chatbot, IT support bot, legal research assistant) route all LLM traffic through an AI gateway (Portkey Enterprise, on-prem deployment). Before any request leaves the network: a PII redaction filter strips names, emails, SSNs, and financial data using a regex + NER model pass; a content policy check blocks prompts that violate acceptable-use policy. Redacted prompts are routed to the approved provider list (Azure OpenAI in the EU region only, for GDPR compliance; no OpenAI direct API calls allowed). Responses are logged to an immutable audit store (Azure Blob Storage with WORM policy) for 7-year retention. The gateway enforces per-department monthly spend caps (HR: $500/mo, IT: $2,000/mo, Legal: $5,000/mo) and alerts the finance team at 80% of cap. Show PII redaction and policy check as sequential filter boxes in the request path before the provider call."

Components to include in your diagram

  • Application services: Each service that calls the gateway, with its service name and token budget
  • AI gateway: The central proxy, labeled with the product (LiteLLM, Portkey, etc.) and its routing logic
  • Request pipeline: Auth → rate-limit check → PII filter → semantic cache lookup → provider routing
  • Semantic cache: Redis or vector DB used for prompt deduplication
  • LLM providers: Each provider the gateway can route to, with the fallback chain labeled
  • Observability stack: Where logs and metrics are sent (Prometheus, Datadog, etc.)
  • Secrets store: Where provider API keys are retrieved from

Related guides: API gateway architecture, AI agent architecture diagrams, securing agentic AI systems, and LLM architecture diagrams.

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