Generate LLM Deployment Architecture Diagrams with AI

Map your complete LLM deployment infrastructure — inference serving, API gateway, semantic caching, guardrails, cost tracking, and observability. Describe your stack in plain English and get a professional architecture diagram ready for architecture reviews, incident documentation, or engineering onboarding.

The challenge

LLM deployment architectures are harder to communicate than traditional services. There are new components that most engineers haven't worked with before — LLM gateways, semantic caches, guardrail layers, token usage trackers, model fallback routers — and they interact in ways that aren't obvious from a traditional service diagram. Without a clear architecture document, onboarding new engineers, planning for scale, and justifying infrastructure spend to leadership all become harder.

The solution

Describe your LLM deployment the way you'd explain it to a new team member:

"Client requests go to a Node.js API server that assembles the prompt from a system prompt template, conversation history from Redis, and user input. Requests go through LiteLLM proxy which checks a semantic cache (Redis + embedding similarity) first. Cache misses hit GPT-4o as primary, with automatic fallback to claude-sonnet-4-6 on 429 or 5xx errors. All requests pass through a guardrails layer that checks for PII and prompt injection before sending to the model. Token usage and cost per user are logged to PostgreSQL. Langfuse traces every LLM call with latency, tokens, and model version."

From that description, you get a complete LLM deployment architecture diagram showing every layer from client to model, with costs and observability wired in. Use chat-based editing to add auto-scaling policies, adjust caching TTLs, or annotate budget boundaries.

LLM deployment diagrams we support

LLM API integration architecture
Application-to-model request flows including authentication, rate limiting, retry logic, streaming response handling, and error handling for OpenAI, Anthropic, and Google APIs.
Self-hosted inference serving
GPU cluster architecture for vLLM, TGI, or Ollama deployments, including load balancing, auto-scaling, model sharding, and KV cache management.
Multi-model routing and fallback
Intelligent routing architectures that classify requests by task type and route to the optimal model — with automatic fallback chains on failure or budget exhaustion.
LLM observability and cost tracking
Observability architectures showing how token usage, latency, and model quality metrics flow from inference to dashboards, alerts, and cost attribution systems.

Perfect for

AI platform team architecture documentation
Infrastructure architecture reviews before production launch
Cost optimization audits — visualize what drives LLM spend
Onboarding new AI engineers to your inference stack
Incident postmortems — document the system that failed
Security reviews of data flows through LLM infrastructure

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