Generate AI Product Architecture Diagrams with AI

Visualize your AI product's full architecture. Describe your LLM backend, RAG pipeline, prompt management system, streaming infrastructure, or multi-model routing in plain English and get a professional architecture diagram ready for engineering reviews, investor decks, or team documentation.

The challenge

Building an LLM-powered product introduces architectural decisions that have no equivalent in traditional SaaS — streaming token delivery end-to-end, context assembly pipelines that must fit within model limits, prompt versioning with canary deployments, per-tenant cost isolation across models, and fallback routing when a provider goes down. These decisions are hard to communicate clearly because the patterns are new, the vocabulary is evolving, and most diagramming tools were designed for databases and APIs, not LLM inference flows. Product teams end up with no shared visual model for how their AI backend actually works.

The solution

Describe your AI product architecture the way you'd brief a new engineer:

"AI writing assistant built on Next.js and Supabase. User submits a request → API route assembles context: active system prompt from prompt_versions table, last 10 messages from conversations table, user document from Supabase Storage. Token count checked — if over 80k, oldest messages trimmed. Context sent to Anthropic Claude claude-sonnet-4-6 with stream: true. Server-Sent Events pipe tokens to the React frontend. Usage logged to usage_events table: user_id, tokens_in, tokens_out, cost, latency, model, prompt_version_id. Monthly token budget checked before each call — 429 returned if over limit. Admin UI for deploying new prompt versions with rollback."

From that description, you get a complete diagram showing the streaming path, context assembly, cost tracking, and prompt management. Use chat-based editing to add model fallback routing, annotate where PII scrubbing happens, or show the multi-tenant isolation boundary.

AI product architecture diagrams we support

LLM backend and streaming pipeline
End-to-end diagram from user request through context assembly, LLM API call (with streaming), and token delivery to the browser. Shows API gateway, streaming middleware, and frontend rendering — and flags any buffering points that add latency.
RAG pipeline architecture
Document ingestion (chunking, embedding, vector upsert), retrieval path (query embedding → similarity search → chunk selection), and context injection into the LLM prompt. Shows the vector database, embedding model, retrieval strategy, and re-ranking step if present.
Multi-model routing and fallback
Router service that selects models based on task type, tenant tier, or cost budget. Primary model with fallback chain. AI gateway layer (LiteLLM, PortKey, or custom) for unified logging and retry handling. Cache layer for repeated prompts.
Prompt management system
Prompt versioning workflow: draft → staging → production with rollback capability. Admin UI for authoring and testing. Deployment mechanism that controls which version is active. Logging that records which prompt version was used for each LLM call.
Per-tenant AI cost isolation
Cost tagging per LLM call (tenant, user, feature, model), budget enforcement before calling the API, and usage dashboards for customers. Shows the flow from request to cost logging to subscription enforcement to customer-facing reporting.

Perfect for

Engineering design reviews for new LLM features
Investor and board presentations explaining the AI stack
Onboarding engineers joining an AI product team
Security and compliance reviews of AI data flows
Technical due diligence documentation for fundraising
Cross-functional alignment between product, eng, and data teams

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