Interactive Architecture
How AI Systems Actually Work
Follow a single request through every layer of a production AI application — from the moment you press Enter to the streamed response.
Frontend Layer
💬
User Request
▌
🌐
CDN
Static Assets
Edge Cache
⚡
Streaming Connection
WebSocket / SSE
▌
🔑
Session Check
✓
User Auth
✓
Rate Limits
✓
Permissions
API Layer
🔀
API Gateway
POST /v1/chat/completions
→ route matched → middleware chain → handler
📊
Rate Limiting & Quotas
REQUESTS0 / 100
TOKENS0 / 100K
🛡️
Security & Moderation
Prompt Injection
Content Policy
PII Detection
RAG Pipeline
RAG PIPELINE
🧱
Context Building
System PromptUser MessageChat History
🔍
Retrieval
🔎▌
📄
Your Documents
📚
Knowledge Base
📋
Relevant Context
PROMPTChunk 1Chunk 2Chunk 3Chunk 4
🧠
Semantic Cache
NEW QUESTION
PREVIOUSLY ANSWERED
SIMILARITY
0.0
⊘Cache Miss
MODEL NEEDED →
⚡Cache Hit
⚡ CACHED RESPONSE
Model Selection
◇
LLM Gateway
Routing request to optimal model provider...
🔀
Model Router
Tool Execution
🔧
Tool Calling
📡
Call API
💻
Run Code
🔎
External Search
🔗
MCP
Model Context Protocol
FilesSearchDatabaseAPIsCode
Backend Infrastructure
🏗️Backend Infrastructure
🔮
Vector Database
🗃️
PostgreSQL
🗂️USERS
🗂️CONVERSATIONS
🗂️SETTINGS
Inference
🖥️
Inference Server
Distributing request to GPU cluster...
GPU NODE
⚙️
Inference Engine
vLLM
SGLang
TRT-LLM
📦
Request Batching
BATCH
REQ
REQ
REQ
REQ
REQ
REQ
🔲
Run on GPUs
GPU 0
GPU 1
GPU 2
GPU 3
💲
Infrastructure Cost
$$$$$$$$$$$$$$$$
Observability
📡
Monitor Every Step
📝Logs
📊Traces
📈Latency
📉Token Usage
Response
✨
Response
Streaming to client
▌End of request lifecycle