Architecture Pipeline Metrics Topology
// Modern Cloud / AI Infrastructure

MODERN CLOUD / AI INFRASTRUCTURE

AI CORE 0 TFLOPS

70BParams
512GPU Nodes
4msP99
99.99%SLA
Explore

// 01 — Architecture

Global Infrastructure Stack

Four-layer cloud-native topology — edge, compute, data, and AI — wired for planet-scale resilience.

Edge Layer
CDN Edge
300+ PoPs worldwide
WAF / DDoS
Layer 7 protection
Load Balancer
L4 / L7 routing
API Gateway
Rate limit + auth
TLS / IAM
mTLS · OIDC · RBAC
Observability
Prometheus · Grafana
Compute Layer
Kubernetes
HPA auto-scale
Serverless
λ Functions · ~80ms
GPU Cluster
A100 · H100 · TPU v5p
Service Mesh
Istio · Envoy · mTLS
CI / CD
ArgoCD · GitOps
Data Layer
Object Store
S3 · GCS · 99.999%
Vector DB
Pinecone · HNSW ~1ms
PostgreSQL HA
Multi-AZ failover
Redis Cache
Cluster · p99 <0.1ms
Kafka Streams
10M+ events / sec
Warehouse
Snowflake · Petabyte
AI / ML Layer
Model Registry
MLflow · W&B
Inference
vLLM · KV-cache
RAG Pipeline
LangChain · rerank
Fine-Tuning
LoRA · RLHF · DPO
ML Ops
Drift · shadow deploy
AI Agents
ReAct · Memory
EDGE LAYER COMPUTE LAYER DATA LAYER AI / ML LAYER CDN Edge 300+ PoPs WAF / DDoS L7 protect Load Balancer L4 / L7 API Gateway Rate limit TLS Termination mTLS IAM / RBAC OIDC Observability Prom · Graf Global DNS KUBERNETES Pod:API Pod:Work HPA Scale auto-scale ↔ auto-heal SERVERLESS λ Fn ×6 λ Workers cold start ~80ms GPU CLUSTER A10080GB×64 H100SXM5×32 TPU v5pMatrix×16 SVC MESH Istio + Envoy mTLS + Tracing CI/CD GitHub Actions ArgoCD GitOps OBJECT STORE S3 / GCS / ABS 99.999% durable VECTOR DB Pinecone · Weaviate HNSW · ~1ms ANN RELATIONAL PostgreSQL HA Multi-AZ failover CACHE Redis Cluster p99 <0.1ms STREAMING Kafka / Kinesis 10M+ events/s WAREHOUSE Snowflake / BQ Petabyte scale MODEL REGISTRY MLflow · W&B Versioned weights INFERENCE TensorRT · vLLM KV-cache · batch RAG PIPELINE LangChain · Llama Retrieve + rerank FINE-TUNING LoRA · QLoRA RLHF · DPO ML OPS Drift detection Shadow deploy AI AGENTS Tool use · ReAct Memory graphs

// 02 — AI Pipeline

Intelligent Data Pipeline

End-to-end ML workflow from raw ingestion to continuous model improvement.

01 //
⬡
Ingest

Multi-source ingestion via Kafka, REST webhooks, and batch S3 connectors.

Real-time
02 //
◈
Process

Spark distributed processing with schema evolution and data quality gates.

Distributed
03 //
◎
Embed

Dense vector embeddings via fine-tuned encoders indexed in HNSW vector stores.

768-dim
04 //
◆
Infer

Multi-model serving with dynamic batching, KV-cache, and speculative decoding.

GPU
05 //
◉
Evaluate

Automated evals, RLHF preference signals, drift monitoring, and auto-rollback.

Continuous

// 03 — System Metrics

Infrastructure at Scale

Live performance indicators across the full distributed stack.

0
Req / second
0
GPU Nodes
—
P99 Latency
—
Uptime SLA
—
Params served

// 04 — Global Topology

Multi-Cloud Data Flow

Real-time replication across four regions with zero-RPO architecture and 400 Gbps private backbone.

AI CORE US-EAST-1 AWS Virginia PRIMARY EU-WEST-1 GCP Ireland REPLICA AP-SOUTH-1 Azure Singapore REPLICA AP-NORTH-1 OCI Tokyo REPLICA PRIVATE BACKBONE — 400 Gbps · 10ms RTT CDN EDGE 300 PoPs · <5ms Primary Replica Data flow