AI Architecture Patterns
Enterprise architecture patterns for building secure, observable, and production-ready AI systems.
What You'll Learn
This section covers the architecture patterns that separate proof-of-concept AI demos from production-grade AI infrastructure:
- Secure LLM Pipelines — Defense-in-depth for every stage of the LLM request lifecycle
- AI Observability Stack — Monitoring, tracing, and evaluation for LLM applications
- DevOps for AI Systems — CI/CD, testing, and deployment patterns for AI applications
- Enterprise AI Security — Governance, compliance, and risk management for AI
- Prompt Injection Defense — Multi-layer architecture for detecting and blocking injection attacks
- AI Infrastructure on Kubernetes — GPU scheduling, model serving, and inference autoscaling
- LLM Monitoring and Tracing — OpenTelemetry instrumentation, SLIs/SLOs, and alerting patterns
- AI Agent Infrastructure — Multi-agent orchestration, tool execution, memory systems, and guardrails
- Secure LLM API Gateway Deployment — Production gateway deployment with multi-tenant isolation and compliance
- Multi-Model LLM Routing — Cost-quality routing, failover strategies, and semantic caching
- AI Cost Optimization — Token budget management, model tiering, and cost governance
- LLM Evaluation & Testing — Automated quality benchmarks, regression testing, and CI/CD integration
- AI Data Pipeline Architecture — Document processing, embedding generation, and vector ingestion
Why Architecture Matters
Most LLM applications fail in production not because of the model, but because of the infrastructure:
| Failure Mode | Root Cause | Architecture Fix |
|---|---|---|
| Prompt injection attacks | No input validation layer | Security middleware (Lakera, Guardrails) |
| Silent quality degradation | No LLM observability | Trace-level monitoring (Langfuse, Phoenix) |
| Unpredictable costs | No token tracking | Cost analytics per feature/user |
| Slow RAG responses | Poor retrieval architecture | Hybrid search, re-ranking, caching |
| Agent failures | No state management | LangGraph, workflow orchestration |
| Compliance violations | No governance layer | Policy-as-code, audit logging |
Architecture Decision Framework
When designing AI infrastructure, evaluate every component against these criteria:
- Security — Is every input validated? Is every output scanned?
- Observability — Can you trace a single request through the entire pipeline?
- Cost control — Do you know the cost per user, per feature, per model?
- Reliability — What happens when the LLM provider is down or slow?
- Compliance — Does it meet your industry's regulatory requirements?
Guides in This Section
| Guide | Description |
|---|---|
| Secure LLM Pipelines | Defense-in-depth architecture for LLM applications |
| AI Observability Stack | Monitoring, tracing, and evaluation for production AI |
| DevOps for AI Systems | CI/CD, testing, and deployment for AI applications |
| Enterprise AI Security | Governance, compliance, and risk management |
| Production RAG Systems | Retrieval architecture, hybrid search, re-ranking, caching |
| AI Gateway Architecture | Centralized LLM routing, security, and cost management |
| Prompt Injection Defense | Multi-layer defense against prompt injection attacks |
| AI Infrastructure on Kubernetes | GPU scheduling, model serving, and autoscaling |
| LLM Monitoring and Tracing | OpenTelemetry instrumentation, SLIs/SLOs, alerting |
| AI Agent Infrastructure | Multi-agent orchestration, tool execution, guardrails |
| Secure LLM API Gateway | Production gateway deployment, multi-tenant isolation |
| Multi-Model LLM Routing | Cost-quality routing, failover, semantic caching |
| AI Cost Optimization | Token budgets, model tiering, cost governance |
| LLM Evaluation & Testing | Quality benchmarks, regression testing, CI/CD gates |
| AI Data Pipeline | Document processing, embeddings, vector ingestion |
| Architecture Playbooks Index | Central index of all architecture playbooks |