AI Infrastructure Architecture Playbooks
A comprehensive collection of production-tested architecture patterns for building, securing, and operating AI infrastructure at scale.
Each playbook includes an architecture overview, infrastructure component breakdown, recommended tool stack, phased deployment workflow, and security considerations.
Core Architecture Patterns
Foundational architecture guides covering the essential components of production AI infrastructure.
| Playbook | Focus Area | Key Tools |
|---|---|---|
| Secure LLM Pipelines | Defense-in-depth for LLM request lifecycle — input validation, output filtering, compliance | SlashLLM, Lakera |
| AI Observability Stack | LLM tracing, cost tracking, quality metrics, evaluation dashboards | Langfuse, LangSmith |
| Production RAG Systems | Retrieval architecture, hybrid search, re-ranking, caching, evaluation | Pinecone, Weaviate |
| AI Gateway Architecture | Centralized LLM routing, rate limiting, security, cost governance | LiteLLM, SlashLLM |
| AI Infrastructure on Kubernetes | GPU scheduling, model serving (vLLM/Triton), autoscaling, storage | Kubernetes, KEDA, Prometheus |
Security Architecture
Guides focused on protecting AI systems from adversarial inputs, data leakage, and compliance violations.
| Playbook | Focus Area | Key Tools |
|---|---|---|
| Prompt Injection Defense | Multi-layer defense against prompt injection attacks — detection, blocking, monitoring | SlashLLM, Lakera |
| Enterprise AI Security & Governance | Governance boards, risk management, compliance frameworks, audit trails | OPA, Vault |
| Secure LLM API Gateway Deployment | Production gateway deployment — auth, multi-tenant isolation, PII redaction, compliance logging | SlashLLM, Envoy |
Operational Architecture
Guides for running AI systems reliably in production — DevOps, monitoring, cost management, and testing.
| Playbook | Focus Area | Key Tools |
|---|---|---|
| DevOps for AI Systems | CI/CD for prompts and models, shadow deployment, quality gates, rollback | GitHub Actions, LangSmith |
| LLM Monitoring and Tracing | OpenTelemetry instrumentation, SLIs/SLOs, chain debugging, alerting | OpenTelemetry, Prometheus |
| AI Cost Optimization | Token budget management, semantic caching, model tiering, GPU right-sizing | Langfuse, LiteLLM |
| LLM Evaluation & Testing | Automated quality benchmarks, LLM-as-Judge, regression testing, CI/CD gates | LangSmith, Langfuse |
Advanced Architecture
Patterns for complex, multi-component AI systems — agent infrastructure, multi-model routing, and data pipelines.
| Playbook | Focus Area | Key Tools |
|---|---|---|
| AI Agent Infrastructure | Multi-agent orchestration, tool execution, memory systems, guardrails | CrewAI, LangGraph, SlashLLM |
| Multi-Model LLM Routing | Cost-quality routing, failover, A/B testing, semantic caching across providers | LiteLLM, Portkey |
| AI Data Pipeline Architecture | Document processing, embedding generation, vector ingestion, data quality | Pinecone, Weaviate, Airflow |
How to Use These Playbooks
Starting a new AI project? Begin with Secure LLM Pipelines and AI Observability Stack to establish security and visibility from day one.
Building a RAG system? Follow Production RAG Systems for retrieval architecture, then AI Data Pipeline for the ingestion pipeline, then LLM Evaluation & Testing for quality measurement.
Deploying agents? Start with AI Agent Infrastructure for the orchestration layer, add Prompt Injection Defense for security, and AI Cost Optimization to prevent runaway agent costs.
Optimizing an existing deployment? Use AI Cost Optimization for immediate savings, Multi-Model LLM Routing for provider optimization, and LLM Monitoring and Tracing for operational visibility.
Tool Intelligence
These architecture playbooks reference tools from our AI Infrastructure Tool Directory. For detailed tool evaluations:
- AI Tool Directory → — Interactive tool directory with category filters
- Tool Reviews → — In-depth technical reviews with architecture analysis
- Head-to-Head Comparisons → — Side-by-side tool comparisons