This webinar recording covers the practical AI architecture patterns our lead AI architect has developed across 20+ enterprise LLM deployments. The session focuses on what actually works in production — not research paper benchmarks, but real-world patterns for building cost-effective, reliable LLM systems.
The Core Pattern: Retrieval-Augmented Generation
RAG remains the dominant architecture for enterprise LLM applications in 2025. The core insight is simple: instead of trying to put all enterprise knowledge into a model's context window (expensive) or fine-tune a model on proprietary data (slow and brittle), you retrieve relevant documents at query time and include them in the prompt. The session walks through three RAG architecture variants — naive RAG, advanced RAG with reranking, and modular RAG with query routing.
Vector Database Selection
Choosing the right vector database is one of the most consequential early decisions. The session covers the trade-offs between managed solutions (Pinecone, Weaviate Cloud, Google Vertex Matching Engine) versus self-hosted options (Qdrant, pgvector in PostgreSQL). For most enterprise deployments under 10M documents, pgvector with proper indexing is operationally simpler and cost-effective. Purpose-built vector databases shine above that scale or when you need advanced filtering.
Cost Management in Production
LLM API costs surprise most teams. The session includes a cost modeling framework covering token budgeting, caching strategies (both semantic caching and exact-match caching), model routing (sending simple queries to smaller, cheaper models), and async batching for non-latency-sensitive workloads. One client reduced their monthly LLM API spend by 68% through semantic caching alone.
Access the Recording
The full 90-minute recording includes live Q&A, architecture diagrams for all patterns discussed, and a reference implementation in Python with LangChain and LlamaIndex examples. Register below to watch on demand.