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RAG in Production 2026 — Real Benchmarks and Cost Optimization
An advanced technical training built around real production numbers. You will benchmark Qdrant self-hosted vs Pinecone Serverless on a 1.4M-document legal corpus, model per-employee costs for an HR policy assistant on ChromaDB, and implement hybrid sparse + dense retrieval for ecommerce product search. The course includes a hands-on cost simulator sandbox and a structured ROI calculator to decide when RAG beats fine-tuning — and when it doesn't. All examples use open-source tooling (Qdrant, ChromaDB, Ollama) with proprietary alternatives clearly priced.
Duration
2 days
Level
Advanced
Price
9.99 EUR/month (all courses included)
Max group
10 participants
What you will learn
+Break down a production RAG bill into its four cost drivers and benchmark each one
+Choose the right vector database (Qdrant, Pinecone, ChromaDB) for a given query volume and budget
+Implement hybrid BM25 + dense retrieval with Reciprocal Rank Fusion for ecommerce or multi-intent corpora
+Build a semantic query cache that reduces LLM inference cost by 25–45%
+Calculate RAG vs fine-tuning 36-month TCO and make a data-driven architecture decision
+Harden a production RAG pipeline with batching, cold-start handling, and HNSW tuning
Course program
Module 1: RAG Cost Anatomy: Where the Money Actually Goes
2h30- Four cost drivers: embedding, vector DB reads, LLM inference, infrastructure
- 2026 pricing reference: Voyage AI, Pinecone, Qdrant, Claude, GPT-4o-mini
- Hands-on cost calculator: model your pipeline before optimizing
- Sandbox: configure your own numbers and identify your highest-cost lever
Module 2: Case Study 1: Legal Document Q&A at 1M+ Documents
2h00- Qdrant self-hosted vs Pinecone Serverless: latency P50/P95/P99 benchmark
- Cost crossover: when Qdrant's fixed cost beats Pinecone's per-query pricing
- HNSW parameter tuning for legal recall (m, ef_construct, ef grid search)
- Practical ingestion pipeline with Voyage AI voyage-3-lite embeddings
Module 3: Case Study 2: HR Policy Assistant — Per-Employee Cost Modeling
1h45- ChromaDB self-hosted: when to use it and when to avoid it
- Per-employee monthly cost model (target: < EUR 1.00/employee/month)
- Semantic caching layer: 40% hit rate cuts LLM spend by 38%
- Cache invalidation keyed to document version hash
Module 4: Case Study 3: Ecommerce Hybrid Sparse + Dense Search
2h00- Why pure dense embeddings fail on SKU codes and exact product names
- BM25 + dense with Reciprocal Rank Fusion: 18% nDCG@10 improvement
- Qdrant native hybrid query (v1.9+ fusion: rrf)
- Cost per search transaction at 5k / 50k / 500k searches/day
Module 5: RAG vs Fine-Tuning: ROI Decision Framework
2h00- 6-factor decision matrix: freshness, corpus size, latency SLA, accuracy, citation, TCO
- 36-month TCO calculator with breakeven analysis
- Sandbox: enter your numbers, get an architecture recommendation
- Case studies: when fine-tuning won and when it failed
Module 6: Production Hardening: Batching, Caching, Cold-Start
2h15- Semantic query cache with Qdrant: cosine threshold 0.97, version-keyed TTL
- Embedding batch ingestion: 128-text batches reduce API calls by 99%
- Lambda cold-start handler: global client init + EventBridge warm-up ping
- Open-source toolkit summary: Qdrant / ChromaDB / Pinecone decision guide
Ready to get started?
9.99 EUR/month — All courses included, cancel anytime