Vector Databases (Pinecone/Weaviate/ChromaDB)
A two-day course on running Pinecone, Weaviate, and ChromaDB in production — choosing indexes (HNSW, IVF), scaling, cost, hybrid search, and benchmarking, for teams that already have a working AI application and need to scale it, not just build a first prototype.
From prototype to production — a different class of problems
Building a first vector database integration is relatively simple; keeping it running in production as data and query volume grows is an entirely different set of challenges. The course assumes participants already have a working prototype and focuses exclusively on the decisions that only show up at production scale — index selection, sharding, cost.
Indexing as a conscious trade-off, not a default setting
The choice between HNSW and IVF (and their variants) determines result relevance, latency, and system memory usage. The workshop shows how to measure that trade-off on your own data instead of relying on a provider’s default configuration, and how to reconfigure an index without downtime for a live application.
Cost and benchmarking as the basis for decisions, not preference
The final modules combine a practical cost calculation (Pinecone as a managed model vs. self-hosted Weaviate/ChromaDB) with building a repeatable benchmark comparing hybrid search relevance across solutions — so the vector database choice is based on data, not tool popularity.
Benefits
- Choose the right indexing algorithm (HNSW, IVF) for your scale and query-latency requirements
- Design a scaling strategy for a vector database as data volume grows
- Optimize the operating cost of vector databases in a production environment
- Build hybrid search and a benchmark comparing result relevance
Who is this training for?
Prerequisites
- Basic understanding of embedding and vector search concepts
- Experience with at least one vector database or RAG application in production or as a proof of concept
Training program
Indexing algorithms and their impact on production
- HNSW (Hierarchical Navigable Small World) — mechanism and tuning parameters
- IVF (Inverted File Index) and vector compression (product quantization)
- The recall vs. latency vs. memory-usage trade-off when choosing an index
- Reconfiguring an index without production downtime
Scaling and production architecture
- Sharding and replication in Pinecone, Weaviate, and ChromaDB at large volumes
- Real-time data update strategies without performance degradation
- Multi-tenancy — isolating customer data within a single vector database instance
- Disaster recovery and backup for vector data
Operating costs and comparing solutions
- Pinecone's cost model (managed, per-query) vs. Weaviate and ChromaDB (self-hosted, open source)
- Optimizing storage costs at a high volume of embeddings
- Selection criteria for a vector database given a specific budget and organizational scale
Hybrid search and search-quality benchmarking
- Hybrid search: combining semantic similarity with keyword matching (BM25)
- Reranking results and improving relevance in production scenarios
- Building a benchmark comparing Pinecone, Weaviate, and ChromaDB on your own data
- Monitoring search quality over time — detecting drift
Delivery Methods
Online
- Convenience of participating from anywhere
- Interactive live sessions with trainer
- Materials available for 30 days
- No travel costs
On-site
- Direct contact with trainer and group
- Intensive hands-on workshops
- Networking with other participants
- Full focus on learning
Frequently asked questions
How is this different from Vector Databases: Pinecone, Weaviate, and ChromaDB for AI Applications and Vector Databases: Pinecone, Weaviate, Qdrant, Chroma in our catalog?
Both existing courses introduce building AI applications with a vector database — from the fundamentals of vector representations to a first integration with a language model. This course assumes the vector database is already working in a prototype, and focuses solely on operating it in production: choosing indexes, scaling, cost, and systematic benchmarking — a workshop for teams past the proof-of-concept stage, not an introduction to the topic.
Do I need an already-working RAG application to attend this course?
Yes, the course assumes basic knowledge of how vector databases work and is best suited for teams that already have a prototype or early production version of a RAG or semantic search application and are hitting real problems with scale, cost, or result quality.
Which indexing algorithm should I choose — HNSW or IVF?
HNSW usually delivers higher recall at lower latency, at the cost of higher memory usage — a good choice for moderate scale and real-time requirements. IVF, especially with vector compression, scales better at very large volumes at the cost of somewhat lower recall. The course shows how to measure that trade-off on your own data instead of choosing based on default settings.
How do I compare Pinecone, Weaviate, and ChromaDB costs for my use case?
Pinecone, as a fully managed solution, is usually billed per query and storage, which simplifies operations at the cost of flexibility. Weaviate and ChromaDB, being open source, give you control over infrastructure and potentially lower cost at large scale, but require your own DevOps team. The course walks through calculating total cost of ownership (TCO) for each option.
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