Vector Databases

Storage for embeddings and semantic search

Vector databases store and query high-dimensional embeddings. I evaluate and deploy options like pgvector, Pinecone, Qdrant, and Weaviate based on scale, latency, and operational fit.

3+years in production

30+projects shipped

expertproficiency

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My take

Why I use Vector Databases

Embedding-based retrieval is now table stakes for AI products. The right vector store depends on scale, latency budget, and how much infra you want to own. I default to pgvector for small-to-medium and reach for Pinecone or Qdrant when scale demands it.

Want the broader stack philosophy? Read about how Sri picks tools or browse engineering insights.

Honest assessment

Strengths & tradeoffs

No tool is perfect. Here's what shines and what to watch for.

Strengths

Sub-100ms similarity search at scale
Hybrid search combines vectors with keyword filters
Metadata filtering for tenant isolation
Most options offer managed services

Tradeoffs (honestly)

Index tuning (HNSW parameters) affects recall vs. speed
Embedding model choice locks the schema
Cost grows with vector dimensionality and count
Reindexing on model upgrade is non-trivial

Fit assessment

When to reach for Vector Databases

Pick the right tool for the job.

Best fits

RAG systems for chat over documents

Semantic search over product catalogs

Duplicate detection and clustering

Recommendation systems

Memory layers for AI agents

Not ideal for

Exact-match keyword retrieval (use BM25 or Elastic)

Tiny corpora where in-memory cosine sim suffices

Workloads with no embedding model strategy

Common use cases

RAG retrievalSemantic searchRecommendationDeduplication

Resources

Learn more

Curated official docs, tutorials, and writing on Vector Databases.

OpenAI Embeddings Guide

Services

Where I apply Vector Databases

Engagements where this technology shows up regularly.

AI Engineering

End-to-end AI integration from prototyping to production. I build custom LLM pipelines, RAG systems, and intelligent agents that solve real business problems-not just demos.

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Case Studies