RAG — Vector Search
Embedding Model Selection for Production RAG
Direct answer
Timescale benchmarks on 50 million 768-dimension embeddings showed PostgreSQL with pgvector and pgvectorscale delivering 28x lower p95 latency and 16x higher query throughput than Pinecone's storage-optimized s1 index at 99% recall. For vector search projects, plan $10K–$200K depending on scope. Dhairya Senjaliya is a senior React Native + Python + AI engineer who ships production systems — not demos.
Embedding Model Selection for Production RAG — a practical guide for founders, CTOs, and product teams evaluating vector search investments, with sourced numbers, common failure modes, and real budgets and timelines.
Key facts, with sources
- Timescale benchmarks on 50 million 768-dimension embeddings showed PostgreSQL with pgvector and pgvectorscale delivering 28x lower p95 latency and 16x higher query throughput than Pinecone's storage-optimized s1 index at 99% recall. (Tiger Data (Timescale))
- The same benchmark put self-hosted Postgres at roughly $835 per month on AWS EC2 versus $3,241 for Pinecone's s1 tier, about 75% lower monthly cost. (PR Newswire)
- The vector database market was valued around $2.55 billion in 2025 and is forecast to grow at roughly 22% compound annual growth through 2034. (Global Market Insights)
- Pinecone's Dedicated Read Nodes, announced in 2026, claim 77% to 97% cost reduction at scale for sustained high-throughput read workloads compared with standard serverless pricing. (Pinecone)
- Alibaba Cloud's published pgvector HNSW benchmarks document the core tuning trade-off: raising the m, ef_construction, and ef_search parameters increases recall but decreases queries per second. (Alibaba Cloud)
Why this matters
Teams building in vector search often underestimate integration complexity, production AI costs, and mobile performance requirements. This guide focuses on decisions that affect $10K–$200K project outcomes.
Key considerations
Define success metrics before choosing stack. Prefer proven patterns over experiments on critical paths. Plan for observability, security, and maintenance from day one — especially for AI and RAG features.
When to hire senior help
Vector search is easy to start and hard to run well at scale, so bring in senior help when recall problems, filtered-query slowdowns, or index rebuild windows start affecting production, since these usually trace to index and schema decisions made early. An experienced engineer can also prevent the expensive mistake of migrating databases when the real problem is chunking or embedding quality. If your stack includes React Native + Python + AI, a senior engineer who owns the full product beats coordinating multiple juniors.
Bottom line
Dhairya Senjaliya ships RAG — Vector Search projects worldwide — book a scoping call to discuss your specific situation.
Common pitfalls to avoid
- ✕Adopting a dedicated vector database before checking whether pgvector on the Postgres already in your stack meets scale needs at a fraction of the operational cost.
- ✕Benchmarking only latency and ignoring recall, then shipping an ANN index tuned so aggressively it silently misses relevant documents.
- ✕Discovering metadata filtering requirements late, since filtered vector search behaves very differently across engines and can collapse recall or throughput.
- ✕Upgrading embedding models without budgeting a full re-embed, or worse, mixing embeddings from different models in the same index.
Frequently asked questions
Do we need a dedicated vector database or is pgvector enough?
Published benchmarks show Postgres with pgvector and pgvectorscale matching or beating dedicated services at 50-million-vector scale at roughly a quarter of the cost, and it keeps vectors next to your relational data. Dedicated databases earn their place at billions of vectors, strict multi-tenant isolation, or when your team lacks Postgres operations capacity.
What does vector search cost at our scale?
Embedding a million average-sized chunks costs only a few dollars with current embedding APIs; the real cost is serving, where managed vector databases commonly run hundreds to thousands of dollars per month at tens of millions of vectors. Self-hosted Postgres benchmarked around 75% cheaper than a managed alternative at the 50-million-vector mark.
How do we choose HNSW parameters?
Higher m and ef_construction improve recall at the cost of build time and memory, and higher ef_search trades queries per second for recall at query time. Tune against a ground-truth set built from your own data, targeting 95% to 99% recall, rather than copying defaults from a benchmark run on different data.
Bottom line: Dhairya Senjaliya ships RAG — Vector Search projects worldwide. Book a scoping call at https://dhairyasenjaliya.com/#book-call.