pgvector for production RAG and recommendations
Trusted by 100+ innovative teams
What we build
We help teams add semantic search, RAG, and recommendation features without overcomplicating their infrastructure.
Built for teams like yours
How we deliver
Map your workflows, identify high-impact opportunities, and quantify ROI potential.
Build a focused MVP for your highest-impact use case in 4-6 weeks.
Harden, monitor, and expand — leveraging existing infrastructure for each new capability.
4-8 weeks
pilot to production
95%+
milestone adherence
99.3%
SLA stability
PostgreSQL Vector Search Implementation Implementation
Use the same rollout pattern we apply in production programs: architecture review, risk controls, and measurable milestones from pilot to scale.
4-8 weeks
pilot to production timeline
95%+
delivery milestone adherence
99.3%
observed SLA stability in ops programs
Deep dive
pgvector is the most operationally pragmatic choice for vector search when you're already running PostgreSQL. It's not the highest-performance vector database, and it's not the right answer at every scale — but for a large class of production RAG and recommendation workloads, it eliminates an entirely separate database from the stack.
We help engineering teams decide when pgvector is the right call, build production deployments on it, and recognize when to migrate to specialized vector infrastructure.
pgvector wins decisively when:
It's the wrong call when:
The honest framing: pgvector is "PostgreSQL with vector search," not "competitive vector database in PostgreSQL clothing."
pgvector supports two index types:
Practical guidance:
The biggest pgvector advantage over dedicated vector DBs: native hybrid queries.
A query like "select id, title, embedding distance from products where category='electronics' AND price<5000 AND in_stock=true order by embedding distance limit 20" is one query, one round trip, one transaction. The same logic in Pinecone or Qdrant requires either filtered ANN (which most vector DBs handle reasonably but not optimally) or external joining with PostgreSQL after the vector search.
For workloads where filter selectivity is high (most queries filter to a small subset of the catalog) pgvector's filtered query performance is competitive with or better than dedicated vector DBs.
The caveat: pre-filter selectivity matters. Filtering to 10% of rows then ANN-searching that subset is fast. Filtering to 0.01% then ANN-searching is slower than expected because pgvector's HNSW prunes through the graph regardless of the filter. Tune indexing strategy with this in mind.
A few things that compound across pgvector deployments:
These are PostgreSQL operations applied to pgvector, not pgvector-specific tricks. That's the point: existing PostgreSQL expertise transfers directly.
Signs to plan migration:
Migrating sooner than necessary is a common mistake. So is migrating later than necessary. We help teams identify the actual signal vs. noise.
When migration is warranted, the destination depends on the constraint:
Migration is a real engineering project: re-embedding (or copying vectors), rebuilding indexes, dual-writing during cutover, application changes for the new query API. Plan for 4–10 weeks depending on data volume and downtime tolerance.
pgvector inherits PostgreSQL operations without modification:
This is the core appeal: vector search inherits all the operational maturity of PostgreSQL. No new vendor relationship, no new disaster recovery story, no new training.
For most engagements, pgvector engagements typically run 4–8 weeks:
Many engagements also include a pre-engagement decision review: is pgvector actually the right call, or should the team go to a dedicated vector DB from the start? We answer that honestly.
pgvector earns its place in production architectures by being the simpler answer when the simpler answer is good enough. Most production RAG and recommendation systems below ~10M vectors are exactly that.
Yes. We install and configure pgvector on your existing PostgreSQL instance, design the embedding schema, create optimized indices, and integrate with your application layer. The process typically takes 2-3 weeks for an initial implementation.
We design every pgvector implementation with a migration-ready abstraction layer. When you hit the scale ceiling, we handle the migration to Pinecone, Weaviate, or Qdrant, including data export, re-indexing, sync pipeline setup, and zero-downtime cutover.
A basic implementation (embedding storage, similarity search, single index) takes 2-3 weeks. A full-featured implementation with hybrid search, metadata filtering, performance tuning, and monitoring takes 4-6 weeks. Our full three-phase engagement including discovery, benchmarking, implementation, and handoff runs 8 weeks.
Yes. We run benchmarks on your actual data, not synthetic datasets, before finalizing the architecture. Real-world filter patterns and embedding distributions routinely diverge from standard ANN benchmark results by 40% or more. The benchmark phase takes one week and prevents months of painful in-production discoveries.
Everything. Code, infrastructure-as-code modules, monitoring dashboards, observability configuration, and documentation all live in your repository and your cloud account. We do not retain any deliverables. You also get a 30-day post-launch support window at no additional cost.
Explore related services, insights, case studies, and planning tools for your next implementation step.
Delivery available from Bengaluru and Coimbatore teams, with remote implementation across India.
Case Studies
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Boolean and Beyond
825/90, 13th Cross, 3rd Main
Mahalaxmi Layout, Bengaluru - 560086
590, Diwan Bahadur Rd
Near Savitha Hall, R.S. Puram
Coimbatore, Tamil Nadu 641002