Choosing the right index
HNSW or IVFFlat? Picking the index type and its parameters (m, ef_construction, lists, probes) wrong means slow builds, bloated memory or poor recall.
pgvector PostgreSQL HNSW Vector Search
pgvector development for vector search in your PostgreSQL
We help US and EU teams add production-grade vector similarity search to PostgreSQL with the pgvector extension — no separate vector database to provision, secure or pay for. Your embeddings live next to the relational rows they describe, so hybrid queries, joins and access control stay in one engine you already operate. From index tuning to full RAG backends, we make pgvector fast, accurate and easy to maintain.
We help US and EU teams add production-grade vector similarity search to PostgreSQL with the pgvector extension — no separate vector database to provision, secure or pay for. Your embeddings live next to the relational rows they describe, so hybrid queries, joins and access control stay in one engine you already operate. From index tuning to full RAG backends, we make pgvector fast, accurate and easy to maintain.
Challenges
Industry challenges we solve
Recall versus latency
Approximate search trades accuracy for speed. Tuning that trade-off for your data and query load is non-obvious and easy to get wrong.
Scaling vectors in Postgres
Millions of high-dimension vectors strain memory and index build times; without partitioning and sizing strategy, performance falls off a cliff.
Hybrid search
Real queries combine vector similarity with SQL filters, joins and full-text search. Naive plans scan everything and ignore your indexes.
Embedding drift
Changing the embedding model or dimension invalidates stored vectors, and silent model drift quietly degrades retrieval quality over time.
Keeping vectors in sync
Embeddings must update when source rows change. Without a reliable pipeline, your index goes stale and returns wrong results.
Solutions
Solutions we build
pgvector setup & index tuning
We install and configure pgvector, choose HNSW or IVFFlat per workload, and tune index parameters against your real data for the recall and speed you need.
Hybrid vector + relational queries
We write queries that blend similarity search with SQL filters, joins and full-text, with query plans that actually use your indexes.
RAG backend on Postgres
We build retrieval-augmented generation backends — chunking, embedding, retrieval and re-ranking — entirely on your PostgreSQL with LlamaIndex or LangChain.
Scaling & partitioning strategy
We size memory, partition large tables and benchmark index builds so vector search stays fast as your corpus grows.
Embedding pipeline & resync
We build pipelines that re-embed and re-index automatically when source rows change, keeping vectors consistent with your data.
Migration to pgvector
We migrate workloads off standalone vector databases like Pinecone or Qdrant into pgvector, collapsing two datastores into one.
Stack
Technology stack
pgvector, PostgreSQL, HNSW & IVFFlat indexes, embeddings, Supabase/Aurora/Cloud SQL, SQLAlchemy, LlamaIndex/LangChain, Docker.
Compliance
Compliance & regulations
GDPR · data residency · HIPAA-ready · SOC 2
EU
- GDPR — we treat embeddings of personal data as personal data, support right-to-erasure by deleting vectors with their source rows, and keep your PostgreSQL in an EU region.
- EU AI Act — RAG pipelines retrieve with traceable grounding and lineage, so generated answers can be tied back to the source documents they used.
- eIDAS & sector rules — vector search slots into systems with electronic-identity or sector-specific (finance, health) obligations without bypassing existing controls.
- NIS2 — database hardening, logging and access governance for the Postgres instance holding your vectors meet essential-entity security expectations.
US
- HIPAA — PHI embeddings stay inside your compliant PostgreSQL under a BAA, never shipped to a third-party vector service.
- NIST AI RMF — retrieval quality, recall and drift are measured and documented so RAG behaviour is governable, not a black box.
- SOC 2 — we build to your auditors' controls for access, change management and logging on the database and embedding pipeline.
- CCPA/CPRA — vectors derived from consumer data are catalogued and deletable, supporting access and opt-out requests.
Cases
Selected pgvector case studies
HealthTech · Nutrition
BasilDoc
Cross-platform diet and meal-planning app on Flutter — calorie engine, recipe library, weekly meal-plan, grocery ordering.
Sports Media · Mobile
Media Arena
Cross-platform sports news app and web portal — Telegram-bot CMS instead of a custom admin, Markdown publishing pipeline.
HealthTech · Diagnostics
Unilab
Patient app for a 40-city lab network — appointment booking, digital results, 2,500+ tests, scheduling and accounting integrations.
Why YuSMP
Why teams choose YuSMP for pgvector development
No extra datastore
Your vectors stay in the PostgreSQL you already run, back up and secure — no separate vector database to provision, monitor or pay for, and one engine to reason about.
Infra-accurate tuning
We tune indexes, memory and query plans against your real data and load, not defaults, so recall and latency meet your targets in production.
Compliance-first by default
For US and EU clients we keep embeddings of regulated data inside your compliant region, with the access controls, logging and erasure paths auditors expect.
FAQ
pgvector Development FAQ
Why pgvector instead of a dedicated vector database like Qdrant, Pinecone or Weaviate?
If your data already lives in PostgreSQL, pgvector lets you keep embeddings beside it — hybrid queries, joins, transactions, backups and access control all stay in one engine. That removes an entire datastore to operate, secure and pay for. Dedicated vector databases earn their keep at very large scale or with specialised features, but most teams reach those limits later than they expect.
HNSW or IVFFlat — which index should I use?
HNSW gives high recall and fast queries with slower, more memory-hungry builds, and is the default choice for most read-heavy workloads. IVFFlat builds faster and uses less memory but needs tuned lists and probes and is more sensitive to data distribution. We benchmark both on your data before committing.
How far does pgvector scale?
pgvector comfortably handles millions of vectors on a well-sized instance, and tens of millions with partitioning and careful memory tuning. Beyond that, index build time, RAM and query latency become the constraints — we benchmark your corpus to find the real ceiling before you hit it.
Can I combine vector search with normal SQL filters?
Yes — that is a core reason to use pgvector. We write hybrid queries that mix similarity search with WHERE filters, joins and full-text search, and tune the query plan so both the vector index and your relational indexes are used efficiently.
How do you balance recall and latency?
We tune index parameters (ef_search for HNSW, probes for IVFFlat) and measure recall against an exact-search baseline on your data. You set the target — for example 95% recall under a latency budget — and we tune to meet it, then document the settings.
How do you keep embeddings in sync with the data?
We build an embedding pipeline that detects changed source rows — via triggers, change-data-capture or a queue — re-embeds them and updates the index, so your vectors never drift out of sync with the rows they describe.
When should we outgrow pgvector?
When index builds, RAM or query latency stop meeting targets even after tuning and partitioning, or when you need features pgvector lacks. We will tell you honestly when that point arrives and plan a migration to a dedicated vector database, reusing the embedding pipeline we already built.
Ready to add vector search to your PostgreSQL?
Response within 1 business day. NDA on request.