pgvector vs Milvus for insurance: Which Should You Use?

pgvector is a PostgreSQL extension for vector search, which means you get embeddings, SQL, transactions, and joins in one database. Milvus is a dedicated vector database built for high-scale ANN retrieval, distributed workloads, and heavy similarity search.

For insurance, start with pgvector unless you already know you need multi-million-to-billion scale retrieval or a separate vector serving layer.

Quick Comparison

When pgvector Wins

• •

  Your source of truth is already PostgreSQL

  Insurance systems are full of relational data: policies, endorsements, claims, FNOL records, adjuster notes. With pgvector, you can store embeddings next to that data and query it in one shot:

  SELECT claim_id
  FROM claim_embeddings
  WHERE tenant_id = 'acme'
    AND status = 'open'
  ORDER BY embedding <=> $1
  LIMIT 10;
  

  That WHERE clause matters. In insurance, filtering by tenant, product line, jurisdiction, or claim status is not optional.

• •

  You need hybrid queries with real business constraints

  The value is not just “find similar text.” It is “find similar prior claims for this line of business in this state that are still open and below reserve threshold.” Postgres handles those constraints naturally. Milvus can filter too, but pgvector keeps the whole query in one place with normal SQL semantics.

• •

  You want the simplest production path

  If your team already runs Postgres well, pgvector adds a feature instead of a platform. No new operational domain. No separate cluster to monitor. For many insurance teams, that difference decides whether the project ships this quarter or sits in backlog.

• •

  Your workload is moderate scale

  If you are indexing thousands to low millions of chunks from policy documents, underwriting guidelines, claims history, and call transcripts, pgvector is enough. Use hnsw when latency matters and ivfflat when you want a simpler approximate index strategy.

When Milvus Wins

• •

  You have very large embedding volumes

  Once you get into tens or hundreds of millions of vectors across multiple lines of business or regions, Milvus starts making sense. It is designed for this problem space first. pgvector can work far beyond toy scale, but it is still living inside Postgres.

• •

  You need high QPS retrieval as a core service

  If vector search becomes a hot path for multiple applications — agent assist, fraud similarity lookup, claims routing, document recommendation — Milvus gives you a purpose-built retrieval engine. That matters when latency and throughput are both non-negotiable.

• •

  You expect frequent reindexing and partitioned workloads

  Insurance data changes by product line, geography, customer segment, and retention window. Milvus handles collection/partition-oriented designs better when your embedding corpus grows into separate operational domains.

• •

  Your team is already committed to a vector-first architecture

  If the application is basically “search over embeddings” with minimal relational dependency, don’t force Postgres into being something it is not. Use Milvus and keep your transactional system separate.

For insurance Specifically

Use pgvector for most insurance products: claims triage assistants, underwriting copilot search over guidelines, policy Q&A over document stores with metadata filters. Insurance workflows depend on relational context — policy number, loss date, state code, line of business — and pgvector lets you combine semantic retrieval with exact SQL filtering without building extra plumbing.

Choose Milvus only when vector search becomes a standalone platform problem: huge corpora, strict latency targets under load, or multiple teams sharing the same retrieval layer at serious scale. For most carriers and insurtechs building their first production RAG system on insurance data, pgvector is the right default.

Keep learning

• •The complete AI Agents Roadmap — my full 8-step breakdown
• •Free: The AI Agent Starter Kit — PDF checklist + starter code
• •Work with me — I build AI for banks and insurance companies

By Cyprian Aarons, AI Consultant at Topiax.