pgvector vs Milvus for fintech: Which Should You Use?

pgvector is a PostgreSQL extension for vector search. Milvus is a dedicated vector database built for high-scale ANN retrieval. For fintech, start with pgvector unless you already know you need multi-million-vector scale, high QPS similarity search, or a separate vector infrastructure team.

Quick Comparison

When pgvector Wins

• •

  You already store the source of truth in PostgreSQL

  If your fintech app keeps transactions, users, disputes, KYC docs, and audit trails in Postgres, pgvector is the obvious move. You can add a column like embedding vector(1536) and query nearest neighbors without shipping data into a second system.

• •

  You need hybrid relational + semantic queries

  Fintech workloads rarely ask for pure similarity only. They ask things like “find similar chargebacks for this merchant in the last 90 days” or “retrieve policy snippets for customers in this region.” With pgvector you can combine vector search with normal SQL filters:

  SELECT id, merchant_id
  FROM chargeback_cases
  WHERE status = 'open'
    AND created_at > now() - interval '90 days'
  ORDER BY embedding <-> $1
  LIMIT 10;
  

• •

  You care about operational simplicity

  Banks and insurers do not reward extra systems unless they earn their keep. pgvector keeps backups, HA, IAM controls, encryption-at-rest setups, replication monitoring, and audit logging inside the same Postgres operational model.

• •

  Your scale is sane

  If you are dealing with hundreds of thousands to a few million vectors per tenant or product line, pgvector is enough. Add an HNSW index when latency matters more than perfect recall:

  CREATE INDEX ON documents USING hnsw (embedding vector_cosine_ops);
  

  That gets you production-grade semantic retrieval without introducing a separate platform.

When Milvus Wins

• •

  You are at very large vector scale

  Once you are pushing into tens of millions of vectors with aggressive latency targets, Milvus starts making sense fast. It was built for this job; pgvector was not.

• •

  Your workload is retrieval-heavy and vector-native

  If your product is basically semantic search over claims notes, advisor conversations, fraud patterns, or underwriting documents at very high QPS, Milvus gives you more headroom. Its collection/index model is designed around ANN retrieval first.

• •

  You need more advanced indexing choices

  Milvus gives you explicit control over index types such as HNSW, IVF_FLAT, IVF_SQ8, and disk-oriented setups depending on deployment mode and versioning choices. That matters when tuning recall/latency/cost tradeoffs at scale.

• •

  You expect independent scaling from your transactional database

  Fintech systems often have spiky read patterns during fraud investigations or call-center surges. If your vector workload should scale separately from OLTP traffic on Postgres, Milvus gives you that isolation.

For fintech Specifically

Use pgvector unless your team can clearly justify a separate vector platform with real volume and SRE ownership behind it. Fintech systems live and die on auditability, joins against transactional data, access control consistency, and boring operations — pgvector fits that reality better than a standalone vector database.

Pick Milvus only when vector search becomes a first-class product capability with serious scale requirements: huge corpora, high QPS similarity search, or multiple teams depending on retrieval performance as an external service. For most banks and insurers building fraud assist tools, claims assistants, KYC document search, or advisor copilots: start with Postgres plus pgvector and move only when the numbers force you to.

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.