pgvector vs Cassandra for insurance: Which Should You Use?

pgvector and Cassandra solve different problems. pgvector is a PostgreSQL extension for vector similarity search, while Cassandra is a distributed wide-column database built for massive write throughput and always-on availability.

For insurance, use pgvector first unless you already have a real Cassandra platform team and a workload that is clearly multi-region, write-heavy, and operationally distributed.

Quick Comparison

When pgvector Wins

• •

  Semantic search over insurance documents

  If you need to search claims notes, underwriting memos, policy wording, or adjuster comments by meaning rather than exact keywords, pgvector is the cleanest path. Store embeddings in a vector column and query with operators like <-> for distance search.

• •

  Agent workflows that need SQL joins

  Insurance data is rarely isolated. You often need vector search plus joins to policies, customers, claims, coverage limits, and fraud flags. With pgvector inside PostgreSQL, you can run one query that combines similarity search with business rules.

• •

  RAG over regulated content

  For retrieval-augmented generation over policy documents or claims manuals, pgvector fits well because you can keep metadata filters in SQL. That matters when you need to restrict retrieval by product line, jurisdiction, effective date, or customer segment.

• •

  Teams already standardized on PostgreSQL

  If your platform already uses Postgres for core insurance systems, adding pgvector avoids introducing a second database just for embeddings. You get backup strategy, monitoring, authentication, and governance from the same stack.

Example pattern

CREATE EXTENSION IF NOT EXISTS vector;

CREATE TABLE claim_chunks (
  id bigserial PRIMARY KEY,
  claim_id uuid NOT NULL,
  section ტექxt NOT NULL,
  embedding vector(1536)
);

CREATE INDEX ON claim_chunks USING hnsw (embedding vector_cosine_ops);

SELECT id, claim_id
FROM claim_chunks
WHERE claim_id = '7d2b4f1e-8f4a-4c2f-bd9e-2f0f5c7c1a11'
ORDER BY embedding <=> '[0.12,-0.08,...]'::vector
LIMIT 5;

That pattern is enough for most insurance AI retrieval workloads.

When Cassandra Wins

• •

  High-volume event ingestion

  If you are storing millions of policy events, quote updates, device signals, or claim lifecycle events per day across regions, Cassandra is built for that. Its write path is what it does best.

• •

  Always-on distributed reads and writes

  Insurance platforms often need to survive node failures without drama. Cassandra’s replication model and tunable consistency levels make it a better fit when uptime matters more than relational convenience.

• •

  Time-series-like operational data

  If your workload looks like “fetch all events for claim X ordered by time” or “append status changes forever,” Cassandra handles that pattern well with the right partitioning strategy. Use partition keys carefully so reads stay narrow and predictable.

• •

  Large-scale operational stores with simple access patterns

  Cassandra shines when your queries are known in advance:

  • •Get claim history by claim ID
  • •Get policy activity by policy number
  • •Write telemetry by device ID
  • •Read recent events by tenant and time bucket

  If your access pattern is stable and simple, Cassandra gives you serious scale.

Example pattern

CREATE TABLE claim_events (
  tenant_id text,
  claim_id text,
  event_ts timestamp,
  event_type text,
  payload text,
  PRIMARY KEY ((tenant_id, claim_id), event_ts)
) WITH CLUSTERING ORDER BY (event_ts DESC);

That model is ideal for append-heavy insurance event streams where reads are scoped by entity.

For insurance Specifically

Pick pgvector unless your problem is clearly an operational event store at massive scale. Most insurance AI use cases are document-heavy: claims triage, policy Q&A, underwriting assistant workflows, fraud analyst copilots. Those benefit from vector search plus SQL filters more than they benefit from Cassandra’s distributed write engine.

Use Cassandra only when the system is fundamentally about ingesting and serving huge volumes of predictable records across multiple nodes or regions. In insurance terms: telemetry pipelines yes; semantic retrieval no.

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.