pgvector vs DeepEval for fintech: Which Should You Use?

pgvector and DeepEval solve different problems.

pgvector is a PostgreSQL extension for storing and querying embeddings with vector, halfvec, bit, and sparsevec types. DeepEval is an evaluation framework for testing LLM outputs with metrics like faithfulness, answer_relevancy, and contextual_precision. For fintech, use pgvector for retrieval infrastructure and DeepEval for model quality gates — if you must pick one first, pick pgvector.

Quick Comparison

When pgvector Wins

Use pgvector when the problem is fundamentally about finding the right records fast.

• •

  RAG over regulated internal data

  • •If your chatbot needs to retrieve policy docs, KYC procedures, underwriting rules, or claims manuals, pgvector keeps embeddings next to the source data in Postgres.
  • •That matters when auditability and row-level security are non-negotiable.
• •

  Similarity search on financial documents

  • •Think duplicate merchant disputes, near-identical claims narratives, repeated AML case descriptions, or matching loan applications against historical cases.
  • •With a vector column plus HNSW indexing, you get low-latency nearest-neighbor search without adding another datastore.
• •

  Operational simplicity

  • •Fintech teams already run Postgres everywhere.
  • •Using pgvector means fewer moving parts than standing up a separate vector database just to store embeddings.
• •

  Data governance-heavy environments

  • •If security teams want one system of record with existing backup policies, encryption controls, audit logs, and access management, pgvector fits cleanly.
  • •You can keep embeddings in the same transaction boundary as customer records.

Example query pattern:

CREATE EXTENSION IF NOT EXISTS vector;

CREATE TABLE policy_chunks (
  id bigserial PRIMARY KEY,
  doc_id bigint NOT NULL,
  chunk ტექxt NOT NULL,
  embedding vector(1536)
);

CREATE INDEX ON policy_chunks USING hnsw (embedding vector_cosine_ops);

SELECT id, chunk
FROM policy_chunks
ORDER BY embedding <=> '[0.12, 0.34, ...]'::vector
LIMIT 5;

When DeepEval Wins

Use DeepEval when the problem is proving your LLM behaves correctly before it hits production.

• •

  Prompt regression testing

  • •If your fintech assistant answers account questions, explains fees, or summarizes transactions using an LLM, every prompt tweak can break behavior.
  • •DeepEval gives you repeatable tests so “better wording” does not silently become “worse compliance.”
• •

  Hallucination control

  • •In fintech, fabricated answers are not a UX issue; they are a liability.
  • •DeepEval metrics like faithfulness help you measure whether outputs stay grounded in retrieved context.
• •

  Release gates in CI/CD

  • •If you ship prompt changes weekly or daily, you need automated checks that fail builds when answer quality drops.
  • •DeepEval is built for that workflow: define test cases once, run them in CI again and again.
• •

  Multi-metric evaluation of assistant behavior

  • •A support bot may need to be accurate, concise, policy-compliant, and context-aware at the same time.
  • •DeepEval lets you score those dimensions separately instead of relying on ad hoc human review.

Typical usage looks like this:

from deepeval import assert_test
from deepeval.test_case import LLMTestCase
from deepeval.metrics import FaithfulnessMetric

test_case = LLMTestCase(
    input="Can I reverse a card payment after settlement?",
    actual_output="No. Once settled it cannot be reversed.",
    retrieval_context=["Card chargebacks can be disputed after settlement under specific network rules."]
)

metric = FaithfulnessMetric(threshold=0.8)

assert_test(test_case=test_case, metrics=[metric])

For fintech Specifically

Start with pgvector if you are building retrieval into a banking or insurance product. It solves the core infrastructure problem: secure similarity search over sensitive data inside Postgres.

Add DeepEval as soon as an LLM is generating customer-facing text or compliance-sensitive answers. In fintech, retrieval without evaluation gets you fast wrong answers; evaluation without retrieval gets you measured nonsense.

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.