What is the best vector database for financial RAG in 2026?

For most solo finance stacks, pgvector, because it is free, open-source, and keeps vectors next to your relational metadata (tickers, dates, form types) for clean filtering. Move to Qdrant Cloud (free-forever managed tier) or Pinecone (free Starter, $50/mo Standard) when you outgrow Postgres. The chunking strategy and embedding cost matter more than the database brand.

Is there a free vector database for RAG?

Yes. pgvector is fully open-source and free to self-host. Qdrant Cloud has a free-forever tier (0.5 vCPU, 1GB RAM, 4GB disk). Pinecone's Starter tier is free up to 2GB storage, 2M write units, and 1M read units per month (verified 2026-05-25).

How much does Pinecone cost for production RAG?

The Standard plan has a $50/mo minimum spend, then pay-as-you-go: read units $16-18 per million, write units $4-4.50 per million, and storage $0.33/GB/month, varying by cloud and region (verified 2026-05-25). A single filtered query can consume several read units, so high query volume drives the cost.

Does the vector DB or the LLM cost dominate in finance RAG?

The LLM and embedding cost dominate. Embedding the corpus and running the model over retrieved chunks on every query usually outweighs the vector-store fee, so favor good filtering (fewer, more relevant chunks) and stable chunking (less re-embedding). Price the loop in the Token-Cost Optimizer.

Best Vector DBs for Financial RAG 2026

The short answer

For financial RAG in 2026, pgvector is the right default for most solo stacks (free, no new infrastructure if you run Postgres), Qdrant Cloud has a free-forever managed tier, Pinecone is serverless with a 2GB free Starter and a $50/mo Standard minimum, and Weaviate rounds out the managed set. Embedding and re-query token cost dominates, not the index.

For financial RAG in 2026, the right vector database depends on scale and whether you can self-host. pgvector is the correct default for most solo finance stacks: free, open-source, and zero new infrastructure if you already run Postgres. Qdrant Cloud has a free-forever managed tier and usage-based scaling when you outgrow Postgres. Pinecone is the managed serverless option with a 2GB free Starter tier and a $50/mo Standard minimum once you go to production. Weaviate Cloud rounds out the managed set. For finance specifically, the index cost is rarely the real spend; the embedding and re-query token cost dominates, which the Token-Cost Optimizer prices and which usually argues for fewer, larger chunks over many small ones.

What "for financial RAG" actually changes

A finance RAG corpus (10-Ks, 10-Qs, earnings transcripts, filings) is not a generic document store, and three properties change the database choice:

Numeric and tabular content. Filings are full of tables and figures where a chunk boundary in the wrong place separates a number from its header. Chunking strategy matters more than vector-DB brand; see Structural vs Fixed Chunking.
Auditability. A finance answer must trace back to the source passage. The database needs solid metadata filtering (by ticker, filing date, form type) so retrieval is explainable, not just "nearest neighbor."
Cost asymmetry. The vector store is cheap relative to the embedding pass and the LLM read on retrieved chunks. The token cost dominates, so the database that minimizes re-embedding and supports good filtering beats the one with the lowest per-query index fee.

This roundup ranks the options on published pricing and documented capability only. No retrieval-quality benchmark is asserted; the chunking and filtering points are structural facts about finance corpora, not a study we ran.

The headline table

All figures are vendor list prices and documented capabilities, verified 2026-05-25 on the pages in Sources.

Option	Free tier	Hosting	Paid entry	Notes
pgvector	fully free (open source)	self-host / managed Postgres	$0 on existing Postgres	Simplest if you already run Postgres
Qdrant Cloud	free forever (0.5 vCPU, 1GB RAM, 4GB disk)	managed or self-host	usage-based (not per-unit on page)	Strong filtering; OSS core
Pinecone	Starter: 2GB, 2M writes/mo, 1M reads/mo	managed serverless	$50/mo Standard minimum	Read units $16-18/M, write $4-4.50/M, storage $0.33/GB/mo
Weaviate Cloud	sandbox	managed or self-host	from $45/mo (Flex)	Usage-based on dimensions + storage

Verify on the vendor page before committing; managed vector-DB pricing changes often and several vendors do not publish exact per-unit rates.

Who wins for which RAG stack

Profile: solo finance stack already running Postgres

pgvector. It is open-source and free, and if you already have a Postgres database the incremental cost is zero. For a filing corpus in the tens-to-hundreds-of-thousands of chunks, pgvector on a modestly sized Postgres handles retrieval fine, and you keep your vectors next to your relational metadata (tickers, dates, form types) for clean filtering in one query. This is the right default for most readers of this hub. A dedicated managed Postgres runs roughly $20-60/mo if you do not already have one.

Profile: outgrowing Postgres, want a managed vector DB with a free start

Qdrant Cloud. Its free tier is free forever (0.5 vCPU, 1GB RAM, 4GB disk, single-node), enough to prototype a filing-RAG index, and its open-source core means you can self-host the same engine later without a rewrite. Production is usage-based; the pricing page directs to a calculator rather than publishing exact per-unit rates, so size it there before committing (verified 2026-05-25). Qdrant's metadata filtering is a strong fit for the ticker/date/form-type queries finance RAG needs.

Profile: want fully-managed serverless, no ops

Pinecone. The Starter tier is free up to 2GB storage, 2M write units/month, and 1M read units/month, which is enough for a small filing corpus and light query volume. Production is the Standard plan with a $50/mo minimum spend, then pay-as-you-go: read units $16-18 per million, write units $4-4.50 per million, storage $0.33/GB/month (varies by cloud and region; verified 2026-05-25). Watch the read-unit cost, since a single filtered query can consume several read units, so high query volume is where the bill grows.

Profile: managed with usage-based dimension billing

Weaviate Cloud. Its Flex plan starts around $45/mo with usage-based billing on vector dimensions and storage, plus a managed Query Agent. A reasonable managed alternative to Pinecone; price the dimension-based model against your embedding size before choosing.

The cost that actually decides it

For finance RAG, the vector database is rarely the expensive part. Embedding a full filing corpus and then running an LLM over the retrieved chunks on every query is where the money goes. That reframes the decision: prefer the setup that minimizes re-embedding (stable chunking, durable storage) and supports good filtering (so you retrieve fewer, more relevant chunks and pay for fewer LLM tokens). Price the embedding-plus-retrieval loop in the Token-Cost Optimizer, and weigh RAG against long-context and fine-tuning in Finetune vs RAG vs Long-Context for Filings and the RAG Cost Model vs Fine-Tuning.

Decision guidance

Already on Postgres, solo stack: pgvector, free, vectors next to your metadata.
Outgrowing Postgres, want managed with OSS escape hatch: Qdrant Cloud, free-forever start.
Fully-managed serverless, no ops, light volume: Pinecone Starter (free up to 2GB), then $50/mo Standard.
High query volume: model read-unit cost carefully; filtering to fewer chunks saves both DB and LLM cost.

Finetune vs RAG vs Long-Context for Filings: when RAG beats the alternatives.
RAG Cost Model vs Fine-Tuning: the cost math behind the choice.
Structural vs Fixed Chunking: the chunking decision that matters more than the DB.
Best LLM APIs for SEC Filing Extraction 2026: picking the model that reads the retrieved chunks.

Connects to

Token-Cost Optimizer: prices the embedding-plus-retrieval loop.
SEC Filing Chunk Optimizer: sizes chunks for your embedding model and corpus.
Hallucination Detector: grounds a RAG answer in its retrieved source passage.

Sources

Pinecone. Pricing (Starter free: 2GB, 2M write units/mo, 1M read units/mo; Standard $50/mo minimum, read $16-18/M, write $4-4.50/M, storage $0.33/GB/mo, varies by cloud/region; $300 trial credits). https://www.pinecone.io/pricing/ (accessed 2026-05-25).
Qdrant. Pricing (free-forever tier 0.5 vCPU / 1GB RAM / 4GB disk single-node; production usage-based, exact per-unit rates via calculator not published on page). https://qdrant.tech/pricing/ (accessed 2026-05-25).
Weaviate. Cloud pricing (Flex plan from ~$45/mo, usage-based on dimensions and storage). https://weaviate.io/pricing (accessed 2026-05-25).
pgvector. Open-source Postgres extension (free; self-host or on managed Postgres). https://github.com/pgvector/pgvector (accessed 2026-05-25).

Editorial independence

AI Fin Hub Research maintains editorial independence across sponsor relationships. Vendor placements in tools and comparators are not altered by sponsor payments. Disclosures at /sponsor-disclosure/.

Frequently asked questions

What is the best vector database for financial RAG in 2026?: For most solo finance stacks, pgvector, because it is free, open-source, and keeps vectors next to your relational metadata (tickers, dates, form types) for clean filtering. Move to Qdrant Cloud (free-forever managed tier) or Pinecone (free Starter, $50/mo Standard) when you outgrow Postgres. The chunking strategy and embedding cost matter more than the database brand.
Is there a free vector database for RAG?: Yes. pgvector is fully open-source and free to self-host. Qdrant Cloud has a free-forever tier (0.5 vCPU, 1GB RAM, 4GB disk). Pinecone's Starter tier is free up to 2GB storage, 2M write units, and 1M read units per month (verified 2026-05-25).
How much does Pinecone cost for production RAG?: The Standard plan has a $50/mo minimum spend, then pay-as-you-go: read units $16-18 per million, write units $4-4.50 per million, and storage $0.33/GB/month, varying by cloud and region (verified 2026-05-25). A single filtered query can consume several read units, so high query volume drives the cost.
Does the vector DB or the LLM cost dominate in finance RAG?: The LLM and embedding cost dominate. Embedding the corpus and running the model over retrieved chunks on every query usually outweighs the vector-store fee, so favor good filtering (fewer, more relevant chunks) and stable chunking (less re-embedding). Price the loop in the Token-Cost Optimizer.