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{ "name": "Vector Databases for RAG: Deployment-Shape, License, Hybrid-Search and Pricing Comparison 2026", "description": "A structured comparison of 11 vector databases for retrieval-augmented generation (RAG) across five dimensions: deployment shape, open-source license, native hybrid search, metadata filtering,...
[ { "tool": "pgvector", "shape": "Extension (in Postgres)", "open_source_license": "PostgreSQL License", "hybrid_search": "partial (Postgres FTS)", "metadata_filtering": "yes (SQL)", "pricing_model": "free (pay your Postgres)", "best_for": "apps already on Postgres, under ~10M vectors" }...

Vector Databases for RAG 2026 — deployment shape, licence & hybrid search

First-party research dataset from the Lattice network, published open under CC-BY 4.0 with a permanent DOI. Nothing here is scraped from another dataset — it is computed and published under a single ORCID-verified byline.

DOI 10.5281/zenodo.20738950
Published by Nesyona (nesyona.com)
Study page https://nesyona.com/research/vector-db-rag-comparison-2026/
Licence CC-BY 4.0 — reuse freely with attribution
Publication date 2026-05-09

What it is

A structured comparison of 11 vector databases for retrieval-augmented generation (RAG) across five dimensions: deployment shape, open-source license, native hybrid search, metadata filtering, and pricing model. Compiled vendor-neutral with conflict disclosure; each cell read from the vendor's own repository, documentation, or pricing page. The thesis: hybrid search and filtering are now table-stakes, so the deciding axis is deployment shape and data gravity, not benchmark. Methodology and full sourcing at https://nesyona.com/research/vector-db-rag-comparison-2026/ . License CC-BY 4.0.

Citation

@dataset{vector_db_rag_comparison_2026,
  title  = {Vector Databases for RAG 2026 — deployment shape, licence & hybrid search},
  author = {Couey, Vincent Wesley},
  year   = {2026},
  doi    = {10.5281/zenodo.20738950},
  url    = {https://doi.org/10.5281/zenodo.20738950}
}

Provenance

Part of the Lattice open research index — every study is CC-BY, DOI-backed and published under one byline. The full index: https://www.deepsynthesis.org/#research

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