LanceDB vs PostgresML

A side-by-side look at pricing, capabilities, pros, cons, and our editorial scores.

Tagline

LanceDB

Open-source multimodal lakehouse and vector database built for AI training and retrieval at petabyte scale.

PostgresML

PostgreSQL extension that runs embeddings, vector search, and LLM inference inside your database.

Pricing

LanceDB

Freemium· Open-source free; LanceDB Cloud and Enterprise via contact sales

PostgresML

Freemium· Serverless: From $7.50 per query hour · Dedicated: From $0.60 per instance hour · Enterprise: Custom pricing

Free trial

LanceDB

Yes

PostgresML

Yes

API

LanceDB

Yes

PostgresML

Yes

Platforms

LanceDB

api

PostgresML

api

Open source

LanceDB

Yes

PostgresML

Yes · MIT

GitHub stars

LanceDB

—

PostgresML

6,825

checked 2026-09-29

Last GitHub push

LanceDB

—

PostgresML

2025-07-01

First commit

LanceDB

—

PostgresML

2022-04

Company

LanceDB

PostgresML

Model used

LanceDB

—

PostgresML

Multi-model (Llama, Mistral, open-source embeddings)

Best for

LanceDB

Pick LanceDB if you are building large-scale RAG, multimodal search, or model training pipelines and want one storage layer for files, metadata, and embeddings.

PostgresML

Pick PostgresML if you already run Postgres and want RAG, embeddings, and LLM calls collapsed into one query path instead of four services.

Not for

LanceDB

Skip it if you just need a small hosted vector index for a single chatbot and would rather not run infrastructure or evaluate a lakehouse.

PostgresML

Skip it if your stack isn't Postgres-centric or you need bleeding-edge proprietary models like GPT-4 or Claude.

Editorial score

LanceDB

8.2 / 10

PostgresML

7.1 / 10

Use cases

LanceDB

vector-searchragmultimodal-datasetstraining-pipelinesdata-curationhybrid-search

PostgresML

vector-searchragembeddingsllm-inferencefine-tuningin-database-ml

Pros

LanceDB

Open-source Lance format with embedded Python, TS, and Rust libraries
Handles vector, full-text, and hybrid search plus SQL filters
Scales to 100B+ rows and petabyte multimodal datasets on S3
Git-like versioning, branching, and lineage for training data
Used in production by Runway, Character.AI, Netflix, Uber, NVIDIA

PostgresML

Embeddings, vector search, and LLM inference in one Postgres extension
Eliminates network hops between app, vector DB, and inference service
Open source (PGML, Korvus, PgCat) with SQL/Python/JS SDKs
Self-host or managed cloud with VPC option
Strong benchmarks vs Pinecone on cost and latency

Cons

LanceDB

Cloud and Enterprise pricing is not public
Broader lakehouse feature set is overkill for simple RAG apps
Newer operational tooling than mature databases like Postgres+pgvector

PostgresML

Couples GPU/ML workload to your primary database
Requires Postgres operational expertise to self-host well
Smaller model catalog than dedicated inference providers

Website

LanceDB

lancedb.com

PostgresML

postgresml.org

Editorial score: rule-based, 0–10, from AI-assisted profile inputs (see /methodology) — not a user rating; “—” means unscored. “Not listed” means we have no record of it, not that it is absent. GitHub figures and prices carry the date they were checked or captured; prices are shown as published, unconverted.

Pick LanceDB if

✅ Open-source Lance format with embedded Python, TS, and Rust libraries
✅ Handles vector, full-text, and hybrid search plus SQL filters
✅ Scales to 100B+ rows and petabyte multimodal datasets on S3
✅ Git-like versioning, branching, and lineage for training data

Pick PostgresML if

✅ Embeddings, vector search, and LLM inference in one Postgres extension
✅ Eliminates network hops between app, vector DB, and inference service
✅ Open source (PGML, Korvus, PgCat) with SQL/Python/JS SDKs
✅ Self-host or managed cloud with VPC option

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