LanceDB vs Qdrant vs Turbopuffer vs Weaviate

A side-by-side look at scores, pricing and features — with RECATOOLS' ASEAN-aware verdict for each.

	LanceDB Embedded, serverless vector DB for multimodal AI, no server to run Visit	Qdrant Rust vector search engine built for filtering-heavy AI workloads Visit	Turbopuffer Vector + BM25 search on S3 — cheap when data goes cold Visit	Weaviate Open-source vector DB with hybrid search, now backed by $200M Visit
RECATOOLS Score	8 / 10	8.7 / 10	7.3 / 10	8 / 10
Capability	8	9	7	8
Value for money	9	9	8	8
Ease of use	8	6	7	6
ASEAN readiness	6	6	5	7
API quality	8	9	8	9
Pricing	Open Source	Open Source	Paid	Open Source
Free tier	—	—	—	—
Paid from	—	—	—	—
Has API	✓	✓	✓	✓
Open source	✓	✓	✗	✓
Free to use	✓	✓	✗	✓
Users	—	—	—	—
Founded	2022	2021	2023	2019
Maker	—	—	—	—
Verdict	LanceDB's pitch is genuinely different from the rest of this category: it's embedded, not a service you stand up, so there's no server process to run for local development or edge deployment — import the library and you'...	Qdrant's pitch is speed with filters attached — G2 reviewers give it a 9.3 for semantic search accuracy and 9.1 for ease of use in head-to-head comparisons, and metadata filtering (combining vector similarity with struct...	Turbopuffer's bet is that most vectors sit idle between queries, so keeping them in RAM at all times is a waste of money. It stores indexes on S3 (or GCS/Azure) with a hot cache for whatever got hit recently, and that ar...	Weaviate pairs vector similarity with BM25 keyword search in a single query object, and built-in vectorizer modules mean you're not always wiring your own embedding pipeline. G2 reviewers (30+, averaging around 4.6) call...
	Full review →	Full review →	Full review →	Full review →

← Back to AI Directory

Comparisons cover up to 4 tools. Scores are RECATOOLS editorial assessments; verify current pricing on each vendor's site.