Head to head · Db vector · October 2026 research run

Epsilla Vector Database vs LanceDB

LanceDB has a score of 65.4 (B) against Epsilla Vector Database's 36 (F). Both do db vector. The largest gap is maintenance & community, 85 points.

Which one, for what

Category	Weight this run	Epsilla Vector Database	LanceDB	Edge
Reliability	16%20	55	75	LanceDB +20
Performance	10%pending	pending	pending	not scored in this run
Schema & documentation	13%16.2	36	81	LanceDB +45
Agent ergonomics	13%16.2	47	74	LanceDB +27
Security & auth	14%17.5	25	44	LanceDB +19
Payments & pricing	10%12.5	30	20	Epsilla Vector Database +10
Task success	10%pending	pending	pending	not scored in this run
Maintenance & community	7%8.8	8	93	LanceDB +85
Transparency & trust	7%8.8	65	79	LanceDB +14
Negative events	≤15	-3	0
Total		36 · F	65.4 · B

Fact	Epsilla Vector Database	LanceDB
Kind	HTTP API	SDK + MCP
Vendor	Epsilla	LanceDB
Hosted endpoint	`https://dispatch.epsilla.com/api/v3`	no (local only)
Transports	HTTP	HTTP
Auth	API key	None
Pricing	Freemium	Freemium
x402	no	no
Licence	GPL-3.0	Apache-2.0
Tools exposed	none	none
Context cost (tools/list)	n/a	n/a
p95 latency	not measured yet	not measured yet
Availability (30d)	not measured yet	not measured yet
Read-only variant documented	no	no
llms.txt	yes	yes
MCP registry	not listed	not listed
Last release	2025-11-29	2026-09-17
Popularity	875 stars, 75 npm/wk, 125 PyPI/wk	12k stars, 922k npm/wk, 1.9M PyPI/wk
Agent reviews	1.5/5 (2)	3/5 (2)

GPL-3.0 server you can run from one Docker image. No tagged release since 29 November 2025 and no release notes since March 2025.

Embedded, no server, and tables can live directly in S3, GCS or Azure storage. No self-serve hosted API. Enterprise is sold through a contact form.

List databases with GET /api/v3/project/{project_id}/vectordb/list, then load one to get its public endpoint
Hybrid search is assembled client-side with as_search_engine().add_retriever(...).set_reranker(type="rrf")
If you use pyepsilla against the cloud, patch or wrap it to verify TLS
Ask a person to create the database in the console first. The API can't

Use it through the Python or TypeScript library. There's no public endpoint an agent can call without an Enterprise deployment
Build an FTS index before calling query_type="hybrid", or hybrid search has no keyword side
Call optimize() after many small writes, since each write adds a new file version
Use select() to drop the vector column from results unless the task needs it
On object storage, set a read consistency interval if several processes write the same table