Omendb
Fast embedded vector database with HNSW + ACORN-1 filtered search
Pricing
Free tier
Flat rate
Adoption
→StableLicense
Open Source
Data freshness
Aging · Jun 8, 2026Overview
What is Omendb?
Omendb is a fast, embedded vector database that uses HNSW and ACORN-1 for efficient filtered searches. It's ideal for applications requiring quick retrieval of high-dimensional data.
Key differentiator
“Omendb stands out as an efficient and fast embedded vector database, offering a lightweight solution for high-dimensional data retrieval with HNSW and ACORN-1.”
Capability profile
Capability Radar
Honest assessment
Strengths & Weaknesses
↑ Strengths
↓ Weaknesses
Primary support is for JavaScript and TypeScript, with no official SDKs or libraries for other languages.
The documentation provides basic setup instructions but lacks advanced usage scenarios and detailed API descriptions.
Benchmarking shows significant slowdowns when the dataset exceeds 1 million vectors, despite optimizations for high-dimensional data retrieval.
GitHub repository has minimal contributions from external developers and limited activity in issue tracking and pull requests.
Fit analysis
Who is it for?
✓ Best for
Developers building real-time recommendation engines who need fast, efficient vector searches
Data scientists working with high-dimensional data requiring quick retrieval times
Teams needing a lightweight, embedded solution for vector database needs
✕ Not a fit for
Applications that require cloud-hosted solutions due to its local hosting model
Projects where the use of JavaScript is not feasible or preferred
Cost structure
Pricing
Free Tier
Available
Open source — free to use
Starts at
$0
Model
Flat rate
Enterprise
None
Performance benchmarks
How Fast Is It?
Ecosystem
Relationships
Alternatives
Next step
Get Started with Omendb
Step-by-step setup guide with code examples and common gotchas.