Vector Databases

Nbase

Neural Vector Database for efficient similarity search

Status
Generally available
Last checked
Checked · Oct 16, 2026
License
Open source
Pricing
Free tier · from $0
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What it is

A neural vector database designed to enable efficient and fast similarity searches. It is particularly useful in applications requiring quick retrieval of similar data points.

The only self-hosted neural vector database with efficient similarity search capabilities, making it ideal for developers who need speed and control over their infrastructure.

When it fits — and when it doesn’t

A good fit for

  • Developers building recommendation engines who need efficient vector search capabilities

  • Data scientists working on content-based filtering applications where speed is critical

Look elsewhere if

  • Projects requiring real-time data streaming and processing (batch-oriented architecture)

  • Applications that require a managed cloud service for ease of use

Known weaknesses

Each one links to the public issue, changelog or docs page it came from, where we have one.

  1. 01

    Limited language support beyond JavaScript and Python

    Primary SDKs are only available in JavaScript and Python, limiting cross-language application development

  2. 02

    Performance degradation with large-scale datasets

    Observations show slower query times as the dataset size exceeds 1 million vectors

What it does well

  • Efficient similarity search

  • Neural vector indexing

  • Self-hosted deployment

Pricing

Free tier
YesOpen source — free to use
Paid from
$0
Model
Flat rate
Enterprise plan
No

Pricing not independently confirmed — check the vendor’s page.

Performance benchmarks

How Fast Is It?

Set up Nbase

Setup steps, common gotchas and what to check after 30 days.

Open the setup guide →
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