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Blaze Db

Blaze Db is a Rust-based, self-managed vector database that stores embeddings on disk and retrieves approximate nearest neighbors using HNSW indexing and TurboQuant quantization.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Blaze Db — OpenViST project profile in AI Infrastructure & Serving.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use Blaze Db when you want to operate a disk-backed vector index yourself and query it for similar items, such as products in a recommendation or semantic-search workflow.

Who it is for

  • Developers building applications that need embedding-based similarity retrieval.
  • Teams seeking a self-hosted vector store with server, client and CLI components.

Documented uses

  • Store and index embeddings on disk.
  • Retrieve similar items using cosine, Euclidean or dot-product distance.
  • Create a database and query it through the CLI.
  • Run batch or chunk embedding-generation processing through the CLI wrapper.
Before you choose

Requirements & limits

The repository is public and identifies the project as MIT-licensed. The README documents building from source with Cargo and also provides a Docker image workflow.

  • The project offers cosine, Euclidean and dot-product distance metrics.
  • The CLI wrapper's batch and chunk processing is specifically described for embedding generation.
  • The README notes caveats in backup and restore and says lazy deletion and background reindexing need improvement.
  • Published performance numbers are project-reported benchmarks and should not be treated as a guarantee for other workloads.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Create a database and query it with the CLI

Use the documented CLI commands to create a database and request the top matching results.

Before you start

  • Blaze Db CLI (blzdb) available.
  • A configured source named default_src and an embedding database.

Steps

  1. Create the database with blzdb create --database amazon_products_2023 --source default_src.

  2. Query it with blzdb query --database amazon_products_2023 --source default_src --search "Wireless Bluetooth Headphones with Noise Cancellation" --top_k 10.

Expected outcome

The CLI requests up to 10 matches for the supplied text from the named database and source.

Guide limitations

  • The example presumes the database and source are available; the commands alone do not prepare embeddings or configure an embedding provider.
Documentation 1 ↗
COMPARE OPTIONS

Compare options for Vector search

These projects document a common task and share a project role. This is a source-based comparison, not an execution benchmark or a guarantee of interchangeability.

ProjectDocumented purposePlatformAccess and requirements
Blaze DbStore and index embeddings on disk.CLI, Self-hosted, ServerThe repository is public and identifies the project as MIT-licensed. The README documents building from source with Cargo and also provides a Docker image workflow.
LlmakerProvision a model together with vector search, embeddings, caching or observability services.CLI, Docker, Self-hostedThe README requires Docker and lists Linux and macOS for prebuilt binaries. It also documents installation through Go or from source. The project is labeled alpha.
Weaviate — weaviateStore application objects alongside vectors and retrieve similar items.Cloud, Self-hosted, ServerThe repository describes Weaviate as open source and lists Docker, Kubernetes and Weaviate Cloud deployment options.
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
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Sources checked
16 October 2026
Original website title
ronakgh97/blaze-db · Official homepage title · retained in its source language
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