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Qdrant

Qdrant is an open-source Rust vector database and similarity-search engine that stores vectors with associated payloads and provides an API for searching, filtering, and managing them.

Independent OpenViST project profile, based on linked sources.

Qdrant — OpenViST project profile in RAG & Knowledge Systems.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use Qdrant when an application needs vector retrieval combined with payload filtering and an API-driven storage layer. The self-hosted server suits client-server deployments, Qdrant Cloud provides a managed option, and Qdrant Edge suits local or offline application scenarios.

Who it is for

  • AI engineers building semantic retrieval systems
  • Teams developing RAG knowledge bases
  • Developers creating document, product, image, or multimodal search
  • Builders of recommendation and nearest-neighbor matching services

Documented uses

  • Store embeddings with metadata or other payloads
  • Retrieve similar vectors through nearest-neighbor search
  • Filter semantic or neural-network search results by payload
  • Build semantic document and product search
  • Support image similarity, recommendations, matching, and hybrid search
Before you choose

Requirements & limits

The project is public and open source under the Apache-2.0 license. Documented deployment options include self-hosted server, Qdrant Cloud, and Qdrant Edge. The repository also documents official client libraries for Go, Rust, JavaScript or TypeScript, Python, .NET or C#, and Java.

  • Payload filtering is a central distinction for applications that need metadata-constrained vector retrieval.
  • The project is implemented in Rust and exposes an API for storing, searching, and managing vector points.
  • Qdrant supports both standard client-server deployment and an in-process Edge option for local or offline use.
  • The repository documents a Docker quick start, but its example server has no authentication and listens on all network interfaces.
  • Integration can use official clients for several major programming languages, with additional community clients for Kotlin and PHP.

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

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
QdrantStore embeddings with metadata or other payloadsCloud, Self-hosted, ServerThe project is public and open source under the Apache-2.0 license. Documented deployment options include self-hosted server, Qdrant Cloud, and Qdrant Edge. The repository also documents official client libraries for Go, Rust, JavaScript or TypeScript, Python, .NET or C#, and Java.
MettisIndex text documents through an HTTP endpoint.Linux, Self-hosted, Server
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
1 October 2026
Original website title
qdrant/qdrant · Official homepage title · retained in its source language
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