Does Qdrant have an API?

Yes, Qdrant offers a public API for developers to integrate its capabilities into custom applications and workflows.

Launching soon — get early access:

/

AI Tools

Q: What can Qdrant do?

Qdrant can: Expansive metadata filters (JSON, nested, text, geo), Native hybrid search (dense + sparse) with BM25, SPLADE++, miniCOIL, Built-in multivector support for multimodal retrieval, Efficient one-stage filtering during HNSW traversal. Best for: Building production-grade AI search applications.

/

Qdrant

Open Source

High-performance vector database

Open-source core

Enterprise-ready deployments

Advanced search capabilities

Rust-powered efficiency

High-performance vector search engine for production-grade AI applications at any scale.

Last updated: May 30, 2026

FREEMIUM

qdrant.tech

Quick Answer

Qdrant is an open-source, high-performance vector database for AI retrieval and search. It offers advanced features like hybrid search, metadata filtering, and multivector support, built in Rust for speed and scalability. Best for developers and enterprises building production-grade AI applications, RAG systems, and AI agents. Available as open-source, free cloud tier, and paid managed cloud options.

About Qdrant

Qdrant is an open-source vector database designed for high-performance, full-feature vector search. It enables real-time retrieval for AI applications, supporting various deployment models including cloud, hybrid, and on-premise. Qdrant offers advanced metadata filtering, native hybrid search, and multivector capabilities for enhanced relevance.

What Makes It Unique

Qdrant is built entirely in Rust with SIMD and a custom storage engine (Gridstore) for highest performance and memory efficiency, offering real-time indexing and advanced quantization techniques for vector search.

AI-extracted from public sources. May contain errors. Methodology ·

Are you Qdrant? Claim listing

Use Cases & Target Users

Use Cases

Search

Data Analysis

Best For

Building production-grade AI search applications

Implementing Retrieval Augmented Generation (RAG)

Developing intelligent AI agents with persistent memory

Powering personalized recommendation systems

Detecting anomalies and outliers in high-dimensional data

Company Size

SMALL

MEDIUM

ENTERPRISE

Fit Score

Complexity

Advanced

Target Team Size

SMALL_TEAM

LARGE_TEAM

Target Skill Level

INTERMEDIATE

EXPERT

Key Features

Expansive metadata filters (JSON, nested, text, geo)

Native hybrid search (dense + sparse) with BM25, SPLADE++, miniCOIL

Built-in multivector support for multimodal retrieval

Efficient one-stage filtering during HNSW traversal

Full-spectrum reranking with score boosting and MMR

Real-time indexing without rebuilding the entire index

Technical Specifications

API Available

SDK Available

MCP Compatible

Open Source

Apache-2.0

Team Features

SSO/Enterprise

Webhook Support

Has Demo

Function Calling

Browser Extension

Sandbox Mode

Audit Logs

Free Plan

No Signup

Works Offline

Requires Host App

Framework

MCP Server

IDE Plugin

Inference API

SCIM Provisioning

Developer Access

API

ENTERPRISE

SDK

Deployment:

HYBRID

Uses models:3

Compliance, Privacy & Trust

GDPR (as claimed): Yes

SOC 2 (as claimed): Yes

HIPAA (as claimed): Yes

Trains on user data: No

Pros

+Exceptional performance and scalability due to Rust-based architecture
+Flexible deployment options: Managed Cloud, Hybrid Cloud, Private Cloud, Edge
+Comprehensive features for advanced AI search, including hybrid search and reranking
+Strong enterprise-grade security and compliance (SOC2, GDPR, HIPAA)
+Developer-friendly with intuitive APIs and a built-in Web UI
+Active open-source community and extensive documentation

Limitations

-Free tier has limited resources (0.5 vCPU, 1GB RAM, 4GB Disk)
-Advanced enterprise features like SSO and private VPC links are only available in Premium/Enterprise tiers
-Cloud Inference with paid models requires tokens or specific tiers
-Disk encryption with custom keys is currently AWS only

Uses Models

ColbertBM25SPLADE++

Decision Intelligence

Best For

Building production-grade AI search applications

Implementing Retrieval Augmented Generation (RAG)

Developing intelligent AI agents with persistent memory

Powering personalized recommendation systems

Detecting anomalies and outliers in high-dimensional data

Not Ideal For

Users needing a simple keyword-only search solution without vector capabilities

Small projects that do not require high-performance or scalable vector search

Non-technical users who prefer no-code solutions for data management

Applications that require offline-only vector search without Edge deployment

Frequently Asked Questions

Your Actions

Dimension Scores

Ease of Use8/10

Output Quality9/10

Value for Money8/10

Customization9/10

Support9/10

Integration8/10

Task Scores

Search Accuracy

10

Real Time Indexing

10

Multi Modal Search

10

Filter Refinement

10

Semantic Understanding

9

Personalization

9

Source Attribution

7

Answer Extraction

3

Trust Score

70/100

Good

Based on 11 data signals

Reflects publicly available data, not an independent audit

Social Proof10/40

Customers (per vendor website)Canva, Hubspot, Bosch +7

Company Strength25/25

Team Size51-200

Funding$87.8M

Company ListedQdrant Solutions GmbH

Operational Trust20/20

Uptime SLA99.9%

Status PageView

Data ResidencyNorth America (N. Virginia, N. California, Oregon), Europe (Frankfurt, Ireland, London), Asia Pacific (Tokyo, Singapore, Sydney, Mumbai), South America (São Paulo). Also supports on-premise, hybrid, and edge deployments.

Open Source29.6K stars

Compliance15/15

GDPR (as claimed)Yes

SOC 2 (as claimed)Yes

HIPAA (as claimed)Yes