Qdrant

New

Open-Source Vector Search Engine written in Rust

Vector DatabasesBerlin, Germany51–200 employeesFounded 2021

#2 of 30 in Vector Databases 68% positive in AI answers

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Visibility score

60.5%

New vs last week

Weekly run · Nov 2

Data collected Nov 2, 2026·ChatGPT, Perplexity, Google AI Mode·coverage 100%·next run Nov 9, 2026

Qdrant ranks #2 of 30 in Vector Databases with an AI visibility score of 60.5 (SlateIndex, Nov 2, 2026).
Qdrant is most visible on ChatGPT (71.8) and least on Perplexity (50.2).
AI engines name Qdrant in 26 of the 30 Vector Databases buyer prompts SlateIndex tracks.
qdrant.tech is the source AI engines cite most in answers about Qdrant, with 35 citations.

About

Company details for Qdrant

Qdrant is an open-source vector search engine written in Rust. It provides similarity search over high-dimensional vectors through a REST and gRPC API, with dense and sparse hybrid search, metadata filters and multivector retrieval. It runs as a managed cloud service, in hybrid or private cloud setups, or at the edge. It ranks #2 of 30 in Vector Databases on SlateIndex and has a visibility score of 60.5 in the latest weekly run.

Founded in 2021 and headquartered in Berlin, Germany, the company employs 51–200 people. It has raised about $88M. Its differentiator: filters are applied during HNSW graph traversal rather than before or after the search, keeping recall high with low latency under complex filter conditions.

Website: qdrant.tech
Categories: Vector Databases
Company size: 51–200 employees
Headquarters: Berlin, Germany
Founded: 2021
Company type: Private, venture-backed
Funding: Series B · $88M raised
Pricing: Contact sales · usage-based
Free trial: Free plan available
Deployment: Managed cloud, hybrid cloud, private cloud (air-gapped), self-hosted on-premises, and edge
Platforms: AWS, GCP, Azure
Compliance: SOC 2 Type 2, HIPAA
Support: Community support via Discord on the free tier, Standard tier: email and ticket portal, 10x5 business hours, Premium tier: 24/7 support with private Slack, Discord or Teams channels, Enterprise tier: custom response SLA and advisory services

GitHub · github.com/qdrant/qdrant YouTube · youtube.com/channel/UC6ftm8PwH1RU_LM1jwG0LQA LinkedIn · linkedin.com/company/qdrant/Twitter · twitter.com/qdrant_engine

Read from linkedin.com, qdrant.tech, g2.com, github.com, pulse2.com, seedtable.com · refreshed Nov 3, 2026.

AI visibility

Latest weekly run · Nov 2 · next run Nov 9

Visibility score

60.5%

across Vector Databases answers, weighted by position

ChatGPT71.8%

AI Mode59.4%

Perplexity50.2%

Change

New

vs last week’s run

Category rank

#2 of 30

in Vector Databases

Mentions

in this week’s run

Share of voice

13.4%

of AI mentions in Vector Databases

Visibility score trend

Weekly runs over the last 60 days, with up to 7 competitors. Click a name to hide or show its line.

Now60.5%

30-day change—

High · low60.5% · 60.5%

Rank#2

Changes appear after the second weekly run in Vector Databases. Pinecone leads the category at 71.8%.

Media

Website and product screenshots for Qdrant

All media

Home page

Pricing page

AI mentions & citations

Recent answers that name Qdrant

All prompts

How long does it take to roll out a vector database?

ChatGPT · run of Nov 2

Most affordable vector database that is still reliable

My recommendation: Qdrant or PostgreSQL with pgvector.

ChatGPT · run of Nov 2

Is there a free vector database for small teams?

As of November 2026, I'd shortlist Qdrant, Supabase pgvector, Chroma, and Pinecone.

ChatGPT · run of Nov 2

Which vector database has transparent pricing?

As of November 2026, Qdrant, Weaviate, and Pinecone are among the vector databases with publicly accessible pricing.

ChatGPT · run of Nov 2

What are the best Vector Databases tools in 2026?

For most teams in 2026, the strongest shortlist is Qdrant, Pinecone, pgvector, Weaviate, and Milvus/Zilliz.

Perplexity · run of Nov 2

Top vector database options for B2B SaaS companies

For most B2B SaaS companies, shortlist Qdrant, Pinecone, pgvector, Weaviate, and Milvus/Zilliz.

Perplexity · run of Nov 2

Best vector database for startups

Qdrant is the best default for most startups: it is open source, inexpensive to self-host, supports strong metadata filtering and hybrid search, and offers a managed cloud path as the product grows.

Perplexity · run of Nov 2

Which vector database has the best reviews?

Qdrant has the strongest overall user-review signal, especially for self-hosted deployments: PeerSpot rates it 8.9/10, the highest among its leading vector databases.

Perplexity · run of Nov 2

Products & features

What Qdrant sells

Qdrant Vector Search Engine

Open-source vector search engine written in Rust, available for self-hosting.

Qdrant Managed Cloud

Fully managed Qdrant clusters on AWS, GCP or Azure with auto-sharding and high availability.

Qdrant Hybrid Cloud

Managed Qdrant clusters run on the customer's own Kubernetes infrastructure, with decoupled control and data planes.

Qdrant Edge (Beta)

Lightweight, low-latency vector search close to where data is generated.

Qdrant Serverless (Waitlist)

Serverless Qdrant offering that is currently on a waitlist.

Strengths engines cite

High performance and memory efficiency, written in Rust
Strong metadata and complex payload filtering
Free tier for prototyping and testing
Open-source option with flexible self-hosting
Strong default choice for startups and production RAG

Target market & buyers

Developers and businesses building AI applications, including enterprises that need on-premises, hybrid or air-gapped deployment

Notable customers include Tripadvisor, HubSpot, OpenTable, Bazaarvoice, Bosch, Deutsche Telekom, Dust, Lyzr.

Industry	Primary use case
E-commerce	Product discovery and conversion gains in AI search
Legal tech	Legal retrieval and due diligence over hybrid vector search
Healthcare	Clinical document intelligence
Hospitality & Travel	Restaurant discovery and AI trip planning over large review and image sets
Media and entertainment	Video recommendation and editorial visual search

Funding & recent developments

Funding

Round	Year	Amount
Pre-seed	2022	$2M
Seed	2023	—
Series A	2024	$28M
Series B	2026	$50M

Latest known round	Total raised
Series B	$88M

Recent developments

Sep 2026ProductQdrant-FineWeb-10B benchmark dataset launched
Qdrant launched a benchmark dataset for testing vector retrieval at scale.
Aug 2026ProductQdrant 1.19.0 released
Qdrant 1.19.0 added a new TurboQuant storage format.
Apr 2026ProductGPU-accelerated indexing, Multi-AZ clusters and audit logging added to Qdrant Cloud
Qdrant Cloud gained GPU-accelerated indexing, Multi-AZ clusters and audit logging on paid clusters.
Mar 2026FundingSeries B announced
Qdrant announced a $50M Series B led by AVP with Bosch Ventures, Unusual Ventures, Spark Capital and 42CAP.
Jul 2025ProductQdrant Edge launched in private beta
Qdrant Edge, a lightweight database for edge devices, launched in private beta.

Competitive landscape

How Qdrant compares with its top three alternatives

All alternatives

Qdrant most often appears alongside Pinecone, Weaviate, PostgreSQL in AI answers.

vsQdrant vs Pinecone60.5% vs 71.8% visibility · Pinecone leadsFull comparison vsQdrant vs Weaviate60.5% vs 45.2% visibility · Qdrant leadsFull comparison vsQdrant vs PostgreSQL60.5% vs 49% visibility · Qdrant leadsFull comparison

Brand	Visibility	Share of voice	vs last week	Starting price
Qdrant	60.5%	13.4%	New	Free plan
Pinecone	71.8%	14%	New	Free plan
Weaviate	45.2%	11.7%	New	Free plan
PostgreSQL	49%	12.6%	New	Pricing not available
Chroma	18.6%	6.8%	New	Free plan