Qdrant
Qdrant is a Berlin-based, open-source vector search engine founded in 2021, providing a Rust-built database for production AI workloads. It serves enterprise customers including Tripadvisor, HubSpot, Slack, and Deutsche Telekom via managed cloud, hybrid, private, and edge deployment options.
- Company typePrivate
- Founded2021
- HeadquartersBerlin, Germany
- Headcount101–250
- GTM typeB2B
- OfferingSoftware
What Qdrant does
Qdrant is a Berlin-based, venture-backed company founded in 2021 that develops an open-source vector search engine and database purpose-built for production AI workloads. The core engine is written entirely in Rust with SIMD optimizations and a proprietary key-value store called Gridstore, supporting HNSW indexing with sub-20ms latency, native hybrid retrieval that blends dense and sparse vectors (BM25, SPLADE++, miniCOIL), multivector and late-interaction support (ColBERT), one-stage metadata filtering, and GPU-accelerated indexing across NVIDIA, AMD, Intel, and Apple Silicon hardware. The platform is consumed through four deployment paths: a free open-source build, Qdrant Cloud (managed SaaS on AWS, GCP, Azure starting at ~$25/node/month), Qdrant Hybrid Cloud (customer-managed Kubernetes), Qdrant Private Cloud (air-gapped for regulated industries), and Qdrant Edge (embedded runtime for on-device AI).
The company's revenue model is a freemium-to-enterprise funnel: the open-source distribution (250M+ downloads, 30k+ GitHub stars, 60k+ community members) drives developer adoption that converts into paid managed cloud subscriptions, custom-priced enterprise contracts with SOC 2 Type 2 and HIPAA compliance, and usage-based Cloud Inference for managed embedding generation. Go-to-market combines product-led growth through community, documentation, and AI framework integrations (LangChain, LlamaIndex, Haystack, MCP server) with direct enterprise sales targeting marquee customers including Tripadvisor, HubSpot, Slack, Deutsche Telekom, Canva, Roche, Bosch, OpenTable, Dailymotion, and Sprinklr. Use cases span retrieval-augmented generation, semantic search, recommendation systems, AI agent memory, anomaly detection, and edge AI.
Qdrant Solutions GmbH is incorporated in Berlin (HRB 235335 B) with a regional presence in New York and 101–250 employees. The company has raised $87.8M total across four rounds (Pre-Seed €2M in 2022, Seed $7.5M in 2023 led by Unusual Ventures, Series A $28M in January 2024, Series B $50M in March 2026 led by AVP with participation from Bosch Ventures, Unusual Ventures, Spark Capital, and 42CAP), and disclosed $9.2M of ARR as of 2024.
Qdrant firmographics
Firmographics- Name
- Qdrant
- Legal name
- Qdrant Solutions GmbH
- Website
- https://qdrant.tech
- Company type
- Private
- Founded year
- 2021
- Operating status
- Operating
- Headcount range
- 101–250 employees
- Short description
- Qdrant is a Berlin-based, open-source vector search engine founded in 2021, providing a Rust-built database for production AI workloads. It serves enterprise customers including Tripadvisor, HubSpot, Slack, and Deutsche Telekom via managed cloud, hybrid, private, and edge deployment options.
- Ownership category
- akta.pro rank
Qdrant industry classification
Industry- Product category
- Vector Database / AI Retrieval Infrastructure
- NAICS
- Software Publishers (5132), Computing Infrastructure Providers, Data Processing, Web Hosting, and Related Services (5182), Computer Systems Design and Related Services (5415)
- SIC
- Services-Prepackaged Software (7372), Services-Computer Programming, Data Processing, Etc. (7370)
- akta.pro primary industry
- Database/Application Data Layer Modernization (DB migration, ORM, caching) (BPAEAFAL)
- akta.pro secondary industries
- On-Device Inference Runtimes & SDKs (mobile/embedded) (HDAAAJAB), Developer APIs & Node Access (RPC providers, node infrastructure APIs) (FSAPABAC)
Keywords
Where Qdrant is headquartered
LocationHeadquarters
- HQ city
- Berlin
- HQ country
- Germany
- HQ region
- Europe
Offices2 records
Markets served
Qdrant business model
Business model- GTM type
- B2B
- Offering type
- Software
- Cost components
- Personnel, Technology or R&D, Infrastructure, Marketing or Sales, Operations
Revenue model
- Qdrant Cloud (Managed SaaS): Fully managed vector database service with high availability, auto-sharding, and enterprise features. Operates on AWS, GCP, and Azure with subscription pricing per node per month. Provides zero-ops deployment option for teams that want managed infrastructure.
- Qdrant Hybrid Cloud: Bring-your-own-Kubernetes deployment with decoupled control and data planes, offering full data control with cloud-like management for enterprises with specific infrastructure requirements.
- Qdrant Private Cloud: Air-gapped, compliant deployments for maximum data control, targeting regulated industries requiring on-premises or private cloud deployments.
- Qdrant Enterprise Solutions: Enterprise-tier subscriptions with dedicated support, SSO (SAML/OIDC), advanced security features, and professional services for production deployments.
- Open Source (Community): Free open-source version available for self-hosting, driving community adoption and product-led growth. Serves as funnel for cloud conversion and enterprise upsell.
Pricing tiers
| Model | Billing | Price |
|---|---|---|
| Freemium | Monthly | Free Tier - Limited resources for development and small-scale testing |
| Subscription | Monthly | Qdrant Cloud Managed - Starting at ~$25/node/month |
| Subscription | Annual | Enterprise Solutions - Custom pricing with premium support |
| Usage-based | Pay-as-you-go | Cloud Inference - Free tokens included with paid usage |
Go-to-market motion2 records
Distribution channels6 records
Marketing channels9 records
Qdrant product offering
Product offeringCore offering
Qdrant sells an open-source vector database and search engine built in Rust that enables high-performance similarity search, hybrid dense-sparse retrieval, and AI agent memory at scale. The core engine is delivered through multiple deployment models — a free open-source download, a fully managed Qdrant Cloud SaaS, a Hybrid Cloud (BYO Kubernetes) option, an air-gapped Private Cloud, a managed Qdrant Cloud Inference embedding service, and a lightweight Qdrant Edge runtime for embedded devices — targeting developers and enterprises building RAG, semantic search, recommendation, and AI agent applications.
Product overview
Qdrant is an AI infrastructure company offering a portfolio of vector search products centered on its open-source Rust-built vector database engine. The core Qdrant Vector Database provides high-performance similarity search with hybrid dense-sparse retrieval, HNSW indexing, and GPU acceleration. This core engine is delivered through multiple deployment paths: Qdrant Cloud (fully managed SaaS with enterprise features), Qdrant Hybrid Cloud (customer-managed Kubernetes), and Qdrant Private Cloud (air-gapped enterprise deployments). Qdrant Edge extends the engine to resource-constrained edge devices for on-device AI. Qdrant Cloud Inference adds managed embedding generation, while Qdrant Enterprise Solutions provides premium support and compliance capabilities. The product family supports text and image modalities, enabling use cases from RAG and semantic search to recommendation systems and AI agent memory.
Differentiator
Problem solved
Functional benefit
Brands
- Qdrant Vector Database: Open-source vector search engine built in Rust for production AI workloads
- Qdrant Cloud
- Qdrant Hybrid Cloud
- Qdrant Private Cloud
- Qdrant Enterprise Solutions
- Qdrant Cloud Inference
- Qdrant Edge (Beta)
- FastEmbed
Products and services
- Qdrant Vector Database Open-source vector search engine built in Rust, providing high-performance similarity search and retrieval with HNSW indexing, hybrid dense-sparse retrieval, payload filtering, multivector support, and GPU-accelerated indexing. Powers production AI workloads including RAG, semantic search, and recommendation systems, and is distributed via GitHub for self-hosted deployments.
- Qdrant Cloud Fully managed cloud vector database service with high availability, auto-sharding, GPU-accelerated indexing, multi-AZ clusters with 99.95% uptime SLA, audit logging, and inference capabilities. Operates on AWS, GCP, and Azure with flexible scaling and a free tier, targeting developers and enterprises seeking zero-ops vector search.
- Qdrant Hybrid Cloud Bring-your-own-Kubernetes deployment model with decoupled control and data planes, enabling organizations to run Qdrant on their own infrastructure while maintaining full data control and cloud-agnostic portability. Targeted at organizations that want cloud-like management with on-premises or private infrastructure control.
- Qdrant Enterprise Solutions Enterprise-grade vector search offering that bundles Qdrant Private Cloud (air-gapped, compliant deployments for regulated industries), dedicated support, SSO (SAML/OIDC), advanced RBAC, audit logging, SOC2 Type 2 and HIPAA compliance, and professional services for production deployments at enterprise scale.
- Qdrant Cloud Inference Fully managed service that integrates text and image embedding generation directly into the managed vector search engine, enabling developers to generate, store, and index embeddings in a single API call. Supports MiniLM, SPLADE, BM25, and CLIP models with usage-based pricing beyond included free tokens.
- Qdrant Edge Lightweight vector database for embedded AI that runs locally on edge devices such as robots, kiosks, and mobile devices without requiring a connected server process. Provides hybrid and multimodal search with deterministic performance, in-process execution, and full lifecycle data control for privacy-sensitive and on-device AI applications.
Quantifiable outcome
- 250+ million downloads of open-source project
- +6 more outcomes
Companies that use Qdrant
Customer profileNamed customers13 records
Segments8 records
Ideal customer profiles3 records
Qdrant technology and API
TechnologyTechnology focussed Yes
API detail
- Has API
- Yes
- API docs
- API detail
Core technology
AI maturity
App detail
Integration4 records
AI capability7 records
Feature10 records
Qdrant partnerships and signals
Strategic signalPartnerships
19 partnerships are on record, tiered core_partner, ecosystem_partner, strategic_customer, core_infrastructure_partner, infrastructure_partner, operational_partner and marketing_partner.
- LlamaIndexcore_partnerLlamaIndex is a featured partner at Vector Space Day 2026, demonstrating integration partnership for AI agent development. Qdrant serves as a recommended vector database backend for LlamaIndex-based RAG applications.
- Google DeepMindecosystem_partnerGoogle DeepMind is a featured speaker at Vector Space Day 2026, representing technology ecosystem partnership for advancing agent capabilities and retrieval systems.
- Adobeecosystem_partnerAdobe is presenting at Vector Space Day on GraphRAG approaches combining Qdrant's vector search with Neo4j graph governance for enterprise AI governance.
- HubSpotstrategic_customerHubSpot is presenting on building infrastructure for 20+ billion vectors, demonstrating deep technical partnership and customer case study relationship.
- Slackstrategic_customerSlack is presenting on scaling semantic search to billions of vectors, representing a strategic customer using Qdrant for enterprise-grade semantic search at massive scale.
- Neo4jecosystem_partnerNeo4j is presenting on context graphs for AI agents at Vector Space Day, demonstrating integration partnership for combining graph databases with vector search.
- Qualcommecosystem_partnerQualcomm is presenting on on-device GenAI patterns at Vector Space Day, demonstrating edge AI partnership for running Qdrant Edge on mobile and edge devices.
- TwelveLabsecosystem_partnerTwelveLabs is presenting on agentic video intelligence at Vector Space Day, demonstrating integration for multimodal video search using Qdrant.
- Mem0ecosystem_partnerMem0 is presenting on continual learning and memory for AI agents at Vector Space Day, representing partnership for agent memory infrastructure.
- Cogneeecosystem_partnerCognee is presenting on AI memory patterns at Vector Space Day, representing partnership for memory and retrieval systems.
- Arize AIecosystem_partnerArize AI is presenting on retrieval evaluation at Vector Space Day, representing partnership for AI observability and evaluation of vector search systems.
- AWS (Amazon Web Services)core_infrastructure_partnerAWS is a sponsor and presenting at Vector Space Day, with Qdrant Cloud available on AWS infrastructure. AWS also sponsors the event and Qdrant supports AWS deployments for GPU-accelerated indexing.
- Vultrinfrastructure_partnerVultr is sponsoring Vector Space Day and presenting on distributed enterprise-ready agentic AI, representing cloud infrastructure partnership.
- LangChaincore_partnerOfficial integration partnership with LangChain for RAG and LLM applications. Qdrant is recommended as a vector database backend for LangChain-based AI applications.
- Haystackecosystem_partnerOfficial integration partnership with Haystack NLP framework by deepset, enabling Qdrant as a vector store backend for production search applications.
- Stripeoperational_partnerStripe handles payment processing for Qdrant Cloud subscriptions, acting as a data processor for billing and payment operations.
- Netlifyoperational_partnerNetlify hosts Qdrant's marketing website (qdrant.tech) and content delivery network, providing web hosting infrastructure.
- Auth0operational_partnerAuth0 provides authentication services for Qdrant Cloud, enabling secure login and single sign-on functionality for the managed platform.
- HubSpotmarketing_partnerHubSpot is used for marketing automation, lead management, landing pages, and newsletter distribution for Qdrant's marketing operations.
Scale indicators6 records
Recent moves6 records
Expansion highlights8 records
Qdrant competitors and assessment
Company assessmentDirect peers
- Pinecone: Pinecone is a managed vector database purpose-built for production AI applications. It directly competes with Qdrant in the enterprise vector-search market, offering similar RAG, semantic search, and recommendation use cases with managed-service pricing.
- Weaviate: Weaviate is an open-source vector database with hybrid search and a managed cloud offering (Weaviate Cloud Services). It is one of Qdrant's closest direct competitors, serving the same RAG and semantic-search enterprise workloads.
- Milvus (Zilliz): Milvus, developed by Zilliz, is an open-source vector database designed for large-scale similarity search. It competes head-to-head with Qdrant on open-source community traction, scale to billions of vectors, and managed-service offerings (Zilliz Cloud).
- Chroma: Chroma is an open-source embedding database popular among AI application developers for prototyping and production RAG. It targets a similar developer-led audience as Qdrant with a Chroma Cloud managed offering.
Broad incumbents
- MongoDB Atlas Vector Search: MongoDB has added native vector search to its Atlas platform, allowing existing customers to run vector workloads alongside operational data. It is a broad incumbent bundling vector search into a general-purpose document database, posing an integration-threat to standalone vector databases like Qdrant.
- Elastic (Elasticsearch): Elastic has integrated vector search (dense_vector) and hybrid retrieval (RRF) into Elasticsearch and Elastic Cloud. It is a broad incumbent whose installed base of search/log analytics customers makes it a frequent substitute for vector-database purchases.
- Vespa: Vespa is a Yahoo-origin engine for online low-latency serving of big data, including vector search and hybrid retrieval. It is a broad incumbent serving enterprise AI and search workloads comparable to Qdrant's RAG and semantic-search use cases.
- Google Vertex AI Vector Search: Google Cloud's fully managed vector search service (formerly Matching Engine) is bundled within the Vertex AI platform. As a broad hyperscaler incumbent, it competes with Qdrant Cloud for enterprises standardizing on GCP.
Emerging players
- pgvector (PostgreSQL): pgvector is an open-source PostgreSQL extension that adds vector similarity search to the world's most popular open-source relational database. It is an emerging substitute for teams that prefer a single SQL database instead of a dedicated vector engine.
- Marqo: Marqo is a cloud-native vector and tensor search engine focused on multimodal and end-to-end AI applications. It is an emerging player competing with Qdrant for production multimodal retrieval workloads.
Market position
Strengths5 records
Weaknesses5 records
Competitive moat6 records
Key risks6 records
Key highlights7 records
Customer concentration
Qdrant social profiles
Digital presenceQdrant compliance and trust
Trust signalCompliance3 records
Qdrant financial estimates
Financial estimateRevenue estimate
Valuation estimate
Qdrant leadership team
Management profileNumber of profiles
Profiles2 records
Qdrant funding detail
Funding detailFunding overview
Funding rounds5 records
Investors8 records
Funding detail is available on the Subscription and Enterprise plan.Contact sales →
Qdrant M&A and investment
M&A and investmentM&A
Investments
M&A and investment is available on the Subscription and Enterprise plan.Contact sales →
Frequently asked questions about Qdrant
What does Qdrant do?
Qdrant sells an open-source vector database and search engine built in Rust that enables high-performance similarity search, hybrid dense-sparse retrieval, and AI agent memory at scale. The core engine is delivered through multiple deployment models — a free open-source download, a fully managed Qdrant Cloud SaaS, a Hybrid Cloud (BYO Kubernetes) option, an air-gapped Private Cloud, a managed Qdrant Cloud Inference embedding service, and a lightweight Qdrant Edge runtime for embedded devices — targeting developers and enterprises building RAG, semantic search, recommendation, and AI agent applications.
Is Qdrant a public or private company?
Qdrant is a private company. It is classified as venture growth investor backed and is currently operating.
When was Qdrant founded?
Qdrant was founded in 2021. It employs 101 to 250 people.
Where is Qdrant based?
Qdrant is headquartered in Berlin, Germany, in the Europe region.
How does Qdrant make money?
Five revenue lines are on record. Qdrant Cloud (Managed SaaS) is the primary driver. The others are qdrant Hybrid Cloud, qdrant Private Cloud, qdrant Enterprise Solutions and open Source (Community).
Who are Qdrant's main competitors?
Direct peers on record are Pinecone, Weaviate, Milvus (Zilliz) and Chroma. Broad incumbents are MongoDB Atlas Vector Search, Elastic (Elasticsearch), Vespa and Google Vertex AI Vector Search. Emerging players are pgvector (PostgreSQL) and Marqo.
Does Qdrant have an API?
Yes. Qdrant provides a public REST and gRPC API for vector database operations including collection management, point operations, search, indexing, snapshots, aliases, and distributed deployment configuration. The API is documented at api.qdrant.tech with official client libraries for Python, TypeScript/JavaScript, Rust, Go, .NET, and Java. Qdrant also offers an MCP Server for AI agent integration enabling semantic memory capabilities. Developer documentation is at api.qdrant.tech/api-reference.
What industry is Qdrant in?
Qdrant's product category is Vector Database / AI Retrieval Infrastructure. Its primary akta.pro industry code is BPAEAFAL, Database/Application Data Layer Modernization (DB migration, ORM, caching), with a secondary code of HDAAAJAB, On-Device Inference Runtimes & SDKs (mobile/embedded). Its NAICS code is 5132 and its SIC code is 7372.