Qdrant
Open-source vector search engine built in Rust for production AI workloads — the qdrant/qdrant GitHub repository (29k+ stars, 250M+ downloads) plus Qdrant Cloud managed deployment, with enterprise customers including Tripadvisor, HubSpot, OpenTable, Bazaarvoice, Canva, Roche and Bosch.
The Business
Qdrant builds the open-source vector search engine for production AI workloads. The product line has two main surfaces: the open-source qdrant/qdrant project distributed via GitHub (Rust-built, with 29k+ stars and 250M+ downloads as of the March 2026 Series B disclosure), and Qdrant Cloud, the managed-cluster deployment for enterprise customers. The platform is positioned as composable vector-search infrastructure for AI applications — retrieval-augmented generation, semantic search, recommendation systems and similar production workloads requiring high-dimensional vector similarity search. The company was founded in 2021 in Berlin by Andre Zayarni (CEO) and Andrey Vasnetsov (CTO), and has raised approximately $87.8M of external capital through the March 2026 $50M Series B led by AVP with Bosch Ventures, Unusual Ventures, Spark Capital and 42CAP participating.
Customers and Distribution
Qdrant does not file public financials and has not disclosed a precise ARR figure at the Series B cycle. The Series B announcement disclosed 250M+ downloads and 29k+ GitHub stars on the open-source qdrant/qdrant repository, and named production-scale enterprise customers including Tripadvisor, HubSpot, OpenTable, Bazaarvoice, Canva, Roche and Bosch. Distribution sits across two motions: the open-source qdrant/qdrant project distributed via GitHub as the principal community-funnel driver, and Qdrant Cloud as the managed-deployment commercial channel for enterprise customers. The strategic-investor cohort — Bosch Ventures (customer-and-investor relationship), AVP, Unusual Ventures, Spark Capital and 42CAP — reflects the production-scale enterprise procurement orientation of Qdrant’s positioning.
Model Strategy
Qdrant is a Verticals-first AI-infrastructure play under the IM Framework eight-trajectories taxonomy: the strategic bet is that vector search is a defensible production-AI infrastructure primitive, and that vertical depth on the vector-search workflow — Rust-built performance, open-source distribution, production-scale reliability, composable architecture — beats managed-only competitors and broader data-platform extensions on production retrieval workloads. The Rust-based architecture is the deliberate engineering decision: infrastructure on the critical path of production AI cannot afford garbage-collection pauses, memory-safety issues or the performance unpredictability of managed runtimes, and Rust is the language Qdrant chose to deliver the production-scale guarantees the customer base requires. The platform is model-agnostic by design — Qdrant stores and serves vectors from any embedding model the customer chooses to use — positioning it as the retrieval primitive rather than as a layer locked to specific foundation-model providers.
At A Glance
The Numbers
Annualised revenue
Headcount (FTE)
Funding to date
Leadership Team
Qdrant is founder-led with Andre Zayarni and Andrey Vasnetsov as the principal public voices. The senior team has expanded on the GTM and Cloud-platform engineering side following the March 2026 Series B; named senior commercial appointments below the co-founders are not separately disclosed.
IM Framework Scoring
IM’s structured assessment of Qdrant’s competitive position. The summary below is the headline; expand “Show the full analyst-grade analysis” near the bottom for the per-dimension reasoning and evidence. Methodology →
Funding History
| Date | Round | Raised | Post-money | Lead investor(s) |
|---|---|---|---|---|
| Mar 2026 | Series B | $50M | — | AVP (with Bosch Ventures, Unusual Ventures, Spark Capital, 42CAP) |
| Jan 2024 | Series A | $28M | — | Spark Capital (with Unusual Ventures and 42CAP continuing) |
| 2023 | Seed | $7.5M | — | Unusual Ventures (with 42CAP) |
Cumulative external capital approximately $87.8M through the March 2026 $50M Series B led by AVP with Bosch Ventures, Unusual Ventures, Spark Capital and 42CAP participating. The Series B announcement disclosed 250M+ downloads and 29k+ GitHub stars on the open-source qdrant/qdrant repository.
Competitive Landscape
| Competitor | Positioning | Distribution edge | Threat profile |
|---|---|---|---|
| Pinecone | Managed-first, serverless vector database; closed-source SaaS with consumption pricing. Pioneer category-namer of the vector-DB space; emphasis on ease-of-use and zero-ops onboarding. | Self-serve cloud sign-up plus AWS, GCP and Azure marketplace listings; cited as the default first stop in most LangChain / LlamaIndex tutorials. | High — the managed-first vector-database incumbent with the largest commercial revenue base in the standalone vector-database category. Direct head-to-head on enterprise procurement of vector-search infrastructure. |
| MongoDB Atlas Vector Search (MongoDB (NASDAQ: MDB)) |
Vector search as a feature inside MongoDB Atlas — unified operational + vector store, no separate system to deploy. Lands the “already have MongoDB, why add a vector DB?” pitch. | Embedded in the MongoDB Atlas footprint — 50,000+ paying Atlas customers reached via the existing enterprise sales motion and cloud-marketplace billing. | High — vector-search integrated into the broader MongoDB Atlas managed database; channel control via the MongoDB enterprise install base, compressing the standalone vector-database category. |
| Weaviate | Open-source vector DB with built-in vectoriser modules and a hybrid keyword-plus-vector retrieval story; Go-built, BSD-licensed. Closest philosophical peer to Qdrant on the OSS-first axis. | Open-source GitHub distribution plus Weaviate Cloud Services and hyperscaler marketplaces; developer-led adoption via documentation and integrations. | Medium-High — open-source vector-database competitor with comparable Cloud-managed deployment; similar positioning to Qdrant in the open-source-first lane. |
| Chroma | Developer-first, embedded vector DB — Apache-2 licensed, Python-native, optimised for the local-dev and prototyping loop rather than production-scale serving. | Pip-install ubiquity in LangChain / LlamaIndex tutorials and notebooks; the default choice for hobbyist and early-stage builds. | Medium — developer-first open-source vector database with strong adoption in the LangChain / LlamaIndex ecosystem; less production-scale than Qdrant but competitive on developer mindshare. |
| Amazon OpenSearch / Azure AI Search / Vertex AI Vector Search (Hyperscaler bundle) |
Vector search as a bundled feature inside the broader hyperscaler search and AI stack — not a standalone product, but a check-the-box capability for existing cloud customers. | Hyperscaler enterprise sales, committed-spend drawdown and console-level discoverability; effectively zero-friction for any team already on AWS, Azure or GCP. | High — hyperscaler-native vector-search services bundled into cloud-platform procurement; structural compression risk on the standalone vector-database category as customers default to native cloud-platform search infrastructure. |
Potential Risks
Hyperscaler vector-search compression
Amazon OpenSearch, Azure AI Search and Google Vertex AI Vector Search all ship native vector-search capabilities bundled into hyperscaler cloud commitments. The structural risk is that enterprise customers default to native cloud-platform search infrastructure rather than procure a standalone vector database. Qdrant’s hedge is the production-scale Rust-performance differentiator and the named customer references that demonstrate the trade-off.
Managed-database incumbent flanking
MongoDB Atlas Vector Search bundles vector search into the broader Atlas managed-database platform with the MongoDB enterprise install base as channel control. Elastic and OpenSearch flank from the search-platform lane. The structural risk is whether enterprise customers procure standalone vector-database infrastructure or default to vector-search extensions of platforms they already procure.
Open-source-to-Cloud conversion
Qdrant’s distribution motion is open-source-first with 250M+ downloads and 29k+ GitHub stars on the qdrant/qdrant repository. The commercial conversion from community self-host usage to Qdrant Cloud revenue is the principal commercial watch-item; the dynamic mirrors the n8n / fair-code question on how much open-source distribution converts into managed-platform revenue.
Standalone-vector-database competitive intensity
Pinecone is the managed-first incumbent with the largest commercial revenue base in the standalone vector-database category; Weaviate competes head-on in the open-source-first lane; Chroma competes on developer mindshare in the LangChain ecosystem; Milvus competes from the open-source community. Pricing pressure and feature-cadence requirements are real in a crowded competitive lane.
Berlin headquarter and EU-AI-Act exposure
Qdrant is Berlin-headquartered and increasingly used as the retrieval layer for production AI deployments inside enterprises in scope of the EU AI Act high-risk-AI obligations. The compliance burden is at the customer-deployment layer rather than at the Qdrant platform layer, but EU sovereign-cloud procurement dynamics may push EU customers toward self-host rather than Qdrant Cloud, capping Cloud revenue capture in the European enterprise channel.
Recent IM Coverage
- AI Tracker — methodology May 2026.
- AI Infrastructure sector overview May 2026.
Show recent press coverage of Qdrant
- Mar 2026 — Qdrant Raises $50 Million Series B to Define Composable Vector Search as Core Infrastructure for Production AI (BusinessWire)
- Mar 2026 — Qdrant raises $50M in funding to fuel vector database growth (TechTarget)
- Mar 2026 — We Raised $50M to Build Composable Vector Search as Core Infrastructure (Qdrant Blog)
- Mar 2026 — Qdrant Raises $50M Series B to Define Composable Vector Search as Core Infrastructure for Production AI (BigDATAwire / HPCwire)
- 2023 — Qdrant, an open source vector database startup, wants to help AI developers leverage unstructured data (TechCrunch)
Show the source register for the figures on this page
IM operates a primary-source-where-possible discipline. The figures above come from:
- Revenue: Qdrant is private and does not file public financials. The March 2026 Series B announcement framed commercial-customer growth without a published ARR figure. We decline-to-publish a precise ARR pending a primary disclosure.
- Usage — downloads and community: The March 2026 Series B announcement and BusinessWire coverage disclosed 250M+ downloads and 29k+ GitHub stars on the qdrant/qdrant repository. Named production-scale customers include Tripadvisor, HubSpot, OpenTable, Bazaarvoice, Canva, Roche and Bosch.
- Headcount: Qdrant does not publicly disclose precise headcount in a formal filing. We decline-to-publish a precise figure pending a primary disclosure and reference the Qdrant about page and careers page as the canonical entry points.
- Funding to date: Cumulative external capital approximately $87.8M through the March 2026 $50M Series B led by AVP with Bosch Ventures, Unusual Ventures, Spark Capital and 42CAP participating. Prior rounds: January 2024 $28M Series A led by Spark Capital with Unusual Ventures and 42CAP continuing; 2023 seed led by Unusual Ventures with 42CAP.
Methodology & Disclaimer
For metric definitions, source-tier hierarchy, and decline-to-publish rules, see the tracker methodology. Confidence dots (• green / • amber / • red) follow the same convention as the AI Tracker.
Spotted a figure you believe is wrong? Send corrections to info@informationmatters.net.
Information Matters Framework scores are the considered opinion of the IM team — human and AI — applied to publicly-available evidence under a disclosed methodology. They are not statements of fact about the companies scored and they are not investment advice.
