Qdrant funding, valuation and investors

Overview

Qdrant is a Berlin-headquartered vector database and similarity search engine built in Rust. Founded in 2021 by CEO Andre Zayarni and CTO Andrey Vasnetsov, the company offers an open-source engine and managed Qdrant Cloud for storing and querying high-dimensional embeddings with rich JSON payload filtering. Production AI teams use Qdrant for retrieval-augmented generation, recommendation systems, semantic search, and agent memory. The platform supports dense, sparse, and multi-vector hybrid search, quantization for memory efficiency, and on-prem, hybrid, and edge deployments valued by GDPR-conscious European customers. Qdrant raised a $50 million Series B in March 2026 led by AVP with Bosch Ventures, Unusual Ventures, Spark Capital, and 42CAP participating.

Why Qdrant is interesting

Vector search became table stakes for RAG, but most teams still under-benchmark recall and filtering on real payloads. Qdrant's open-core model, EU deployment options, and Spark-backed Series B make it a credible European alternative to U.S. vector SaaS incumbents.

Product & use cases

Qdrant stores vectors with JSON payloads and serves low-latency similarity search via REST, gRPC, and official client libraries. Teams deploy self-hosted clusters or Qdrant Cloud with horizontal scaling, strict-mode guardrails, GPU-accelerated indexing, and built-in cloud inference for embedding generation.

• RAG pipelines retrieving relevant document chunks for LLM applications
• Hybrid keyword + semantic search for e-commerce and content platforms
• Agent memory and long-context retrieval in production AI agents
• Recommendation and match-making systems over unstructured embeddings

Key facts

• Series B (Mar 2026): $50M led by AVP; Bosch Ventures, Unusual Ventures, Spark Capital, 42CAP
• Apache 2.0 open-source core plus Qdrant Cloud on AWS, GCP, and Azure
• Hybrid dense + sparse search, multivector retrieval, and filterable HNSW indexing
• SOC 2 and HIPAA compliance for enterprise AI deployments
• Founded 2021 in Berlin; co-founders Andre Zayarni and Andrey Vasnetsov

Funding history (newest first)

Investors in our directory

Funds linked from Qdrant's profile — open a fund page for stage focus and related deal articles.

Competitive landscape

Edge: Rust-native performance with filterable HNSW—search respects metadata filters in one stage rather than post-filtering—and flexible deployment from embedded edge to managed multi-cloud without vendor lock-in on the open core.

Vector databases commoditized at the demo level; winners need production-grade filtering, quantization, and deployment flexibility. Qdrant competes with U.S. SaaS leaders on developer experience while leaning into European data-sovereignty and open-source adoption.

• Pinecone direct

  Managed vector SaaS leader; Qdrant counters with open-source portability and hybrid/on-prem options.

• Weaviate direct

  Open-source vector DB with strong hybrid search; overlapping developer audience in RAG stacks.

• Milvus / Zilliz direct

  Scale-focused open-source vector platform; Qdrant emphasizes filtering ergonomics and Rust efficiency.

• PostgreSQL pgvector alternative

  Good enough for lighter workloads; Qdrant targets higher recall/latency demands at AI scale.

Notable stories

• CTO Andrey Vasnetsov published filterable HNSW research in 2019 that major vector vendors later adopted—Qdrant grew from that open-source work into a company (Microsoft DevBlog, 2024).
• CEO Andre Zayarni told TechCrunch that connecting LLMs to vector databases extends model "memory" with real-time unstructured data—the core RAG thesis driving the category.

Industries

Related funding articles

Venture Capital Tracker pieces that cover Qdrant's financing or category context.

• Qdrant's $50M Series B: Berlin's Open-Source AI Infrastructure Grows Up