Qdrant is an open-source vector database built in Rust for AI search and retrieval-augmented generation. Compare pricing, features, pros/cons, and FAQs.
Category
Databases
Pricing
Open-core: free self-hosted/open-source engine plus usage-based Qdrant Cloud tiers (Free, Standard pay-as-you-go, Premium minimum-spend) and custom Hybrid/Private Cloud and Enterprise plans, from Free (open source and free cloud tier); paid Standard cloud clusters are billed hourly based on resources used
Verified
Not yet
Last updated
July 19, 2026
Founded
2021
Headquarters
Berlin, Germany
Web AppAPIOpen SourceAIFreemium
Overview
Qdrant is an open-source vector database built in Rust, founded in 2021 in Berlin by CEO Andre Zayarni and CTO Andrey Vasnetsov after Vasnetsov built a production-ready vector search engine from scratch for an earlier project. The open-source release drew strong developer interest, leading the founders to formalize Qdrant as a company.
The company has since raised roughly $87.5 million across multiple funding rounds, including a $28 million Series A in January 2024 and a $50 million Series B in March 2026, and has grown to more than 100 employees across 20-plus countries.
Key Features
Qdrant provides fast approximate nearest-neighbor vector search with support for payload filtering, hybrid dense-and-sparse search, and quantization options for reducing memory footprint at scale.
Client libraries are available in Python, JavaScript/TypeScript, Go, and Rust, and the engine can be deployed self-hosted, via Qdrant Cloud, or through Hybrid and Private Cloud options for data-residency-sensitive workloads.
Pricing
The core Qdrant engine is free and open source for self-hosted use. Qdrant Cloud offers a permanently free tier for testing, a usage-based Standard tier billed hourly, a minimum-spend Premium tier with enhanced SLA and support, and custom-quoted Hybrid Cloud and Private Cloud options.
Exact Standard and Premium tier costs depend on chosen resources, cloud provider, and region, and are best estimated using Qdrant's online pricing calculator or by contacting sales for larger deployments.
Key Features
High-performance vector search engine — Built in Rust for fast, memory-efficient approximate nearest-neighbor search over large-scale vector datasets.
Payload filtering — Combine vector similarity search with structured metadata filters in a single query for more precise retrieval.
Hybrid dense and sparse search — Supports combining dense vector embeddings with sparse keyword-style search for improved retrieval relevance.
Quantization for memory efficiency — Reduces memory footprint through scalar, product, and binary quantization options for cost-effective scaling.
Multi-language client libraries — Official clients for Python, JavaScript/TypeScript, Go, and Rust integrate Qdrant into most modern application stacks.
Flexible deployment options — Deploy self-hosted, on Qdrant Cloud, or via Hybrid/Private Cloud for organizations with data residency or compliance requirements.
High availability and disaster recovery — Standard and Premium cloud tiers support replication, backups, and horizontal/vertical scaling for production workloads.
Free cloud inference credits — Selected embedding models offer free cloud inference credits to reduce the cost of generating vectors for indexing.
Pros & Cons
Pros
Free, open-source core engine with no licensing fees for self-hosting
Built in Rust for strong performance and memory efficiency at scale
Multiple deployment options, including Hybrid and Private Cloud for compliance-sensitive workloads
Well-funded and growing quickly, with backing from established venture investors
Multi-language client support covers most modern AI development stacks
Free cloud tier makes it easy to prototype before committing to paid infrastructure
Cons
Standard and Premium cloud pricing is usage-based and not fully transparent without using the calculator or contacting sales
Premium tier requires a minimum spend commitment rather than flat predictable pricing
Free cloud tier is limited to a single small node, unsuitable for production workloads
Competition from well-funded alternatives like Pinecone, Weaviate, and Milvus is intense
Self-hosting for full control requires infrastructure and ops expertise
Company is still relatively young (founded 2021), so long-term enterprise track record is shorter than some legacy database vendors
Pricing
Free (Open Source) Free N/A
Qdrant Cloud Free Tier $0 N/A
Standard Usage-based, billed hourly Hourly usage, monthly invoicing
Premium Custom minimum spend Custom
Hybrid/Private Cloud Custom quote Custom
Frequently Asked Questions
Is Qdrant free to use?
Yes, the core Qdrant engine is free and open source for self-hosted deployments, and Qdrant Cloud also offers a permanently free tier for testing and prototypes.
How much does Qdrant Cloud cost?
Qdrant Cloud's Standard tier is usage-based and billed hourly according to the resources selected, while Premium requires a minimum spend commitment; exact costs depend on configuration and are best estimated with Qdrant's online pricing calculator.
Who founded Qdrant?
Qdrant was founded in 2021 in Berlin, Germany, by CEO Andre Zayarni and CTO Andrey Vasnetsov.
What programming language is Qdrant written in?
Qdrant's core engine is written in Rust, chosen for performance and memory safety, with official client libraries available in Python, JavaScript/TypeScript, Go, and Rust.
How much funding has Qdrant raised?
Qdrant has raised approximately $87.5 million total, including a $28 million Series A in January 2024 and a $50 million Series B in March 2026.
What is Qdrant commonly used for?
Qdrant is commonly used for retrieval-augmented generation (RAG), semantic search, recommendation systems, and other AI applications that require fast similarity search over vector embeddings.
What are Qdrant's main competitors?
Qdrant is commonly compared to other vector database providers including Pinecone, Weaviate, Milvus, and Chroma.
Can Qdrant be deployed for data residency or compliance requirements?
Yes, Qdrant offers Hybrid Cloud and Private Cloud deployment options specifically designed for organizations with data residency, regulatory, or custom SLA requirements.