Qdrant vs SingleStore: Vector Databases Comparison
Compare Qdrant and SingleStore side by side. Both are tools in the Vector Databases category.
Updated March 1, 2026
The short answer
Choose Qdrant if written in Rust for exceptional performance and memory safety.
Choose SingleStore if unified platform combining transactional, analytical, and vector workloads.
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Quick Comparison
| Category | Vector Databases | Vector Databases |
| Pricing | Open Source | — |
| Best For | Engineering teams who need a fast, self-hosted vector database with strong filtering | — |
| Website | qdrant.tech | singlestore.com |
| Key Features |
| — |
| Use Cases |
| — |
Pros & Cons: Qdrant vs SingleStore
- +Written in Rust for exceptional performance and memory safety
- +Advanced filtering with rich payload indexes during search
- +Built-in quantization reduces RAM usage by up to 97%
- +Flexible deployment: self-hosted, cloud, or hybrid
- +ACID-compliant operations with point-in-time consistency
- −Horizontal scaling features still evolving compared to mature systems
- −Higher learning curve than simpler alternatives like pgvector
- −Data ingestion hiccups possible with very large initial datasets
- −Smaller community and ecosystem compared to Pinecone
- +Unified platform combining transactional, analytical, and vector workloads
- +Co-located vector data with relational data for powerful SQL queries
- +Multiple vector index types (FLAT, IVF_FLAT, IVF_PQ, HNSW_FLAT, HNSW_PQ)
- +MySQL and MongoDB wire protocol compatibility for easy integration
- +Proven at scale with 100+ Fortune 500 customers
- −More complex than dedicated vector databases for pure vector search use cases
- −Pricing not transparently published
- −May have higher learning curve for teams unfamiliar with distributed SQL
- −Recent acquisition may lead to product changes
Pricing: Qdrant vs SingleStore
- · 1 GB storage
- · Community support
- · Single-node deployment
- · Scalable clusters
- · Multiple regions
- · Automatic backups
- · Deploy in your VPC
- · Managed control plane
- · Data sovereignty
- · Dedicated support
- · Custom SLAs
- · Advanced security
- · On-premise option
When to Choose Qdrant vs SingleStore
- High-throughput vector search
- Production RAG with complex filtering
- Self-hosted vector infrastructure
How to Choose a Vector Databases Tool
Key criteria to evaluate when comparing Vector Databases solutions:
About Qdrant
Qdrant is a high-performance open-source vector database written in Rust, optimized for speed and reliability. It supports advanced filtering with payload indexes, quantization for memory efficiency, and distributed deployments for horizontal scaling.
Qdrant offers both a self-hosted open-source version and a managed Qdrant Cloud service with free, hybrid cloud, and enterprise tiers. The Rust-based architecture provides memory safety without garbage collection overhead, leading to consistently low latency and high throughput.
Founded in 2021 and headquartered in Berlin, Germany, Qdrant has raised $37.8M in total funding including a $28M Series A led by Spark Capital in January 2024. The company is popular with teams that need production-grade vector search with fine-grained control over indexing and query parameters.
About SingleStore
SingleStore is a real-time, unified, distributed SQL database that combines transactional, analytical, and vector data workloads in a single platform. MySQL and MongoDB wire protocol-compatible, SingleStore enables organizations to scale from one to one million customers, handling SQL, JSON, full text, and vector workloads all in one unified platform. Unlike traditional vector databases, SingleStore stores vector data in relational tables alongside other types of data, allowing easy querying of extended metadata and other attributes with the full power of SQL. The system supports both semantic search using FLAT, IVF_FLAT, IVF_PQ, IVF_PQFS, HNSW_FLAT, and HNSW_PQ vector indexes, with dot product and Euclidean distance for similarity matching. Founded in 2011 and headquartered in San Francisco, SingleStore serves hundreds of customers including 100+ Fortune 500, Forbes Global 2000, and Inc. 5000 brands. The company was acquired by Vector Capital in September 2025.
What is Vector Databases?
Purpose-built databases for storing, indexing, and querying high-dimensional vector embeddings used in semantic search, RAG, and recommendation systems.
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