Vector databases · Qdrant Solutions GmbH

Qdrant

Open-source vector database written in Rust, focused on filtered similarity search with a free managed cloud tier.

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Qdrant is an open-source vector database written in Rust that stores high-dimensional embeddings alongside structured payload data, and specializes in combining approximate nearest-neighbor search with rich payload filtering so queries can narrow results by both semantic similarity and exact attributes (price range, category, permissions) in one pass. It exposes gRPC and REST APIs and client libraries for major languages, and supports quantization options to reduce memory footprint at scale. Qdrant can be self-hosted via Docker or Kubernetes with no licensing fee, or run as Qdrant Cloud, the managed service with a free forever tier for testing plus usage-based Standard and minimum-spend Premium tiers, and Hybrid/Private Cloud options for regulated environments. It is often chosen for its performance-per-resource efficiency compared to other open-source vector databases.

At a glance

Vendor Qdrant Solutions GmbH
Pricing model Free tier + paid plans
Free tier Yes
Deployment Cloud, Self-hosted
Open source Yes (Apache-2.0)
Best for Teams needing efficient, filterable vector search with the option to self-host at no license cost.

Pricing

Self-hosted is free; Qdrant Cloud has a free-forever tier, usage-based Standard pricing, and minimum-spend Premium and Hybrid/Private Cloud options.

Plan Price Notes
Free Free forever Single-node cluster, 0.5 vCPU, 1GB RAM, 4GB disk; for testing and prototypes
Standard Usage-based (rates via calculator) Dedicated resources, high availability, 99.5% uptime SLA
Premium Minimum spend required (rates via calculator) SSO, private VPC links, 99.9% uptime SLA

Prices read from the vendor's own page on September 21, 2026. Vendors change prices; check the source before you budget.

Features

  • Approximate nearest-neighbor search with payload filtering
  • Rust-based engine for high performance and low memory use
  • Vector quantization for reduced memory footprint
  • gRPC and REST APIs
  • Self-hosted or managed cloud, including hybrid/private cloud
  • Sharding and replication for horizontal scale

Integrations

Profile last reviewed September 21, 2026

Head to head

Alternatives

Pinecone Fully managed, cloud-only vector database for similarity search and retrieval-augmented generation at scale. Weaviate Open-source vector database with built-in hybrid (vector + keyword) search, available self-hosted or as managed cloud. Milvus Open-source vector database built for billion-scale similarity search, governed under the Linux Foundation. Chroma Open-source embedding database designed for simple local-first development, with a managed serverless cloud tier.

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Terms to know

Machine learning Building models that learn patterns from data to make predictions or decisions, rather than following hand-coded rules. Semantic search Search that matches results to a query's meaning rather than its exact keywords, using vector similarity between embeddings. Vector database A database optimized to store numeric embeddings and quickly find the ones most similar to a given query vector.

Related guides

How to choose a vector database Pick a vector database by deployment model, scale and search type, not by benchmark charts — most teams' real constraint is who runs the cluster.