Overview

Qdrant is a vector similarity search engine for storing, searching, and managing vectors alongside additional payload data. Its API makes the vector store accessible to applications, while JSON metadata on each point can help narrow search results with filters. Qdrant describes semantic search and recommendation systems as use cases for vector databases.

The product supports dense, sparse, and multivector configurations, as well as hybrid retrieval that combines semantic and lexical search. It can run locally from a Docker image or through Qdrant Cloud. Qdrant is made by Qdrant, founded in 2021 and headquartered in Berlin, Germany.

For teams comparing where a vector database fits in a broader stack, see Vector database hosting, Search Databases, and Database Software.

Key features

Vectors, payloads, and retrieval

Qdrant points pair vectors with JSON payload metadata. That structure supports metadata-based filtering of results, and the product offers dense, sparse, and multivector options. Its index types include HNSW, sparse vector index, payload index, and filterable HNSW. The maximum supported vector size is 65,535 dimensions.

Hybrid retrieval combines semantic and lexical search, useful when an application needs both vector similarity and keyword-oriented signals. Qdrant also identifies semantic search and recommendation as relevant vector database applications.

APIs, clients, and integrations

REST and gRPC APIs provide access to Qdrant. Official client libraries are available for Python, JavaScript/TypeScript, Rust, Go, .NET, and Java. Listed integrations include LangChain, LlamaIndex, Airbyte, Unstructured, DocArray, and AutoGen.

Security and deployment choices

Qdrant can be self-hosted or run through Qdrant Cloud. Cloud includes built-in TLS and encryption at rest, and its security features are enabled by default. Self-hosted open-source deployments do not enable authentication or encryption by default, so operators need to configure those controls themselves.

Qdrant says documentation on SOC 2 Type 2 and HIPAA compliance is available through its Trust Center. The cloud offering also includes Hybrid Cloud, which runs on customer infrastructure and is managed through Qdrant Cloud, and Private Cloud, described as a dedicated isolated deployment with custom SLAs and air-gapped deployment support.

Pricing

Qdrant uses a freemium model. The Free Tier costs 0.00 USD per free (billed Free forever). It includes a single-node cluster with 0.5 vCPU, 1GB RAM, and 4GB disk, plus free cloud inference with selected models. Qdrant positions this tier for testing and prototypes; the resource limits make it a constrained starting point rather than a stated production configuration.

Standard Tier and Premium Tier prices are not listed. Standard Tier has a 99.5% uptime SLA, while Premium Tier has a 99.9% uptime SLA. Hybrid Cloud and Private Cloud also have no listed prices. Paid support options are described as either 10 hours per day during business hours or 24/7 coverage; community support is Discord-only.

Platforms

Qdrant is listed for API, Linux, macOS, Windows, web, and self-hosted use. Local deployment is available through a Docker image, while Qdrant Cloud offers a managed cloud option. Hybrid Cloud provides a managed control plane while keeping data in the customer's network.

Who it's for

Qdrant is relevant to teams building applications that need vector similarity search with metadata filtering, including semantic search and recommendation systems. Its REST and gRPC APIs, range of official clients, and integrations may suit stacks that already use those languages and frameworks. Dense, sparse, and multivector support plus hybrid retrieval offer several retrieval configurations within the same product.

Teams weighing deployment responsibility should distinguish self-hosted operation from Qdrant Cloud: self-hosted deployments require customer setup for authentication and encryption, while Cloud enables security features by default. The free cloud tier can support small tests and prototypes, but its single node, 1GB RAM, and 4GB disk limits matter when estimating whether it can meet an application's needs.

Pros and cons

Pros: Dense, sparse, and multivector configurations; hybrid semantic and lexical retrieval; payload filtering; REST and gRPC APIs; and official clients across six language families.
Pros: Local Docker deployment, cloud deployment, and options for customer-infrastructure or isolated deployments.
Cons: The free cloud tier is limited to a single node with 1GB RAM and 4GB disk.
Cons: Self-hosted open-source deployments do not enable authentication or encryption by default, so teams must configure these controls.
Cons: Standard, Premium, Hybrid Cloud, and Private Cloud prices are not listed here.

Alternatives

Other products to compare include Chroma, Weaviate, Pinecone, Zilliz Cloud, Upstash Vector, and Cloudflare Vectorize. For broader database comparisons, see Embedded Databases.

Verdict

Qdrant presents a flexible vector search option for builders who want to choose between local, self-hosted, and managed-cloud deployment. Its combination of payload filtering, several vector configurations, hybrid retrieval, and broad client support gives teams multiple ways to fit it into an application stack. The main trade-offs are operational: self-hosted security needs deliberate configuration, the free cloud tier is modestly sized, and several paid-plan prices are not listed. Those constraints are worth resolving against workload and deployment requirements before choosing a tier.

	Datafari	Qdrant
Free plan	No	Yes
Free trial	No	No
Paid from	—	Free
Open source	No	No
Platforms	Web, Windows, Linux	api, Linux, macOS, self-hosted, Web, Windows
Free plan	Yes	Yes
Primary search mode	hybrid	—
Vector search	Yes	—
Faceted search	Yes	—
Deployment	both	both
Query interface	REST	—

Datafari vs Qdrant

Datafari

Qdrant