Native Hybrid Search (Dense + Sparse) for Better Relevance

Qdrant allows you to blend keyword and vector search in a single query, using dense or sparse vectors. It supports algorithms like BM25, SPLADE++, and miniCOIL, giving you the flexibility to handle various types of queries and data. This is a significant advantage over pure vector databases that struggle with exact keyword matches.

Business outcome: Delivers more accurate and relevant search results by understanding both semantic meaning and exact terms.

Expansive Metadata Filters for Precise Queries

Store metadata in JSON and use advanced filters such as nested, text, geo, and has_vector to refine search results. This allows for complex, multi-dimensional queries that go beyond simple vector similarity, making the retrieval layer more expressive and controllable.

Business outcome: Enables highly targeted retrieval, improving the precision of AI features and user-facing search.

One-Stage Filtering for High Recall and Low Latency

Filters are applied during the HNSW (Hierarchical Navigable Small World) graph traversal itself, not before or after. This efficient, one-stage filtering process ensures high recall with low latency, even under complex conditions with many filters applied.

Business outcome: Maintains fast response times for end-users while handling complex queries, ensuring a smooth experience.

Full-Spectrum Reranking for Smarter Results

Infuse business logic with score boosting, achieve token-level precision with late interaction models like ColBERT, and diversify results with Maximum Marginal Relevance (MMR). This allows for fine-tuning the final output to meet specific business needs, such as promoting certain products or ensuring diverse content.

Business outcome: Provides a superior user experience by delivering more relevant and diversified results, increasing engagement.

Memory-Efficient Storage with Advanced Quantization

Store billions of vectors with a minimal memory footprint using an optimized storage architecture. Techniques like asymmetric, scalar, and binary quantization can reduce memory usage by up to 64x while maintaining search quality, significantly lowering infrastructure costs.

Business outcome: Reduces cloud and hardware costs, making it feasible to scale AI retrieval to very large datasets.

Deploy Anywhere at Enterprise Scale

Choose from a fully managed cloud (Qdrant Cloud), a hybrid cloud that brings your own Kubernetes, a private cloud for air-gapped compliance, or an edge solution for low-latency. This flexibility allows you to align your infrastructure with security, compliance, and performance requirements.

Business outcome: Offers complete data control and flexibility, allowing businesses to scale securely without compromising on compliance.

• Built for PerformanceEngineered entirely in Rust with SIMD and a custom storage engine (Gridstore), ensuring high speed and scalability.
• Real-Time IndexingIndex new data instantly without rebuilding the entire index, making vectors searchable the moment they are added.
• Deployment FlexibilityRun on-prem, in the cloud, or at the edge, with options for fully managed, hybrid, or private cloud setups.
• Enterprise-Grade SecurityOffers SOC2 and HIPAA compliance, SSO (SAML/OIDC), and granular RBAC for secure enterprise deployments.

• Operational OverheadSelf-hosting Qdrant requires engineering resources to manage, monitor, and scale the database infrastructure.
• Complexity for Simple NeedsFor basic search needs or small datasets, a vector database may be overkill compared to simpler, integrated solutions.
• Pricing ModelCloud pricing is usage-based, which can be unpredictable for businesses with rapidly growing data volumes if not carefully monitored.
• Professional RealityThe main challenge is not the software itself, but the expertise required to tune HNSW parameters and optimize retrieval quality for your specific data and use case.

Retrieval-Augmented Generation (RAG)

Qdrant provides the high-performance retrieval layer needed to ground large language models with accurate, up-to-date company data. This is essential for building reliable AI assistants and chatbots that don't hallucinate. It is a core component in the modern AI stack for startups.

Semantic Product Search

E-commerce businesses can use Qdrant to power search that understands user intent, not just keywords. By indexing product descriptions and images as vectors, customers can find what they are looking for even if they don't use the exact product name.

AI Agent Memory

For AI agents that need to maintain context across long conversations, Qdrant can serve as a long-term memory store. This allows agents to access past interactions and provide more personalized and coherent responses.

Real-Time Recommendations

Qdrant's low-latency search and filtering make it ideal for powering recommendation engines. By analyzing user behavior and item embeddings, businesses can deliver personalized suggestions in real-time, driving engagement and revenue.

Choose your deployment: sign up for a free Qdrant Cloud cluster or install the open-source version locally.

Create a collection and define your vector size and distance metric (e.g., cosine, dot product).

Upload your data by generating embeddings with your preferred model and using the REST or gRPC API to upsert points.

Build a search query using the API, experimenting with filters and hybrid search to refine your results.