Qdrant Vector Search Engine
Choosing a Search Database? Run Hybrid Search Across Keywords and Vectors
Combine keyword search, vector search, and filters in one query and get one ranked list back. Start free on Qdrant Cloud with no credit card.
Hybrid search
Rank Keyword and Vector Matches in One Query
Some queries need exact terms, like a part number or a legal phrase. Others need meaning. Run both in one hybrid search query and get a single ranked list back.
Index keywords and meaning in one collection.
Score exact terms with BM25 on the text Qdrant already stores, or with sparse vectors, next to your dense vectors.
Merge both rankings in one request.
Fuse the keyword and vector rankings with Reciprocal Rank Fusion (RRF) and put documents that rank well on both lists first.
Filter inside the search.
Qdrant applies payload filters while it searches, so results stay accurate when filters are strict.
Himalayas runs semantic and keyword retrieval in one query with filters for location, time zone, and employment type: 25M+ requests a month at about 30 ms median inside Qdrant. Read the Himalayas case studyStart free with Qdrant Cloud
Search API
Run Every Query Type Through One Search API
Run dense, sparse, hybrid, and multi-stage queries through one Query API. Call it over REST or gRPC, or from the official Python, TypeScript, Rust, Go, .NET, or Java client. Use it as your search results API and get one ranked list per query.
Rescore candidates in a second stage.
Fetch them with a fast first query, then rescore with a more accurate one in the same request.
Choose your fusion method.
Fuse prefetch results with Reciprocal Rank Fusion (RRF) or Distribution-Based Score Fusion (DBSF).
GoPerfect runs hybrid search over 200M+ profiles stored as multivectors on Qdrant Cloud. Match accuracy rose from the recruiting industry's 30% baseline to 99.993% in internal benchmarks. Read the GoPerfect case study
High-performance search at scale
From Prototype to Billions of Vectors
Qdrant is a vector search engine built entirely in-house, from the search algorithms to the storage engine. It's written in Rust.
Grow to billions of vectors.
Qdrant runs billion-scale workloads with predictable, low-latency search.
Control memory and cost.
Qdrant stores vectors as 32-bit floats, so memory grows with dimensionality. Turn on quantization to shrink that footprint.
Deploy where your data lives.
Start on Qdrant Cloud, or run the same engine in your own infrastructure.
Bazaarvoice keeps billions of vectors on Qdrant Hybrid Cloud in its own VPC: about 100x less storage than in PostgreSQL, with sub-100 ms queries at about 98% nearest-neighbor accuracy. Read the Bazaarvoice case studyStart free with Qdrant Cloud
Results
Measured Gains in Production
Tripadvisor
over a billion reviews and images indexed on Qdrant Cloud, and users of its AI trip planning features show 2 to 3x more revenue.
Read the Tripadvisor case studyLyzr
query latency of 20 to 50 ms, over 90% faster than before, with hundreds of concurrent agents sending over 1,000 queries a minute.
Read the Lyzr case study
My AskAI
about 75% of support requests deflected by AI agents running RAG and hybrid search on Qdrant Cloud.
Read the My AskAI case studyGetting started
From Collection to Live Search
Create a collection.
Start free on Qdrant Cloud or run Qdrant in a single Docker container. Then create a collection for your data.
Ingest your data.
Store each item as a point with its vectors and a JSON payload for filtering.
Query your search API.
Run a semantic, keyword, or hybrid query.
Common Questions
How do I get started with Qdrant Cloud?
How is a vector search engine different from a traditional search engine?
Can I use Qdrant as a search results API?
