Qdrant Cloud for RAG
Retrieval Augmented Generation That Finds the Right Context
Qdrant, the vector search engine, filters and ranks your LLM's context in a single query. Start free on Qdrant Cloud with no credit card.
AI retrieval
Better Answers Start With Better Retrieval
To turn a promising RAG demo into reliable answers, start with retrieval. When the search returns the right chunks, the LLM has what it needs to answer well. Qdrant gives the retrieval step of your RAG pipeline architecture the controls to get context right.
Filter on metadata during the search.
Qdrant applies payload filters while it traverses the HNSW graph, so every result already matches the user, product, or date range you scoped. Create a payload index on each field you filter on to keep filtered search fast.
Match keywords and meaning in one call.
Run dense and sparse retrieval in one Query API request and fuse the two rankings with Reciprocal Rank Fusion (RRF).
Rank by your business logic.
Boost results by recency, document section, or any payload field with score formulas.
Pathwork runs retrieval augmented generation for insurance underwriting on Qdrant Cloud. Query latency fell from 9 seconds to 2, and mean squared error nearly halved, from 3.5 to 1.8. Read the Pathwork case studyStart free with Qdrant Cloud
Managed by Qdrant Cloud
Managed RAG Infrastructure That Grows With Your App
Qdrant Cloud runs the cluster in your RAG infrastructure, so your team can spend its time on retrieval quality.
Start free and scale from the Console.
Begin on a free cluster, then scale up or out from the cluster detail page. Qdrant Cloud rebalances data automatically when you add nodes.
Keep your RAG app answering during upgrades.
Qdrant Cloud runs version upgrades as rolling, zero-downtime updates when your multi-node cluster's collections have a replication factor of 2 or more.
Control who reaches which data.
Use Cloud RBAC and API keys with read or write access to individual collections. Add SSO on a Premium plan.
Sapu indexed 28 million PubMed abstracts in a single collection on Qdrant Cloud, and runs 10 million parallel queries per research workflow. Read the Sapu case study
Frameworks and tenants
Keep the RAG Framework You Already Use
Qdrant integrates with more than 40 AI agent and RAG frameworks, including LangChain, LlamaIndex, Haystack, CrewAI, and AutoGen. Point your retriever at a Qdrant collection and keep the rest of your chain.
Serve many customers from one collection.
Partition a single collection by payload so each tenant's documents stay separate. Bayer runs more than 10,000 knowledge bases this way.
Keep context fresh.
Upsert new chunks while the collection keeps serving queries, so answers draw on the latest documents.
Getting started
Set Up RAG on Qdrant Cloud
Create a free cluster.
Sign up for Qdrant Cloud with no credit card and create a free cluster in the Console.
Load your chunks.
Split your documents with the chunking strategy for RAG that fits them. Embed the chunks with the model you choose, and store each one with the payload fields you want to filter on.
Connect your framework.
Point LangChain, LlamaIndex, or Haystack at the collection and retrieve context for each prompt.
Common Questions
How do I get started with Qdrant Cloud?
Where does Qdrant fit in retrieval augmented generation?
Do I need a separate collection for each customer?
