Category · Vector database for AI
Qdrant
Dedicated vector database for teams with their own AI pipeline
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What Qdrant is, in plain words
Qdrant is a database specialized in storing "vectors" — numerical representations of text, images or audio. That format lets you search by meaning rather than by exact word match. If you already have an AI pipeline that produces embeddings, Qdrant is where they live and where the final semantic search happens.
For an AI agent
For an AI agent doing RAG (answering grounded in your documents), Qdrant is its long-term memory: it pulls the relevant passages back in milliseconds.
What you can do with it
RAG over your own documents
Real-world use case
Indexes contracts, manuals and wikis and retrieves the right passage when the agent needs it.
Semantic search
Real-world use case
Internal site search that understands meaning — finds "monthly budget" even when the user types "how much I spent".
Recommendation
Real-world use case
Recommends similar product/content based on behavior and description, not just tags.
Beta testers get special pricing
Want early access and a say in shaping the product? Get in touch. Beta testers receive special pricing for the whole beta period — no fixed % promise, just genuinely better pricing.
We'll let you know at launch
No spam. Just one email on the day it opens.
Frequently asked questions
I already have an AI pipeline. Why not use your RAG Vector DB?
RAG Vector DB is the full stack (ingest + index + LLM). Plain Qdrant is just the vector storage — for teams who already own the pipeline and only need the database.
Does it support REST and gRPC?
Yes, both. Full compatibility with the official Python, JavaScript, Rust and Go clients.
How is isolation handled?
Each customer has an isolated collection with scoped JWT. No data leaks across tenants.
Does it work with LangChain and LlamaIndex?
Yes. Qdrant is a native provider in both libraries — just point at the URL and the API key.