01
Self-hosted Kubernetes clusters
Running Qdrant in your own VPC so proprietary embeddings stay inside your security perimeter.
Technologies — Databases & storage
Qdrant vector search engineering & self-hosting
We deploy and tune high-throughput Qdrant vector databases, in the cloud or self-hosted, so embeddings stay where your data policies say they should.
Core capabilities
Why we build with Qdrant
02
Quantization & HNSW tuning
Scalar quantization stores vectors in a quarter of the memory, usually with little loss of recall, measured on your data before it ships.
03
Payload-based filtering
Rich JSON payload filtering integrated directly into the Rust vector search engine.
Use cases
Where Qdrant fits
On-premises healthcare AI
Vector retrieval for HIPAA-regulated data, running entirely inside your own infrastructure.
High-volume recommendation engines
Real-time similarity matching for recommendations at high query volume.
How we staff it
Qdrant engineers you interview first
Seniority and experience are agreed in the proposal, and you interview every engineer before they start.
Working-hours overlap is agreed for each engagement and written into the statement of work — the shared window, who shifts hours, and how handoffs work outside it.
Technical FAQs
Frequently asked engineering questions
Qdrant is open source, written in Rust, and can run on your own infrastructure, which helps when data-residency rules require it or when you want to control hosting costs directly. Pinecone is fully managed, which suits teams that would rather not operate a database.
As a rule of thumb, 10 million 1536-dimension vectors take about 61 GB as float32. With scalar quantization in RAM and the originals on disk, the in-memory part drops to about 15 GB plus the HNSW graph, so we size nodes with headroom above that and confirm recall on your data.
Ecosystem
Related technologies
Planning a Qdrant project?
Tell us about your architecture, backlog and team. We'll reply within one business day with an honest read on whether we can help.