Qdrant is a high-performance, open-source vector search engine built in Rust for maximum efficiency. Its HNSW indexing with quantization delivers the best price-performance ratio among vector databases — 4x faster queries and 30x less memory than alternatives at scale. For...
ZTABS builds ai-powered search with Qdrant — delivering production-grade solutions backed by 500+ projects and 10+ years of experience. Qdrant is a high-performance, open-source vector search engine built in Rust for maximum efficiency. Its HNSW indexing with quantization delivers the best price-performance ratio among vector databases — 4x faster queries and 30x less memory than alternatives at scale. Get a free consultation →
500+
Projects Delivered
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Client Rating
10+
Years Experience
Qdrant is a proven choice for ai-powered search. Our team has delivered hundreds of ai-powered search projects with Qdrant, and the results speak for themselves.
Qdrant is a high-performance, open-source vector search engine built in Rust for maximum efficiency. Its HNSW indexing with quantization delivers the best price-performance ratio among vector databases — 4x faster queries and 30x less memory than alternatives at scale. For AI-powered search applications where latency and cost matter (e-commerce product search, content discovery, code search), Qdrant provides sub-10ms search across millions of vectors. Self-hosted deployment keeps data on your infrastructure, while Qdrant Cloud offers managed convenience.
Rust-native engine with scalar/product quantization uses 30x less memory than alternatives. Run billion-vector workloads on modest hardware.
Optimized HNSW indexing delivers single-digit millisecond latency at million-vector scale. Perfect for real-time search and autocomplete.
Combine vector similarity with complex payload filters in a single query without performance degradation. AND/OR/NOT conditions on any field.
Full-featured open-source deployment. No usage limits, no data sent externally. Qdrant Cloud available for managed infrastructure.
Building ai-powered search with Qdrant?
Our team has delivered hundreds of Qdrant projects. Talk to a senior engineer today.
Schedule a CallEnable scalar quantization from the start for production workloads. It reduces memory usage by 4x with less than 1% accuracy loss — the best optimization for cost-sensitive deployments.
Qdrant has become the go-to choice for ai-powered search because it balances developer productivity with production performance. The ecosystem maturity means fewer custom solutions and faster time-to-market.
| Layer | Tool |
|---|---|
| Vector Engine | Qdrant |
| Embeddings | OpenAI / Sentence-Transformers |
| Framework | LangChain / LlamaIndex / custom |
| Backend | Python / Rust / Node.js |
| Deployment | Docker / Kubernetes / Qdrant Cloud |
| Monitoring | Prometheus / Grafana |
A Qdrant search system starts by defining a collection with vector dimensions matching your embedding model. Products, articles, or code snippets are embedded and uploaded with rich payload metadata (price, category, language, timestamp). At query time, the search request combines a query vector with payload filters — "find products similar to this image, priced under $100, in the electronics category, rated 4+ stars." Qdrant evaluates both conditions simultaneously without post-filtering, maintaining speed.
For production, distributed mode shards collections across nodes for horizontal scaling. Collection aliases enable blue-green deployments — reindex into a new collection and swap the alias for zero-downtime updates. Snapshot-based backups protect against data loss.
Our senior Qdrant engineers have delivered 500+ projects. Get a free consultation with a technical architect.