Qdrant Cloud Inference

Qdrant powers Fillr’s AI search and matching capabilities. The vector search performance is excellent, and the API is straightforward, making AI features seamless to integrate.

Thanks to ElevenLabs for the startup grant, helping Sonura grow!

Qdrant’s performance and cloud inference options are solid. It feels reliable for production workloads across modalities.

Vector database enabling fast semantic search and retrieval of coding memories.

Yes

Powers Cartify’s vector search and similarity matching, enabling fast and accurate product recognition. It helps our AI quickly find similar products, enhancing recommendations, inventory insights, and real-time detection performance.

Open-source vector database that powers every semantic search and similarity check inside Invezgo. We feed millions of IDX filings, news snippets and price charts into Qdrant’s lightning-fast HNSW index; the AI then surfaces the most relevant tickers in <30 ms, letting us give investors “why this stock matters” context in plain Bahasa. Without Qdrant, we’d still be waiting on slow cosine scans—now our users get answers before their coffee gets cold.

I loved how easy Qdrant's interface and setup was in order to start leveraging them for my VectorDB. Their local testing support is also superb allowing for quick iteration.

We chose Qdrant because it’s fast, reliable, and super easy to integrate with our RAG pipeline. It gives us great performance even with large-scale scientific embeddings, and the filtering capabilities are a big plus compared to other vector databases. Also, the open-source community around Qdrant is active and helpful, which made a huge difference during development.

We use Qdrant together with FastAPI and the CLIP model to power our card image search and similarity features in TCGHi. Qdrant stood out because of its blazing-fast performance, simple API, and seamless integration with our Python-based stack.

Thanks to Qdrant for providing a powerful vector database that forms the backbone of our AI solution. Their open-source vector similarity search engine, built with Rust for unmatched performance and reliability, has been crucial for Batyr.assist's semantic search capabilities.

The best open source vector database for RAG workflows, super easy to get started with and integrate with other tools like n8n. It works perfectly for our small AI assistant that lets you find deals based on your specific use case.

Qdrant has transformed our vector search implementation with performance that other solutions can't match. While alternatives like Pinecone are solid, Qdrant's open-source nature gives us complete control over our deployment and data residency. Its filtering capabilities are more intuitive and expressive, making complex queries simple to implement. The clustering features for high-dimensional data analysis have uncovered insights we wouldn't have found otherwise. For mission-critical vector search, Qdrant delivers reliability without compromise.