| Embed faster. Query faster. Go hybrid or multimodal. |
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AI |
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Vector search with built-in embeddings |
Generate embeddings inside the network of your Qdrant Cloud cluster. No separate model server or pipeline needed. |
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Bars growth |
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In-cluster inference, lower latency |
Generate embeddings and run search in-region on AWS, Azure, or GCP (US only). No external hops, no extra egress. Ideal for real-time apps that can’t afford delays or data transfer overhead. |
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Cloud data |
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Supports Dense, Sparse & Image Models |
Build vector search the way you need. Use dense models like all-MiniLM-L6-v2 for fast semantic match, sparse models like splade-pp-en-v1 or bm25 for keyword recall, or CLIP-style models for image and text. Need Hybrid and/or multimodal search? Covered. |
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