Qwen3-VL-Embedding generates unified embeddings for text, images, and videos, which you store and search in Zilliz Cloud for enterprise-grade multimodal retrieval at scale.
The workflow is straightforward: vectorize your multimodal content using Qwen3-VL-Embedding, ingest the resulting vectors into Zilliz Cloud, then perform similarity searches across mixed-media queries. Qwen3-VL-Embedding's support for 100+ languages and 32K context window accommodates complex documents and long-form content. Zilliz Cloud's distributed architecture handles billions of multimodal vectors with sub-millisecond query latency.
For enterprises, Zilliz Cloud offers compliance-friendly deployment with HIPAA-eligible regions and SOC 2 Type II certification. You control where your visual data resides, avoiding third-party embedding APIs. Milvus community content shows how to combine Qwen3-VL-Embedding with the Qwen3-Reranker for two-stage multimodal retrieval, delivering search relevance that exceeds traditional single-stage systems.