Qwen 3.5 Embedding models support 100+ languages and rank #1 on the MTEB multilingual leaderboard, making them well-suited for enterprise document search systems that span multiple languages and regional markets.
For enterprises operating across multiple countries, building separate retrieval pipelines per language creates operational complexity: multiple indexes, multiple embedding models, and multiple query interfaces. Qwen 3.5 embeddings unify this into a single vector space that handles cross-lingual retrieval — a query in English can retrieve relevant documents written in French, Japanese, or Spanish without translation.
Zilliz Cloud hosts these multilingual embeddings at enterprise scale, serving concurrent queries from teams across regions with consistent sub-100ms latency. Data governance features ensure documents stay in the appropriate regional deployment. For global enterprises with compliance requirements (e.g., GDPR for EU data, data localization laws in other regions), Zilliz Cloud's region-specific deployments ensure Qwen 3.5 embeddings are processed and stored in compliant locations.