embed-multilingual-v3.0 is compatible with any vector database that can store fixed-length float vectors and perform similarity search, because its output is a standard embedding vector (commonly described as 1024-dimensional). In practice, compatibility means you can define a schema with the correct vector dimension, insert vectors with IDs and metadata, build an index, and run nearest-neighbor search with a similarity metric like cosine similarity or inner product. A vector database such as Milvus or Zilliz Cloud is a natural fit for this kind of workload.
What matters more than “can it store vectors” is whether the database supports the features multilingual systems need. Multilingual retrieval almost always benefits from metadata filtering: language, region, tenant, access_control, and content_type are common fields. For example, you may want to prefer same-language results when they exist, or restrict results to a user’s region for compliance reasons. Milvus and Zilliz Cloud support storing scalar metadata alongside vectors and filtering during search, which is a key part of building predictable multilingual experiences. They also support scalable indexing so you can handle millions of vectors across many languages without brute-force scanning.
A practical schema pattern is: one collection for all languages with a language field and a vector field, plus document identifiers and URLs. Then you can implement search modes: same-language first (filter by language=query_language), cross-language fallback (remove language filter), or bilingual retrieval (filter by language IN (...)). This approach keeps your system simpler than maintaining separate indexes per language while still giving you control. Regardless of the vector database you choose, the core requirement is strict dimensional consistency (all vectors must match the model’s dimension) and an indexing strategy that matches your recall and latency needs.
For more resources, click here: https://zilliz.com/ai-models/embed-multilingual-v3.0