Google's Gemini Embedding 2 arrives with native multimodal support to cut costs and speed up your enterprise data stack

Yesterday amid a flurry of enterprise AI product updates, Google announced arguably its most significant one for enterprise customers: the public preview availability of Gemini Embedding 2, its new embeddings model — a significant evolution in how machines represent and retrieve information across different media types.

While previous embedding models were largely restricted to text, this new model natively integrates text, images, video, audio, and documents into a single numerical space — reducing latency by as much as 70% for some customers and reducing total cost for enterprises who use AI models powered by their own data to complete business tasks.

Who needs and uses an embedding model?

For those who have encountered the term "embeddings" in AI discussions but find it abstract, a useful analogy is that of a universal library.

In a traditional library, books are organized by metadata: author, title, or genre. In the "embedding space" of an AI ...