Yes, combining Nano Banana 2 with a vector database is a practical and increasingly common architecture for applications that need image retrieval, deduplication, or similarity-based recommendations. The typical workflow is: generate an image with Nano Banana 2, pass the resulting image through a vision encoder model (such as CLIP or a similar multimodal embedding model) to produce a dense float vector, and then store that vector in a vector database such as Zilliz Cloud alongside metadata about the image—its prompt, generation parameters, storage URL, and any application-specific tags. When a user later queries for visually similar images, you embed their query (whether it is a text description or a reference image) using the same encoder and run a nearest-neighbor search against the stored vectors.
Zilliz Cloud is a managed service built on Milvus that handles the infrastructure concerns of running a vector index at scale—index building, replication, and query serving—without requiring you to operate the underlying cluster yourself. For teams generating large volumes of images with Nano Banana 2, the managed service removes the operational overhead of keeping the vector index available and performant as the collection grows. You interact with Zilliz Cloud using the standard Milvus SDK, defining a collection schema that includes a vector field for the image embedding and additional scalar fields for the metadata you want to filter on during retrieval. Searches can be filtered by metadata—for example, returning only images generated with a specific aspect ratio or within a certain date range—in addition to vector similarity.
This architecture is particularly useful for applications that generate many images over time and need to avoid duplicating similar outputs, surface previously generated assets to users, or build a searchable creative library. The embedding step adds latency and compute cost per image, so it is worth evaluating whether you need similarity search for all generated images or only a subset. A common optimization is to generate embeddings asynchronously in a background job after the image is delivered to the user, rather than blocking the generation response on the embedding and indexing steps.