While Nano Banana (Google’s Gemini 2.5 Flash Image model) is powerful, it’s not perfect. One common limitation is that it sometimes struggles with fine-grained details in complex scenes. For example, if you ask it to edit small objects in a busy photo — like changing the reflection in a mirror or altering text on a product label — the output may come back distorted or inconsistent. Similarly, when dealing with intricate backgrounds, the model can over-edit areas you didn’t intend to change unless you give very precise instructions. This makes it less reliable for pixel-level precision compared to professional photo-editing tools.
Another challenge is consistency across edits. While the model is designed to preserve likeness when editing people, pets, or branded objects, drift can still occur after multiple rounds of editing. A person’s facial expression might subtly change, or a company logo might look slightly different after several transformations. Multi-turn editing helps manage this by focusing on one change at a time, but developers and creators often need to monitor results carefully to avoid small variations that matter in professional use cases. In particular, businesses relying on strict brand consistency should still use guardrails, such as prompt patterns that explicitly state “preserve proportions” or “do not alter the logo.”
Lastly, Nano Banana is subject to usage policies and watermarking requirements. Every output includes both a visible watermark and Google’s invisible SynthID mark to indicate that it’s AI-generated. This ensures transparency but also means you can’t generate “clean” outputs for commercial use without disclosure. On the access side, free-tier users face daily limits, and API users need to account for quotas and costs. For heavy workloads, infrastructure scaling and prompt design become important factors to manage. In short, Nano Banana is excellent for creative and practical editing, but users should be aware of its limitations in handling fine detail, maintaining long-term consistency, and working within Google’s watermarking and quota systems.
Production/enterprise usage
Teams are already applying Nano Banana in production. A mobile entertainment platform is testing avatar dress-up features where players upload photos and instantly try on in-game accessories. E-commerce brands are using a “shoot once, reuse forever” approach, capturing a single base model image and generating outfit or hairstyle variations instead of running multiple studio shoots.
To make this work at scale, generation needs retrieval. Without it, the model can’t reliably find the right outfits or props from huge media libraries. That’s why many companies pair Nano Banana with Milvus, an open-source vector database that can search billions of images and embeddings. Together, they form a practical multimodal RAG pipeline—search first, then generate.
👉 Read the full tutorial on Nano Banana + Milvus