Today, Gemini 3 is effectively a cloud-only model for normal developers and enterprises. It is delivered through hosted services like the Gemini API, Google Cloud (Vertex AI), and related Google products. You don’t download the Gemini 3 weights or run them fully offline on your own hardware. That means every call to Gemini 3 goes over the network to Google’s infrastructure, and you design your application around that assumption. For most teams, this is acceptable because they already depend on cloud services for databases, storage, and authentication.
Because it is cloud-based, you should think carefully about latency, availability, and data boundaries. For latency, you can place your application servers in regions close to the Gemini 3 endpoints and keep prompts as lean as possible. For availability, assume the model API can have transient failures or throttling and build retry logic, circuit breakers, and graceful degradation. For data boundaries, you must treat prompts and responses as sensitive data if they contain user or enterprise content. Use TLS, strict IAM roles, and avoid logging full raw prompts unless they are anonymized or scrubbed.
If you want some of the benefits of “offline-like” privacy and control, you can design your system so that most data remains inside your own infrastructure, and only the minimum necessary context is sent to Gemini 3. A common pattern is to store documents and embeddings in a vector database such as Milvus deployed in your own environment, or in the managed Zilliz Cloud. At query time, you retrieve only the relevant slices and send those plus the user question to Gemini 3. This way, Gemini 3 never sees your entire corpus, only the pieces needed to answer a specific question, which is often a good compromise between cloud-only models and stricter data policies.