Spring AI connects a Java application to Google’s Gemini models through either the Gemini Developer API or Vertex AI. The Spring AI 1.1 integration uses spring-ai-starter-model-google-genai for Spring Boot auto-configuration and also documents manual setup. Because the Google GenAI integration page is versioned for Spring AI 1.1 while the general API and comparison references identify Spring AI 2.0.1, confirm dependency names, properties, and model identifiers against the release your application actually uses.
Choose the Google service path first
Spring AI’s Google GenAI integration supports two routes: the Gemini Developer API and Vertex AI. They differ in credentials and configuration, so choose the service your application is intended to call before setting up the Spring model.
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| Route | Documented setup | What to verify |
|---|---|---|
| Gemini Developer API | Obtain an API key through Google AI Studio and provide it to the application. Spring AI’s 1.1 reference describes this as useful for prototyping and development. | Confirm that the key and integration properties match the Spring AI release in use, and check current model availability with Google. |
| Vertex AI | Configure a Google Cloud project ID and location; the reference uses Google Cloud credentials and illustrates application-default login with the gcloud CLI. The documentation presents this route for production deployments using Google Cloud features. |
Confirm the project, location, credentials setup, and model availability for the intended deployment. |
These are documented setup distinctions, not a security or cost comparison. The reviewed Spring AI references do not establish pricing, quotas, regional coverage, or a comparative security assessment.
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The Spring AI 1.1 Google GenAI reference names org.springframework.ai:spring-ai-starter-model-google-genai as the Spring Boot starter. It documents these connection properties and the chat-model switch:
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| Property | Use in the 1.1 reference |
|---|---|
spring.ai.google.genai.api-key |
Gemini Developer API key |
spring.ai.google.genai.project-id |
Google Cloud project ID for Vertex AI |
spring.ai.google.genai.location |
Vertex AI location |
spring.ai.google.genai.credentials-uri |
Credentials URI configuration |
spring.ai.model.chat |
Top-level switch for enabling the Google GenAI chat model |
These names are specifically from the Spring AI 1.1 integration documentation; they should not be assumed unchanged in another release. Consult the Spring AI 1.1 Google GenAI Chat reference and the documentation matching your exact dependency before applying them.
Chat options are documented under spring.ai.google.genai.chat.options.*, including model selection and temperature. For request-specific settings, the reference demonstrates GoogleGenAiChatOptions. Keep model identifiers aligned with current Google availability rather than relying on older examples in a versioned guide.
Use manual configuration when auto-configuration is not the fit
The 1.1 reference also describes a manual configuration path using GoogleGenAiChatModel and the Google GenAI Client. This provides an alternative to the starter-based Spring Boot setup when you need to construct and configure those components directly. The reference’s class and configuration details are version-sensitive, so use its release-specific examples rather than copying them into a different Spring AI version without checking compatibility.
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What the integration documents as supported
Spring AI’s current chat-model comparison page lists the following Google GenAI capabilities. These are framework documentation claims, not independent tests of model quality or performance.
| Capability | Google GenAI listing |
|---|---|
| Input modalities | Text, PDF, image, audio, and video |
| Tools or function calling | Supported |
| Streaming | Supported |
| Retry and observability | Supported |
| Built-in JSON | Supported |
| Local deployment | Unsupported |
| OpenAI API compatibility | Unsupported |
See the Spring AI chat model comparison for the current framework listing. Support in the framework does not by itself establish that every Gemini model or deployment path accepts every modality or option.
How Spring AI fits into an application
Spring AI presents its model API as a portable abstraction across providers, and its ChatClient as a fluent API for communicating with a model. That makes it possible to keep common application interaction patterns within Spring AI while using provider-specific options where Gemini configuration requires them. The wider framework also documents tool calling, advisors, MCP integration, and vector-store APIs; those broader features are not proof that each is automatically enabled by the Google GenAI chat starter.
The Spring AI ChatClient documentation explains the broader chat API. For Google-specific configuration, follow the documentation for the integration and release you target.
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Check versions and model availability before shipping
The Google GenAI integration page cited here is for Spring AI 1.1, whereas the current general API and chat comparison references identify Spring AI 2.0.1. The model context and configuration examples differ across these version scopes. Before upgrading or deploying, check the matching Spring AI reference for the exact starter coordinates, properties, option types, and supported features; separately verify that the chosen Gemini model is currently available for your API path and location.
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