If you are new to Gemini, using the quickstarts is the fastest way to get started.
However, as your generative AI solutions mature, you may need a platform for building and deploying generative AI applications and solutions end to end. Google Cloud provides a comprehensive ecosystem of tools to enable developers to harness the power of generative AI, from the initial stages of app development to app deployment, app hosting, and managing complex data at scale.
Google Cloud's Vertex AI platform offers a suite of MLOps tools that streamline usage, deployment, and monitoring of AI models for efficiency and reliability. Additionally, integrations with databases, DevOps tools, logging, monitoring, and IAM provide a holistic approach to managing the entire generative AI lifecycle.
The following table summarizes the main differences between Google AI and Vertex AI to help you decide which option is right for your use case:
| Features | Google AI Gemini API | Vertex AI Gemini API |
|---|---|---|
| Gemini models | Gemini 2.0 Flash, Gemini 1.5 Flash, Gemini 1.5 Pro, Gemini 1.0 Pro, Gemini 1.0 Pro Vision (deprecated) | Gemini 2.0 Flash, Gemini 1.5 Flash, Gemini 1.5 Pro, Gemini 1.0 Pro, Gemini 1.0 Pro Vision, Gemini 1.0 Ultra, Gemini 1.0 Ultra Vision |
| Sign up | Google account | Google Cloud account (with terms agreement and billing) |
| Authentication | API key | Google Cloud service account |
| User interface playground | Google AI Studio | Vertex AI Studio |
| API & SDK | Server and mobile/web client SDKs
|
Server and mobile/web client SDKs
|
| No-cost usage of API & SDK | Yes, where applicable | $300 Google Cloud credit for new users |
| Quota (requests per minute) | Varies based on model and pricing plan (see detailed information) | Varies based on model and region (see detailed information) |
| Enterprise support | No |
Customer encryption key Virtual private cloud Data residency Access transparency Scalable infrastructure for application hosting Databases and data storage |
| MLOps | No | Full MLOps on Vertex AI (examples: model evaluation, Model Monitoring, Model Registry) |
To learn which products, frameworks, and tools are the best match for building your generative AI application on Google Cloud, see Build a generative AI application on Google Cloud.
Migrate from Gemini on Google AI to Vertex AI
If your application uses Google AI Gemini APIs, you'll need to migrate to Google Cloud's Vertex AI Gemini APIs.
When you migrate:
You can use your existing Google Cloud project (the same one you used to generate your API key) or you can create a new Google Cloud project.
Supported regions may differ between Google AI Studio and Vertex AI. See the list of supported regions for generative AI on Google Cloud.
Any models you created in Google AI Studio need to be retrained in Vertex AI.
The Google Gen AI SDK provides a unified interface to Gemini 2.0 through both the Gemini Developer API and Vertex AI. With a few exceptions, code that runs on one platform will run on both.
Note that if you want to call the Gemini API directly from a production mobile or web app, then migrate to use the Vertex AI in Firebase client SDKs (available for Swift, Android, Web, and Flutter apps). These client SDKs offer critical security options and other features for production mobile and web apps.
Delete unused API Keys
If you no longer need to use your Google AI Gemini API key, follow security best practices and delete it.
To delete an API key:
Open the Google Cloud API Credentials page.
Find the API key you want to delete and click the Actions icon.
Select Delete API key.
In the Delete credential modal, select Delete.
Deleting an API key takes a few minutes to propagate. After propagation completes, any traffic using the deleted API key is rejected.
Next steps
- See the Generative AI on Vertex AI overview to learn more about generative AI solutions on Vertex AI.
- Dive deeper into the Vertex AI Gemini API.