I'm in the process of developing a chatbot application and I'm exploring options for integrating Gemini, a large language model (LLM).
I'm considering whether to utilize Vertex AI (@google-cloud/vertexai) for integration or to directly incorporate Gemini (@google/generative-ai)
google-cloud/vertexai:
https://cloud.google.com/vertex-ai/docs/generative-ai/start/quickstarts/quickstart-multimodal
google/generative-ai:
https://ai.google.dev/tutorials/node_quickstart.
Given that Gemini is an LLM and Vertex AI is a platform, I'm seeking insights on which approach might be more suitable for my project. Any recommendations or considerations would be greatly appreciated .
Gemini is a large language model as you specified. You can use it (via API calls) by calling either Google AI APIs or Vertex AI APIs.
If you are relatively new to Gemini and wants to explore the feature and build some prototype for your chatbot app, Google AI APIs (with Google AI Studio) is a fast way to get started. While your app and idea matures and you'd like to leverage more MLOps tools that streamline the usage, deployment, and monitoring of models, you can move to Google Cloud Vertex AI which provides Gemini APIs along with many other features. Basically, to help you productionize your app.
Depending on your app requirement and stages of your development, you can choose the path:
Pay attention to the Google AI vs. Vertex AI differences when making decision.
Update (2025):
Google released a new GenAI SDK that provides a unified interface to Gemini >2.0models through both the Gemini Developer API and the Gemini API on Vertex AI. With a few exceptions, code that runs on one platform will run on both. This means that you can prototype an application using the Gemini Developer API and then migrate the application to Vertex AI without rewriting your code.
Detail see here.