deprecated-generative-ai-swift VS vertex-ai-samples

As developers, we can access Gemini via the Gemini API in Google AI Studio. There are also SDKs available for Python, JavaScript, Swift, and Android.

The previous post kicked off the conversation about generative AI, presenting "Hello World!" examples that help you get started with the Gemini API in a more user-friendly way than in Google's documentation. It presented code samples showing you how to use the API from both Google AI as well as GCP Vertex AI. This post follows up with a multimodal example, one that supports streaming output, and another one that supports multi-turn conversations ("chat"), and how to upgrade to the latest 1.0 and 1.5 models, the latter of which is in public preview at the time of this writing.

Get your API Key here

Vertex AI (Google Cloud/"GCP")

Google's introduction of new tools for building and managing multi-agent ecosystems through Vertex AI is a pivotal move for enterprises. The Agent Development Kit (ADK) is a notable feature, providing an open-source framework that allows users to create AI agents with fewer than 100 lines of code. This framework supports Python and integrates with the AI capabilities of Vertex AI.

For further exploration, visit: Vertex AI Overview | Live API

We use Vertex AI to simplify our implementation, to test different LLM providers and models, and to compare metrics such as cost, latency, errors, time to first token, etc, across models.

Ironwood is part of Google's AI Hypercomputer architecture, a system optimized for AI workloads. This integrated supercomputing system leverages over a decade of AI expertise. It supports various frameworks such as Vertex AI and Pathways, enabling developers to utilize Ironwood effectively for distributed computing.

Perhaps you're new to AI or wish to experiment with the Gemini API before integrating into an application. Using the Gemini API from Google AI is the best way for you to get started and get familiar with using the API. The free tier is also a great benefit. Then you can consider moving any relevant work over to Google Cloud/GCP Vertex AI for production.

Access through Vertex AI, Google's unified AI development platform.

Seamless integration with Google Cloud: GKE integrates smoothly with other Google Cloud services like Cloud Storage, Cloud SQL, and, importantly, Vertex AI, where Gemini and other LLMs are hosted.

It's Google Cloud Platforms "AI" service[0], so actually more analogous to what is now called Azure AI Foundry[1], and what used to be called Azure OpenAI Studio.

Microsoft Copilot Studio[2] (formally Power Platform Power Virtual Agents) imho is unique in it's enterprise AI offering. I truly think Copilot Studio is going to be Microsoft's "killer app" when it comes to companies utilizing AI at scale internally, and not it's Azure service.

0. https://cloud.google.com/vertex-ai

1. https://ai.azure.com/

2. https://copilotstudio.microsoft.com