Vertex AI Studio vs. Google AI Studio: Choosing the Right AI Tool for Your Startup
Generative AI models are revolutionizing the startup landscape, offering access to powerful tools for creating text, images, and video content in mere seconds. This shift is democratizing innovation, enabling startups to leverage cutting-edge technology and compete on a level playing field with established players. However, navigating this new terrain requires the right tools and guidance.
Google Cloud provides a suite of tools designed to empower startups in their Generative AI journey, with two platforms standing out: Vertex AI Studio and Google AI Studio. These platforms offer distinct approaches to prototyping and testing your prompts on Google foundation models.
This article will be your guide to navigating these platforms, highlighting their unique capabilities and helping you choose the perfect fit for your AI endeavors.
Vertex AI Studio
Vertex AI Studio is your comprehensive AI control center for customizing and deploying generative models. It’s baked into Vertex AI, Google Cloud’s unified machine learning platform. The tool gives you the ability to experiment with some of Google’s foundation models including Gemini, PaLM 2 and Imagen 2 in-console. You can also tune and distill foundation models to optimize them for specific tasks or knowledge domains.
Here’s why Vertex AI Studio stands out as the perfect launchpad for your startup’s generative AI journey:
- Access to Google’s Foundation Models:
Within Vertex AI Studio, you can experiment and build with some of Google’s most advanced foundation models, including Gemini PaLM 2, Imagen, and Codey. Leverage the latest advancements in AI. Stay ahead of the curve with access to continually updated models, ensuring your startup remains at the forefront of generative AI innovation.
2. Customization and Tuning:
Tune foundation models with your own data, creating bespoke model solutions tailored to your unique needs and use cases. Tuning approaches include supervised tuning, reinforcement learning from human feedback (RLHF) tuning and model distillation.
3. Enterprise-Grade Infrastructure and Security:
Leverage Google Cloud’s robust infrastructure and security features like VPC-SC (Virtual Private Cloud Service Controls), CMEK (Customer-Managed Encryption Keys), and AXT (Access Transparency) to protect your data and ensure the reliability of your AI models.
4. Multi-modality Support:
Prompt across a variety of modalities, including text, images, and videos. Combining any of these three modalities can help achieve the best model outputs.
5. Prototype to Code:
You can export code that includes your prompt along with any relevant examples and settings used during prompting, such as temperature, top p, top k, and safety settings.
Vertex AI Studio is easy to use for any skill level, from seasoned data scientists to those just starting their AI journey. The interface is intuitive, the documentation is comprehensive, and you get access to Google-specific models, including fine-tuned versions of those models.
Google AI Studio
For startups just starting their exploration into AI tools or wanting to experiment with them without a hefty investment, AI Studio is your ideal playground. Here’s why it’s perfect for early-stage startups:
- Free and Accessible:
Budget constraints? No problem! AI Studio is completely free and web-based. With just a Google account, you can begin building and experimenting immediately.
2. Rapid Prototyping
Google AI Studio helps you turn your prompts into reality quickly. Its intuitive interface and prompt examples enable rapid prototyping and launching of AI-powered experiences for consumers. Exporting code is also supported for individual prompts.
3. Community Collaboration:
Prompts can easily be saved and shared via Google Drive.
4. Mainly Interested in Text Prompt Prototyping:
As of the time of this writing, Google AI Studio does not support image generation.
Google AI Studio retains many of the multimodal and tuning features found in Vertex AI Studio, however it lacks enterprise protections and MLOps functionality for production use-cases. It is primarily intended for initial prototyping or small-scale model deployments.
Choosing the Right Tool For Your Use
Still unsure which platform suits you best? Here’s a quick guide:
Choose Google AI Studio If:
- You are in the initial stages of testing different AI models, features and techniques.
- You are exploring consumer or hobbyist use-cases that don’t require enterprise data protections.
- You seek a free and user-friendly platform for experimentation.
- You are rapidly prototyping and testing prompts.
Choose Vertex AI Studio If:
- You are looking to deploy and scale AI solutions to multiple users.
- Enterprise-grade security and compliance are essential for your use-case.
- Customization and fine-tuning of AI models using a variety of techniques is crucial for your project.
- You’re looking to use speech-to-text, text-to-speech, and text-to-image models.
One More Thing…
It’s important to note that Google AI Studio quota limits are significantly different from those on Vertex AI. While Google AI Studio deals with a limited amount of QPM (queries per minute), RPM (requests per minute) and TPM (tokens per minute), Vertex AI has more flexible, region and model-specific quotas that apply at the GCP project level and are shared across all applications using a project. Vertex AI quotas can potentially be increased upon request in the cloud console, making it more suitable for larger-scale production environments.
No matter where you are in your startup journey, Google Cloud provides the tools to help you harness the transformative power of generative AI. So, unleash your creativity, build groundbreaking solutions, and let AI propel your startup to new heights!
Feel free to reach out to our team if you have questions or want to explore specific use cases.
Happy building!
