Google Cloud AI vs Vertex AI We bring clarity to Google Cloud AI vs Vertex AI. Learn when to choose either.

Engineers are faced with shiny new tools and platforms every day, and it’s especially true in the artificial intelligence space. So what’s the difference between Google Cloud AI vs Vertex AI?

Google Cloud AI and Vertex AI stand out as two robust options, each offering unique strengths that cater to different facets of AI development and deployment.

We’ll break down these two platforms to help you understand their functionalities and determine which might suit your AI needs—from machine learning model development to deploying serverless apps.

Let’s understand the basics: Google Cloud AI vs Vertex AI

What is Google Cloud AI

Google Cloud AI encompasses a broad suite of AI services and tools provided by Google Cloud Platform (GCP). It supports a range of AI capabilities such as natural language processing, machine learning, and more, using popular frameworks like TensorFlow and PyTorch. It’s designed to cater to data scientists and developers looking to integrate AI functionalities into their applications, making AI development accessible with automation features like AutoML and robust machine learning workflows.

Top Google Cloud AI features

1. Comprehensive AI Development Ecosystem Google Cloud AI offers a robust platform that supports the complete AI development lifecycle, from data preparation to model deployment. This unified workflow allows data scientists to seamlessly transition through stages of model training, leveraging powerful tools like TensorFlow and PyTorch along with GCP’s cloud storage and BigQuery for efficient data handling.
2. Advanced Machine Learning Capabilities The platform excels in facilitating advanced machine learning applications, including generative AI, natural language processing, and large language models (LLMs). With features like AutoML for hyperparameter tuning and the use of APIs for easy integration, it’s designed to enhance the functionality and scalability of ML models across diverse applications.
3. Customization and Automation Google Cloud AI enables high customization and automation levels, making it easier to tailor AI models to specific needs and automate routine tasks. This is especially beneficial for complex workflows involving multiple datasets, MLOps, and custom model development, all within an environment that supports both Python and serverless computing.

What is Google Vertex AI

Vertex AI on the other hand, represents a more unified and streamlined approach within the Google Cloud ecosystem. It merges Google’s AI Platform and other AI tools into a cohesive, end-to-end environment that simplifies the deployment of ML models. Vertex AI supports both custom models and AutoML, providing tools for every stage of the AI lifecycle, from data preparation to model training and hyperparameter tuning.

Top Google Cloud Vertex AI features

1. Versatile Framework and Tool Integration Vertex AI supports a wide array of machine learning frameworks and tools, including TensorFlow, PyTorch, and scikit-learn, accommodating a diverse range of ML models and applications. This versatility is key for developers and data scientists aiming to optimize their AI models for specific tasks, whether it’s chatbot creation or predictive analytics.
2. Streamlined Machine Learning Pipelines Vertex AI simplifies the management of machine learning pipelines, integrating various stages of the AI model lifecycle within a single platform. This includes everything from data analysis and model training using Vertex AI pipelines to deploying and managing endpoints, thus accelerating the development and deployment processes.
3. Scalable AI Model Deployment Leveraging Google Cloud’s infrastructure, Vertex AI allows for scalable deployment of AI models, supported by powerful GPUs and custom containers that ensure performance and flexibility. The platform’s serverless options and ecosystem support help enterprises scale their AI solutions efficiently, catering to both small-scale experiments and large-scale production environments.

One of the main differences between Google Cloud AI vs Vertex AI is that Vertex AI offers a one-stop shop where you can handle everything from prepping your data to training your models, and even deploying and monitoring them. This really cuts down on the hassle of juggling different services and components separately. It makes the whole process smoother and simpler.

Hope the basic differences between Google Cloud AI vs Vertex AI is beginning to take shape. Read on.

Google Cloud AI vs Vertex AI features & capabilities

Model training and deployment

Both platforms offer comprehensive tools for model training. Vertex AI provides an integrated environment with Vertex AI Workbench and supports frameworks like TensorFlow, PyTorch, and even the newer, experimental framework Gemini.

Google Cloud AI leverages similar frameworks but also offers extensive support for MLOps, ensuring that the machine learning models are scalable and manageable throughout their lifecycle.

Data handling and integration

Vertex AI excels with its seamless integration with BigQuery and other GCP services, facilitating efficient data analysis and preparation.

Google Cloud AI also offers robust options with its AI Platform, enabling easy access to other Google services like Cloud Storage and more advanced data handling capabilities through pipelines.

Automated machine learning (AutoML)

AutoML is a highlight for both platforms, allowing data scientists to automate the creation of machine learning models.

Vertex AI takes it a step further by integrating with Tabular, a tool for handling tabular data, which simplifies the workflow for non-experts and accelerates the development process.

AutoML is also one of the key differentiators between Google Cloud AI vs Vertex ai.

Pricing and accessibility

Google Cloud AI’s pricing structure is competitive, often compared with other major players like AWS and Microsoft.

Vertex AI typically follows a similar pricing model, focusing on pay-as-you-go options, but offers some additional flexibility that might be appealing for handling large-scale deployments or enterprises needing custom pricing models.

Ecosystem and community support

Both platforms benefit from Google’s strong ecosystem, including extensive documentation, a vibrant community of developers, and integration with various Google Cloud services.

The open-source nature of tools like TensorFlow and the backing of Google’s infrastructure mean that both platforms are continually evolving with contributions from developers worldwide.

Google Cloud AI vs Vertex AI: From chatbots to generative AI

Vertex AI and Google Cloud AI are both adept at handling a variety of AI use cases. For instance, if you’re looking to deploy a chatbot, both platforms support natural language processing capabilities, with APIs that integrate easily into existing apps.

For more specialized applications, such as generative AI or large language models (LLMs) like ChatGPT, Vertex AI provides customized pipelines and the ability to deploy models using custom containers or serverless environments, enhancing flexibility and scalability.

Whether you’re a seasoned data scientist or a developer eager to explore AI, both Google Cloud AI and Vertex AI offer powerful tools and capabilities that can enhance your AI projects. Google Cloud AI provides a broad array of AI functionalities across multiple domains, making it a versatile choice for many applications.

Vertex AI streamlines the workflow for deploying ML models and offers a slightly more integrated and user-friendly approach, particularly beneficial for those deeply embedded in the Google Cloud ecosystem.

Google Cloud AI vs Vertex AI: When to choose either of them

Choosing between Google Cloud AI vs Vertex AI often depends on the specific needs of your project, including the complexity of the machine learning models, the integration requirements with other cloud services, and the scale of deployment. Here are some scenarios or use cases that might guide your decision on when to use one platform over the other:

When to use Google Cloud AI

1. Complex AI Solutions Across Multiple Domains: If your project requires a broad array of AI functionalities across various domains like video analysis, natural language processing, and more, Google Cloud AI provides a wide range of services and tools that can be combined to create comprehensive solutions.
2. Integrated Cloud Solutions: For projects that benefit from deep integration with other Google Cloud services (like Compute Engine, Google Kubernetes Engine, and more), Google Cloud AI offers extensive capabilities that are well-suited to complex, cloud-based infrastructures.
3. Advanced Machine Learning Operations (MLOps): If your focus is on managing the lifecycle of ML models with an emphasis on monitoring, versioning, and pipeline management, Google Cloud AI’s strong support for MLOps is ideal.
4. Custom AI Model Development: For teams that require extensive customization and control over their AI model development, particularly those using open-source frameworks like TensorFlow or PyTorch, Google Cloud AI provides a flexible environment.
5. Research and Development Projects: Researchers or developers experimenting with cutting-edge AI technologies or developing proprietary algorithms might prefer Google Cloud AI for its openness and extensive toolset.

When to use Vertex AI

1. End-to-End Machine Learning Model Deployment: If you need a streamlined, cohesive platform that simplifies the process from model training to deployment, Vertex AI is designed to offer an integrated approach, reducing complexity and improving efficiency.
2. Automated Machine Learning (AutoML): Projects that benefit from automated model training, such as those without deep expertise in model tuning or those needing to quickly deploy models, will find Vertex AI’s advanced AutoML features very useful.
3. Large Scale AI Model Deployments: If deploying scalable and high-demand AI models is a key requirement, especially in commercial environments, Vertex AI’s robust and scalable deployment options, including serverless and custom containers, are optimal.
4. Rapid Prototyping and Development: Startups and enterprises looking to quickly develop and test AI models will find Vertex AI’s tools conducive for rapid prototyping, thanks to its user-friendly interface and simplified workflow.
5. Integration with Data Analytics Services: For projects heavily reliant on Google Cloud data services like BigQuery for real-time data analytics and insights, Vertex AI offers superior integration capabilities, enhancing data-driven decision-making processes.

These scenarios showcase the strengths of each platform, and we hope they’ve helped you decide based on the specific requirements and goals of your AI projects.

Whether you’re looking for a comprehensive AI toolkit or a streamlined model deployment platform, both Google Cloud AI and Vertex AI offer compelling features tailored to a range of use cases.

Google Cloud AI vs Vertex AI comparison table

Feature	Google Cloud AI	Vertex AI
Integration with GCP	Full integration with Google Cloud services	Seamless integration with services like BigQuery
AI Frameworks Supported	TensorFlow, PyTorch, others	TensorFlow, PyTorch, Gemini
Model Training	Extensive support for custom and AI Platform models	Simplified model training, supports custom and AutoML models
Machine Learning Workflows	Complex workflows supported, emphasizes MLOps	Streamlined workflows with Vertex AI Pipelines
AutoML	Robust AutoML capabilities for various AI models	Advanced AutoML integration, including Tabular for handling tabular data
Data Handling	Strong capabilities with Cloud Storage and Pipelines	Direct integration with GCP data services, including serverless options
Pricing	Competitive, similar to AWS and Microsoft	Flexible, pay-as-you-go with potential for custom plans
User Accessibility	Broad toolset for data scientists and developers	User-friendly for quick deployment and management
Customization	High customization for complex AI solutions	Emphasizes easy deployment of custom models and containers
Deployment	Supports scalable deployment, including GPUs	Serverless deployment, supports custom containers and GPUs
Use Cases Supported	Wide range, including NLP, chatbots, and data analysis	Specializes in ML models, including generative AI and large language models
Community and Ecosystem	Strong support through documentation and community	Integrated support within Google’s ecosystem, including extensive docs

Like a bonus scene in a Marvel movie that predicts a sequel, then there’s also AWS SageMaker.

Amazon’s equivalent to Vertex AI is Amazon SageMaker. SageMaker is a fully managed service that provides every developer and data scientist with the ability to build, train, and deploy machine learning models quickly.

Similar to Vertex AI, SageMaker supports a wide range of machine learning frameworks, offers tools for every step of the machine learning lifecycle, and simplifies the process of model training and deployment with automation features.