What is Vertex AI? Unpacking Google's ML Platform

Machine learning has evolved from requiring dedicated engineering teams to becoming a standard business tool. Organizations deploy AI models for everything from fraud detection to recommendation engines, while cloud platforms now handle the complex infrastructure that once required specialized teams. This shift toward managed ML services has changed how companies approach AI development. Data scientists can now push models from experimentation to production without weeks of infrastructure work. Previously, getting a model live meant juggling separate tools for data processing, training, versioning, deployment, and monitoring—a process so cumbersome that most ML projects died in development.

Google Cloud introduced Vertex AI in May 2021 to tackle these workflow bottlenecks by creating an all-in-one machine learning platform. The service consolidates every step of the ML lifecycle—from data preparation through model monitoring—into a single managed environment. Built to “get data scientists and engineers out of the orchestration weeds,” Vertex AI leverages Google’s internal AI infrastructure to eliminate the operational complexity that traditionally kept models trapped in research phases. However, as the MLOps ecosystem has expanded, numerous alternatives now offer different approaches to unified ML development, each with distinct advantages for specific use cases or cloud preferences beyond Google’s ecosystem.

What is Vertex AI?

Vertex AI is Google Cloud’s machine learning platform that unifies all of Google’s ML tools and services under a single interface. Google launched it in May 2021 to bring together its previously separate ML offerings (AutoML and AI Platform) into one comprehensive system where data scientists and developers can build, train, and deploy machine learning models.

Vertex AI is trying to simplify the machine learning workflow. It wants to take you from data preparation and model building through to deployment and monitoring with less code and fewer steps than traditional approaches. The platform supports both AutoML (for teams with limited ML expertise) and custom training options (for those who need more control over their models). For teams already living in the Google Cloud ecosystem, this consolidated approach is appealing.

How Vertex AI works

Vertex AI coordinates different services across Google Cloud’s infrastructure to handle the heavy lifting of ML operations. The platform works by providing standardized APIs and interfaces that let you:

• Work with data at scale

• Access Google’s pre-trained models

• Customize models to your specific needs

• Deploy them to production environments

This approach means you don’t need to cobble together different tools for each stage of your ML workflow or relearn interfaces as you move from development to deployment.

For a real-world comparison, if traditional ML development feels like building a car from parts sourced from different manufacturers (with all the compatibility headaches that implies), Vertex AI is more like buying a pre-designed vehicle where all the components are made to work together. Sure, you can still customize it, but you’re starting from a functional foundation.

Primary functions and capabilities of Vertex AI

Google built Vertex AI to handle the entire machine learning lifecycle in one place. Instead of jumping between different tools or platforms for each step, developers can run the whole show from a single dashboard. Here’s what Vertex AI provides:

• AutoML tools: Create custom ML models without writing a single line of code (great for teams without dedicated data scientists).

• Custom model training: Develop models with your framework of choice (TensorFlow, PyTorch, scikit-learn) when you need more specialized control.

• Pre-trained APIs: Use Google’s ready-made models for common tasks like vision, language, and speech recognition without starting from scratch.

• Feature store: Manage, share, and reuse machine learning features across teams and projects to avoid duplicate work.

• Model deployment: Push models to production with a few clicks and scale them automatically based on traffic demands.

• Prediction service: Get real-time or batch predictions from your deployed models through standardized APIs.

• Model monitoring: Track your model’s performance and get alerts when quality drops or data drift occurs.

• MLOps tools: Manage the operational side of machine learning with pipelines, metadata tracking, and version control.

• Vertex AI Workbench: Work in integrated JupyterLab notebooks where data science and ML engineering happen in the same environment.

• Generative AI Studio: Experiment with foundation models and create text, code, and image applications without deep expertise.

Vertex AI limitations and challenges

Like any platform, Vertex AI comes with tradeoffs that might impact your team’s workflow and bottom line. Here are a few potential limitations and downsides to the platform:

• Google Cloud lock-in: Once you’re in the Vertex AI ecosystem, moving your models and workflows elsewhere can become increasingly difficult (your ML operations become tightly coupled with Google’s infrastructure).

• Pricing complexity: Vertex AI uses a multi-dimensional billing model with separate charges for training hours, prediction nodes, AutoML usage, data storage, and other services. This structure can make accurate cost estimation challenging, particularly for teams new to the platform.

• Learning curve: Despite its unified interface, the platform still often requires massive tech know-how to navigate. The documentation is comprehensive but can overwhelm newcomers.

• Resource consumption: Costs can spiral quickly during experimentation phases. While Google offers a free tier ($300 credit for new users), production workloads typically start at several hundred dollars monthly and can reach thousands (or more) depending on your usage patterns. Pricing information is accurate as of June 6, 2025.

• Performance overhead: The convenience of an integrated platform comes with some performance tradeoffs. Custom-built infrastructure might deliver better performance for specific use cases.

• Limited fine-tuning options: Vertex AI’s AutoML capabilities offer less granular control than fully custom approaches—sometimes creating a ceiling for model optimization.

• Feature fragmentation: Not all ML capabilities are equally mature within the platform. Some newer features still feel like they’re in beta, with occasional API changes disrupting workflows.

• Support considerations: Premium support plans start at $100/month (on top of usage costs), which smaller teams might find steep, especially when debugging complex ML issues.

Top alternatives to Vertex AI

Vertex AI packs a punch, but it’s not the only player in the ML platform space. And depending on your needs, it might not even be your best option. The best ML platform should match your team’s expertise, budget constraints, and specific use cases. You might find that alternatives to Vertex AI offer better pricing clarity, simpler workflows, or specialized features that align better with your actual needs rather than providing a kitchen sink approach.

1. DigitalOcean GenAI Platform

The DigitalOcean GenAI Platform brings AI agent creation to the masses without the complexity. It’s designed as a fully-managed service where developers can build, customize, and deploy powerful AI agents in minutes instead of months. It’s your one-stop shop for creating everything from customer service chatbots to specialized knowledge assistants—all without needing a degree in machine learning or the budget of a Fortune 500 company.

DigitalOcean achieves this by balancing sophistication with simplicity. You get access to top-tier models from Anthropic, Meta (Llama 3.3 70B), and Mistral AI, but wrapped in an interface that doesn’t require you to understand the inner workings of large language models to get results.

Key features:

• RAG workflows: Build agents that can reference your own data, documents, and knowledge bases designed to deliver accurate, context-aware responses.

• Customizable guardrails: Help filter out harmful content and keep your AI responses on-brand and appropriate for your audience.

• Function calling: Enable your AI agents to perform real-world actions like checking inventory, processing orders, or fetching real-time data.

• Agent routing: Combine multiple specialized agents to create comprehensive user experiences without building one massive all-purpose bot.

• Developer-first experience: API endpoints, SDKs, and simple integrations mean you can embed your agents into applications in minutes.

• Transparent pricing: Pay per token with predictable rates (starting at $0.009 per million tokens for embeddings) without the complex multi-variable formulas typical of enterprise platforms.

• Integration options: Add AI agents to websites and apps with pre-built plugins for WordPress, Ghost, Joomla, and more.

2. LangChain

LangChain is less a platform and more like a toolbox that developers can use to build their own AI solutions from scratch. It’s an open-source framework that helps developers connect language models to other data sources and applications.

Think of LangChain as a set of Lego blocks for building AI applications. Unlike fully-managed platforms, it doesn’t host models or provide infrastructure. Instead, it gives you the components to assemble your own solutions (which is both its strength and limitation). You can build nearly anything with it, but the tradeoff is that you’re responsible for hosting, scaling, and maintaining everything yourself.

Key features:

• Chain building: Create sequences of operations that connect language models with various tools and data sources.

• Customizable agents: Build autonomous AI agents that can use tools and make decisions based on user inputs.

• Document loaders: Connect to virtually any data source—from PDFs and websites to databases and APIs.

• Memory systems: Implement conversation history and context management without starting from scratch.

• Evaluation frameworks: Test and refine your AI applications with built-in evaluation tools.

• Prompt management: Create, version, and optimize prompts for better AI responses.

• Open-source community: Benefit from a large developer community and extensive documentation.

• Extensibility: Connect to almost any LLM provider, including OpenAI, Anthropic, and open-source models.

3. CrewAI

CrewAI takes a new approach to AI development that focuses on orchestrating multiple AI agents to work together as a team. It’s gained traction among developers building complex, multi-agent systems where different specialized AIs need to collaborate on solving problems.

It’s not a comprehensive platform like Vertex AI. CrewAI is laser-focused on a specific part of the AI landscape: agent collaboration. It’s less about providing an end-to-end solution and more about giving you the framework to create your own AI teams with specialized roles and responsibilities.

Key features:

• Multi-agent orchestration: Create systems where specialized AI agents work together on complex tasks.

• Role-based architecture: Assign different capabilities and responsibilities to each agent in your crew.

• Process delegation: Define workflows where agents pass tasks to each other based on their specialties.

• Human-in-the-loop options: Integrate human oversight and intervention at critical decision points.

• Tool integration: Connect agents to external tools, APIs, and data sources to extend their capabilities.

• Hierarchical structures: Design systems with manager agents that coordinate the work of specialized workers.

• Customizable interaction patterns: Define how agents communicate with each other and escalate issues.

• Open-source foundation: Build on a transparent codebase you can modify for your specific needs.

Vertex AI vs. DigitalOcean GenAI: Head-to-head comparison

Choosing between Google’s enterprise-grade ML platform and DigitalOcean’s developer-friendly alternative comes down to your specific needs, team capabilities, and budget. Both platforms aim to simplify AI implementation, but they take different approaches to pricing, usability, and integration.

Developer experience and learning curve

Vertex AI provides comprehensive capabilities but comes with a steeper learning curve. The platform requires familiarity with Google Cloud’s ecosystem and often demands more specialized knowledge. Documentation is extensive but can overwhelm newcomers with technical jargon and multiple implementation paths.

DigitalOcean GenAI Platform prioritizes simplicity with a straightforward interface that gets you from concept to working AI agent quickly. The platform abstracts away infrastructure complexities without limiting capabilities, making it accessible to developers without ML expertise.

Pricing structure and cost predictability

Vertex AI uses a multi-dimensional pricing model with separate charges for training, prediction, AutoML usage, and various other components. This complexity can make cost estimation challenging (if not impossible), especially for teams scaling their AI usage. The free tier provides limited experimentation capacity before costs begin to accumulate across multiple billing dimensions.

DigitalOcean GenAI Platform offers transparent, token-based pricing that scales predictably with usage. Starting at just $0.009 per million tokens for embeddings and with clear rates for different models, you can accurately forecast costs as your usage grows. This straightforward approach eliminates the surprise bills that often accompany complex ML deployments.

Integration and ecosystem compatibility

Vertex AI integrates with Google Cloud’s broader ecosystem for easy connections to BigQuery, Cloud Storage, and other Google services. This tight integration benefits teams already invested in Google’s infrastructure but can create dependencies that make potential migrations difficult.

DigitalOcean GenAI Platform offers standard API endpoints and SDKs that work with any modern tech stack. It integrates naturally with other DigitalOcean services, but it’s designed for flexibility, allowing you to incorporate AI capabilities into existing applications (regardless of where they’re hosted). The platform includes pre-built plugins for common CMSes like WordPress and Ghost to accelerate web integration.

Specialized capabilities and use cases

Vertex AI is great for enterprise-scale ML operations with robust capabilities for custom model training, hyperparameter tuning, and managing complex ML pipelines. It’s ideal for large organizations with dedicated data science teams working on sophisticated machine learning projects.

DigitalOcean GenAI Platform focuses on practical AI implementation for customer-facing applications like chatbots, content generation, and knowledge assistants. Its RAG workflows, function calling, and agent routing capabilities make it perfect for businesses looking to add practical AI features to their products without the overhead of building ML infrastructure.

What is Vertex AI FAQs

What is the difference between OpenAI and Vertex AI?

OpenAI is primarily a research company that develops and provides access to foundation models like GPT and DALL-E through APIs, while Vertex AI is Google Cloud’s comprehensive machine learning platform that handles the entire ML lifecycle from data preparation to model deployment. OpenAI focuses on providing powerful pre-trained models, whereas Vertex AI offers a complete MLOps environment for building, training, and managing both custom and pre-trained models.

What is the difference between Google AI and Vertex AI?

Google AI is the umbrella term for all of Google’s artificial intelligence research, products, and initiatives across the company, while Vertex AI is specifically Google Cloud’s unified machine learning platform for enterprise customers. Google AI encompasses everything from Search algorithms to Google Assistant, whereas Vertex AI is a focused tool for businesses to build and deploy their own ML models.

Can I use Vertex AI for free?

Vertex AI offers a free tier with $300 in credits for new Google Cloud users, but this is limited and primarily designed for experimentation. Production workloads typically cost several hundred to thousands of dollars monthly, depending on usage, with complex multi-dimensional pricing for training, predictions, storage, and various services.

What is Vertex AI used for?

Vertex AI is used for building, training, and deploying machine learning models at enterprise scale, handling everything from data preparation and model training to production deployment and monitoring. Companies use it for applications like fraud detection, recommendation engines, and custom AI solutions that require the full ML lifecycle management.

How does Vertex AI compare to DigitalOcean GenAI?

Vertex AI is a comprehensive enterprise ML platform with complex pricing and a steep learning curve, designed for large organizations with dedicated data science teams building sophisticated ML pipelines. DigitalOcean GenAI Platform focuses on simplicity and speed, offering transparent pricing and easy AI agent deployment for developers and smaller businesses who want practical AI features without ML infrastructure complexity.

References

• Google Cloud unveils Vertex AI

Build with DigitalOcean’s GenAI platform

DigitalOcean’s GenAI Platform makes it easier to build and deploy AI agents without managing complex infrastructure. Our fully-managed service gives you access to industry-leading models from Meta, Mistral AI, and Anthropic with must-have features for creating AI/ML applications.

Key features include:

• RAG workflows for building agents that reference your data

• Guardrails to create safer, on-brand agent experiences

• Function calling capabilities for real-time information access

• Agent routing for handling multiple tasks

• Fine-tuning tools to create custom models with your data

Don’t just take our word for it—see for yourself. Get started with AI and machine learning at DigitalOcean to get access to everything you need to build, run, and manage the next big thing.