Vertex AI 2025 Review: Features, Steps, User Sentiment, And Competitive Edge Revealed

What Is Vertex AI?

Vertex AI is Google Cloud’s unified platform for building, training, deploying, and monitoring machine learning and generative AI models. From drag-and-drop pipelines to advanced custom code, it empowers teams to accelerate data science without tool fragmentation or complex wiring.

Firsthand Experience: Strengths and Weaknesses

Having worked extensively with Vertex AI, the overall experience aligns closely with the consensus from recent user reviews: the unified workflow and Google Cloud integrations stand out as the most impressive aspects. The platform truly shines when building, deploying, and monitoring machine learning pipelines end-to-end its integration with BigQuery accelerates data exploration and model iteration dramatically. The UI design supports both no-code and custom code users, making it flexible for a variety of workflows.

However, the platform also has notable drawbacks. The most significant pain point is the fragmented and sometimes unclear documentation, especially when customizing training or dealing with advanced configurations. This can create friction, particularly for teams less familiar with Google Cloud's ecosystem or those requiring complex, non-standard setups. The learning curve, especially for those beyond basic workflows, is steeper than some competitors like SageMaker.

• Most liked: Unified workflow and BigQuery integration dramatic speed gains.
  Most disliked: Documentation gaps and setup complexity for advanced/custom scenarios.

User Ratings by Platform

Vertex AI enjoys high average scores with most reviewers citing its core integrations and workflow as major strengths. Sentiment on forums like Reddit is more mixed, reflecting frustration around complexity in advanced use cases.

Sentiment Breakdown

Most users feel positively especially about day-to-day workflow and rapid data-to-model deployment. As one G2 reviewer wrote:

• “Vertex AI unifies the ML workflow, making data handling efficient… the UI is intuitive and flexible between no-code and custom development.”

Only a small share rated their experience negatively, mostly pointing to documentation issues and setup friction. In this regard, a Reddit user shared:

• “Vertex API is one of the most challenging to navigate; documentation and support are sorely lacking compared to AWS.”

How Does Vertex AI Compare?

The above image clearly shows the Comparison with AWS SageMaker, Azure ML, Databricks:

• Workflow Unification: Vertex AI and Databricks are leaders; Azure ML slightly behind.
• Data Integration: Vertex’s synergy with BigQuery is a key differentiator.
• Multimodal/GenAI: Gemini 2.0 gives Vertex a real edge over Azure ML and Databricks for rich media and generative tasks.
• Cost Efficiency: Users praise Vertex for affordable batch ops, but pricing can be high on real-time workloads.
• User Complexity: All these platforms have learning curves, but fragmentation in docs and setup is a clear negative for Vertex AI’s advanced users.

STEPS : From signing up for Vertex AI to exploring its workspace.

Step 1: Sign Up for Google Cloud

To begin, visit the Google Cloud Console and sign in or create a new account using your Google email (Gmail or other Google-linked address). New users receive $300 in free credits for exploring Google Cloud services, including Vertex AI. During sign-up, Google Cloud requires you to provide valid bank details typically a credit or debit card, or internet banking information—to verify your identity and prevent fraud. Google performs a minimal, refundable charge (such as 2 INR) to confirm your payment method, and these details are used strictly for verification and billing setup unless you upgrade beyond free credits.

Step 2: Create a Google Cloud Project

Once signed in, you'll need to create a new project in the Google Cloud Console. Projects are the organizational units for all Google Cloud resources. Give your project a meaningful name and select a region. This project will house all your Vertex AI resources, including datasets, models, and endpoints. You can create multiple projects for different teams or use cases.

Step 3: Enable Billing

Vertex AI is a paid service, so you must enable billing for your project. Link your project to a billing account by providing payment information. This step is crucial because many Vertex AI features (like training models or deploying endpoints) incur costs. Google offers a free tier with limited usage, but full access requires billing.

Step 4: Enable the Vertex AI API

After enabling billing, you need to activate the Vertex AI API for your project. This is done in the Google Cloud Console under the "APIs & Services" section. Search for "Vertex AI API" and enable it. This step grants your project access to Vertex AI's machine learning and generative AI tools. You may also need to grant specific IAM roles (like Service Usage Admin) to enable APIs if you're not the project owner.

Step 5: Explore the Vertex AI Workspace

With the API enabled, you can now access the Vertex AI workspace. The workspace is a unified dashboard where you can manage datasets, train models, deploy endpoints, and use generative AI tools. Vertex AI Studio provides a user-friendly interface for experimenting with models, building agents, and exploring AI capabilities. You can start by importing datasets, training models with AutoML, or using pre-built templates for common AI tasks.

Recent Trends & Changes (2024–2025)

• Improved Gemini 2.0 support for unified text, code, and video workflows.​
• Batch prediction costs have dropped, making large-scale experimentation more affordable.​
• More enterprise projects leveraging Vertex AI grants and incentives.​
• Slight uptick in migration to AWS for advanced, highly custom workloads due to documentation/support. 

These trends have solidified Vertex AI’s leadership in innovation and cost-effective batch operations for most use cases, but the platform is less preferred by teams with the most specialized, regulated, or complex requirements where AWS SageMaker and Azure ML still excel.

Vertex AI has solidified its place as a top-tier enterprise ML platform in 2025, excelling in unified workflows and generative AI. It is especially compelling for innovators who value Google-native cloud integrations and need to scale across modalities. If rapid deployment and accessibility matter more than deep customization or hand-holding, it’s a category leader—just be prepared to lean on community forums and documentation for support on the trickier, edge-case builds.