Google Cloud Vertex AI: The Complete 2025 Guide

Google Cloud Vertex AI is Google’s unified AI platform that empowers AI developers to build and deploy generative and predictive AI models. This comprehensive guide covers Vertex AI’s tools, pricing, features, use cases, and future trends in 2025.

Google Cloud Vertex AI is a fully managed, unified platform for machine learning and generative AIcloud.google.com. It gives AI developers a one-stop environment to prepare data, train custom models or use prebuilt foundation models (like Google’s Gemini, Imagen, and others), and deploy intelligent applicationscloud.google.compromevo.com. In 2025, Vertex AI continues to lead Google Cloud’s AI portfolio with expanded generative media (video, image, speech, music) and improved MLOps tooling, supporting both predictive AI (analytics, forecasting) and generative AI workloads.

Best Google Cloud Vertex AI Tools in 2025

On the Google Cloud Vertex AI platform, developers can leverage a rich suite of tools for every stage of the ML lifecycle. Notable tools include:

• Vertex AI Workbench (Managed Notebooks): A hosted JupyterLab environment (including Colab Enterprise) for data exploration and prototyping. It integrates with BigQuery and other Google Cloud data servicescloud.google.com.
• Vertex AI Training & Prediction: Managed training on GPUs, TPUs, or CPU clusters. It automates distributed training and hyperparameter tuning, and provisions scalable endpoints for online or batch predictions. All usage is billed per second (with 30-second minimums)cloud.google.com.
• Vertex AI Pipelines: A fully managed MLOps orchestration service. Developers define pipelines (Kubeflow or TFX DAGs of preprocessing, training, and deployment steps) and Vertex AI runs them on Google’s scalable infrastructurecloud.google.comcloud.google.com.
• Vertex AI Feature Store: A managed feature repository tied to BigQuery. It lets teams create, share, and serve ML features consistently, ensuring the latest feature values are available for training and real-time predictioncloud.google.comcloud.google.com.
• Vertex AI Matching Engine: A high-scale vector similarity search service. This managed engine can index billions of embedding vectors and perform low-latency nearest-neighbor search for tasks like semantic search and recommendationscloud.google.com.
• Vertex AI Search (RAG): A Google-quality enterprise search and retrieval-augmented generation tool. It supports natural-language and keyword search over your data using pretrained embeddingscloud.google.com, and is part of Vertex AI Agent Builder.
• Vertex AI Generative Models (Model Garden): A library of foundation models and APIs. Vertex AI includes Google’s generative models (e.g. Gemini for text, Imagen for images, Chirp for speech, Veo for video, and Lyria for music) as well as many third-party and open-source modelscloud.google.comcloud.google.com.
• Vertex AI Agent Builder: A no-code console for creating AI agents (chatbots/workflows). It provides grounding and orchestration to build interactive AI assistants that use your data.
• Vertex AI Explainable AI & Monitoring: Built-in tools for model explainability and monitoring. It provides Shapley-based explainers and drift/skew detection to ensure models remain accurate over time.
• Vertex AI Model Registry: A centralized model catalog for versioning and governance, making it easy to reuse and manage ML models across teams.
• Connectors & Protocols: Pre-built connectors (Slack, Salesforce, etc.) and the new Agent2Agent (A2A) open protocol enable seamless integration of Vertex AI models and agents into enterprise applicationsblog.googlecloud.google.com.

Figure: Vertex AI Search enables advanced semantic search across enterprise datacloud.google.comcloud.google.com. It infers user intent beyond keywords (e.g. understanding “Net Zero plans” in a finance report) to retrieve and summarize relevant informationcloud.google.com. This RAG approach accelerates workflows in data-intensive fields like finance and legal.

Google Cloud Vertex AI Pricing & Plans in 2025

Vertex AI uses pay-as-you-go pricing with no upfront costs and per-second billingcloud.google.com. Key points include:

• Free Credits & Tiers: New Google Cloud accounts get $300 in free credits to spend on Vertex AI and other Google Cloud servicescloud.google.comcloud.google.com. Many Vertex AI features also have monthly free usage limits under Google’s Free Tier.
• Infrastructure Billing: Compute is billed by the second (with 30-second minimums)cloud.google.com. For example, GPUs and TPUs used in training or endpoints are charged only for active compute seconds.
• Generative AI Costs: Using Vertex AI’s generative models is charged per output character. Text, chat, and code generation can cost as little as $0.0001 per 1,000 characterscloud.google.com. (Higher-resolution image/video generation or larger models carry higher rates.)
• Training & Prediction Rates: Training custom models incurs standard compute rates (e.g. a few dollars per GPU-hour). Deploying an endpoint has a per-node hourly fee (often around $0.75–1.00 per node-hour). Batch prediction is charged by compute and data volume.
• AutoML & Forecast: Vertex AI’s AutoML services have fixed pricing. For example, Vertex AI Forecast training is $21.25 per hourcloud.google.com. Each deployed model (even if idle) incurs a deployment cost, so undeploying unused endpoints avoids charges.
• Cost Optimization: Vertex AI supports Spot VMs (preemptible instances) and committed-use discounts to reduce costs. Optimized runtime kernels and autoscaling can also lower resource usage.
• Pricing Transparency: Google publishes a detailed pricing page. Notably, Vertex AI’s costs align with legacy AI Platform and AutoML pricescloud.google.com, so users will see familiar rates. Tools like the Google Cloud Pricing Calculator can help estimate costs for specific workloads.

Google Cloud Vertex AI Features & Capabilities

Vertex AI combines Google’s latest AI research and cloud infrastructure into a unified platform. Its key capabilities include:

Figure: Vertex AI’s generative media models cover multiple content types (video: Veo, image: Imagen, speech: Chirp, and music: Lyria)cloud.google.com. By adding Lyria (a text-to-music model) and other state-of-the-art models, Vertex AI lets enterprises generate complete multimedia content from simple text promptscloud.google.com.

• Multimodal Generative AI: Vertex AI is now the only platform with generative media across video, image, speech, and musiccloud.google.com. It provides access to Google’s top generative models (Gemini) and specialized models (Imagen, Chirp, Veo, Lyria). Lyria, for example, generates custom soundtracks for marketing or media with a few text instructionscloud.google.com.
• Advanced MLOps: Vertex AI automates the ML lifecycle for predictive AI workflowscloud.google.comcloud.google.com. It offers managed pipelines for data processing, training, and deployment tasks. Developers can define a pipeline (a DAG of tasks) and Vertex AI will run it on Google’s infrastructurecloud.google.com. The integrated Feature Store ties into BigQuery, serving consistent feature data for training and real-time predictioncloud.google.com.
• Custom & AutoML Training: Vertex AI supports any ML framework (TensorFlow, PyTorch, XGBoost, etc.) in custom training jobs. It also offers AutoML for tabular, text, image, and video data, enabling no-code model building. For instance, Vertex AI Forecast streamlines time-series forecasting for business planning.
• Scalability & Performance: Under the hood, Vertex AI uses Google’s high-performance infrastructure. It can provision the latest CPUs, GPUs, and custom TPUs for both training and inference. Google’s new Ironwood TPU (optimized for inference on “thinking” AI models) and Gemini 2.5 Flash models will be integrated into Vertex AIblog.googleblog.google, promising significant performance gains.
• Security & Compliance: Enterprise security is built-in. Vertex AI ensures customer data remains private (Vertex AI Search meets HIPAA/ISO/SOC standards and Google won’t train models on your proprietary data)cloud.google.com. Google Cloud’s security features (IAM, VPC Service Controls, etc.) apply to all Vertex AI resources.
• AI Agents & Automation: Vertex AI supports AI-driven automation. Its Agent Builder lets you create autonomous agents and chatbots. The new Agent2Agent (A2A) protocol ensures these agents can interoperate and coordinate tasks across systemsblog.google, blurring the line between AI and automation in business processes.
• Search & Analytics Integration: Beyond training, Vertex AI powers intelligent search and analytics. It brings Google’s semantic search expertise to your data (as shown in the figure above), enabling AI-driven Q&A and summarization. Vertex AI also integrates with BigQuery ML and Looker, letting analysts and developers use natural-language queries and models within their workflows.
• Continuous Innovation: Google continuously adds new features. Recent additions include a Gen AI evaluation service, model optimization tools, and AI Studio features (like NotebookLM) for data-driven AI assistants. Vertex AI evolves in step with Google’s AI advances, giving users early access to capabilities.

These features make Vertex AI a comprehensive solution for AI and automation. It covers classical predictive AI needs as well as cutting-edge generative AI, all within Google Cloud’s ecosystemcloud.google.comtechtarget.com.

How to Use Google Cloud Vertex AI (Step-by-Step Guide)

Follow these steps to build, train, and deploy an AI model on Vertex AI:

1. Enable Vertex AI: In the Google Cloud Console, create or select a project and enable the Vertex AI API. Set up billing and necessary IAM permissions. (New users can apply the $300 free credit in the console.)
2. Prepare your data: Upload your dataset to Cloud Storage or BigQuery. Label any data if needed (using Vertex AI’s Data Labeling service). Clean and format the data for training.
3. Explore with Notebooks: Launch a Vertex AI Workbench notebook (JupyterLab). Use Python, BigQuery, or Cloud APIs to analyze and visualize the data. These managed notebooks come preconfigured with popular ML librariescloud.google.com.
4. Choose a model type: Decide between AutoML or custom training. For structured data or forecasting, use Vertex AI Forecast (AutoML). For images or text, consider AutoML Vision/NLP. Otherwise, write a custom training script in TensorFlow or PyTorch.
5. Run the training job: Submit a training job in Vertex AI. Specify the machine type (CPU/GPU/TPU) and your training code or data. Vertex AI will allocate compute and execute the job. Monitor progress in the console.
6. Evaluate and tune: After training, examine model metrics (accuracy, RMSE, etc.) in Vertex AI. If performance needs improvement, run a hyperparameter tuning job or adjust your model architecture and retrain.
7. Deploy the model: Deploy your model to a Vertex AI Endpoint. Choose the number and type of nodes for serving (they auto-scale with traffic). Test the endpoint by sending sample prediction requests.
8. Automate with pipelines: (Optional) Create a Vertex AI Pipeline to automate recurring tasks. For example, build a pipeline that fetches new data, retrains the model, and updates the endpoint on a schedule. Define this workflow and let Vertex AI orchestrate it.
9. Monitor the deployed model: Enable Vertex AI Model Monitoring on your endpoint. Track prediction data to detect drift or anomalies. Set up alerts so you can retrain if performance degrades.
10. Integrate into applications: Finally, use your model in your applications. Call the Vertex AI endpoint from your app code, or build a Vertex AI agent (chatbot) that uses the model to answer queries. Leverage Vertex AI Search or AI Studio to add intelligent search and generative features to your apps.

These steps illustrate a typical Vertex AI workflow. The Google Cloud Console and SDK provide guided interfaces at each stage, making it easier for AI developers to manage the end-to-end lifecyclecloud.google.comcloud.google.com.

Future of Google Cloud Vertex AI in 2025 and Beyond

Looking ahead, Google Cloud Vertex AI will evolve alongside Google’s AI innovations. Google announced that Gemini 2.5 Flash models will be offered through Google Cloud Vertex AIblog.google, and its new Ironwood TPUs (designed for inference on large models) have been introducedblog.google. These developments promise faster, more efficient AI compute for Vertex AI users.

Vertex AI’s agent and search features will also expand. Google Agentspace is being enhanced with new capabilities, and the open Agent2Agent (A2A) protocol will let Vertex AI agents coordinate across systemsblog.google. Additionally, Google is extending Vertex AI to hybrid environments: Gemini and Agentspace will soon run on Google Distributed Cloud (on-premises)blog.google, enabling enterprises with strict data requirements to use Vertex AI in their own data centers.

We can also expect deeper integration of AI and automation. Vertex AI will likely add higher-level tools for building autonomous workflows. Its predictive AI features (like forecasting and anomaly detection) will become more powerful, and integration with automation platforms (e.g. Cloud Workflows, App Engine) will grow.

Throughout 2025 and beyond, data security and trust will remain central. Google will integrate features like SynthID watermarks for AI-generated content and enhance compliance tools. Vertex AI will continue to meet stringent privacy and security standards (HIPAA, SOC, etc.), allowing organizations to adopt AI responsibly.

In summary, Google Cloud Vertex AI is positioned to stay at the forefront of enterprise AI. Its comprehensive tools, deep integration with Google Cloud, and continuous innovation mean that AI developers can rely on Vertex AI for both today’s predictive AI use cases and tomorrow’s generative AI breakthroughs.