GCP Vertex AI: Everything You Should Know

Google Cloud’s Vertex AI brings all the essential tools for machine learning into one place. Instead of using separate services for training, deployment, and monitoring, you can manage everything through a single interface. 

It’s designed for engineers building production systems, data scientists testing models, and teams looking to deploy their work efficiently. 

In this guide, we’ll look at what Vertex AI does, how it works, and why it’s become a useful choice for building and scaling machine learning projects on Google Cloud.

What is Vertex AI?

Vertex AI is Google Cloud's managed machine learning platform. It combines AutoML, custom training environments, and MLOps tools in one service. 

You can train models using pre-built algorithms or bring your own code in PyTorch or TensorFlow. The platform handles infrastructure provisioning, scales compute resources automatically, and provides APIs for deploying models to production endpoints.

The service is part of the GCP ecosystem alongside BigQuery for data warehousing and Cloud Storage for object storage. This integration lets you query training data directly from BigQuery, store model artifacts in Cloud Storage buckets, and manage everything through the same IAM permissions system you already use.

Why Use Vertex AI on GCP?

Running ML workloads requires compute, storage, networking, and orchestration. Building this yourself means provisioning GPU instances, setting up data pipelines, writing deployment scripts, and maintaining monitoring dashboards. Vertex AI handles these infrastructure concerns so you can focus on model development.

The platform scales from prototype to production without rewriting code. You train a model on a small dataset locally, then run the same training script on Vertex AI with TPUs and larger data. Security configurations inherit from your GCP project, giving you encryption at rest, VPC service controls, and audit logging without additional setup.

Time-to-market improves because you skip the infrastructure work. A team can go from raw data to deployed model in days instead of weeks. The unified interface also reduces context switching between tools, which speeds up iteration cycles.

Key Components & Architecture

1. Unified ML workflow (data to deployment)

Vertex AI structures work around the standard ML pipeline: ingest data, train models, evaluate results, deploy to endpoints, and monitor performance. Each step connects to the next through managed services.

You start by pointing Vertex AI to your data source. This could be CSV files in Cloud Storage, tables in BigQuery, or images in a storage bucket. The platform reads this data during training without requiring you to move it manually. 

Training jobs run on managed compute instances that spin up when needed and shut down when finished. After training completes, you deploy the model to an endpoint that serves predictions via REST API. Monitoring tools track request latency, error rates, and prediction drift automatically.

2. Model Garden and pre-trained models

Model Garden provides access to over 200 foundation models from Google and third parties. You can use Gemini for multimodal tasks, PaLM 2 for text generation, Imagen for image synthesis, or Anthropic's Claude models. 

Third-party options include Llama 3.2 and open models like Gemma. These models come pre-trained on large datasets, so you can use them immediately or fine-tune them on your specific data.

The garden also includes task-specific models for common use cases. For document classification, you can start with a pre-trained BERT model and fine-tune it on your documents. For image recognition, pre-trained vision transformers are available. This saves weeks of training time and computational cost compared to building from scratch.

3. Infrastructure backing: GPU, TPU, storage

Vertex AI runs on Google's infrastructure, which includes NVIDIA GPUs (T4, V100, A100, H100) for general-purpose training and Google's custom TPUs (v2, v3, v5e) for large-scale workloads. You select instance types based on your model size and training duration. The platform handles provisioning, so you don't manage VM images or driver installations.

Storage integrates directly with Cloud Storage and BigQuery. Training data stays in these systems during model training, which reduces data transfer costs and latency. Model artifacts save to Cloud Storage automatically, and you can version them in the Model Registry. 

Auto-scaling adjusts compute resources based on workload, preventing over-provisioning and reducing costs. Billing operates in 30-second increments rather than full-hour blocks.

Main Features of Vertex AI

1. AutoML for beginners

AutoML lets you build models without deep ML expertise. You upload a dataset, select the target variable, and it handles feature engineering, model selection, and tuning. It works with tabular data, images, text, and video.

For example, with tabular data, AutoML can predict customer churn using historical transactions, testing different algorithms and providing a trained model ready to deploy. 

For image classification, labeled images train a vision model that can classify new ones. In us-central1, tabular training costs around $21 per node hour, and image models about $3.50 per hour.

2. Custom training with open-source frameworks

For more control, you can run custom training using frameworks like PyTorch, TensorFlow, Scikit-learn, or XGBoost. You package your code in a container, specify compute needs, and Vertex AI runs the job.

This works for complex architectures, custom loss functions, or specialized models, such as transformers with custom attention or reinforcement learning setups. Machine types range from $0.22 per hour for n1-standard-4 to $35 per hour for A100 GPU nodes, with Spot VMs available for cost savings on fault-tolerant workloads.

3. Generative AI and large language models

Vertex AI gives access to Google’s foundation models, including Gemini 2.5 and PaLM 2. Gemini handles text, images, video, and code in one model. You can generate text, summarize documents, answer questions, or create embeddings for semantic search, and fine-tune models on your own data.

Vertex AI Studio provides a console for prototyping and testing generative models. You can experiment with prompts, adjust parameters, and convert between speech and text without coding. For production, the Gemini API integrates directly with applications via standard REST calls.

4. MLOps: pipelines, feature store, model registry

Vertex Pipelines orchestrates multi-step ML workflows at $0.03 per pipeline run. You define steps - preprocessing, training, evaluation, deployment - as components, which run in sequence or parallel, making retraining and reproducibility straightforward.

Feature Store manages features at scale, using BigQuery for offline storage and optimized nodes for online serving. Features are defined once and used for both training and production, preventing training-serving skew. Online nodes cost $0.30 per hour, with 200GB storage included.

Model Registry tracks model versions, metadata, and deployment history. You only pay storage and compute when deploying or running batch predictions. This makes it easy to compare versions, roll back if needed, and audit which model served predictions at any time.

How to Get Started with Vertex AI

Enable Vertex AI in your GCP project

Start by enabling the Vertex AI API in your Google Cloud project. In the Cloud Console, go to APIs & Services, search for "Vertex AI API," and click enable. New accounts get up to $300 in free credits for trying Vertex AI and other Google Cloud services.

Set up IAM permissions for users who will access Vertex AI. The predefined Vertex AI User role provides basic access for running training jobs and deploying models. For production, consider creating custom roles tailored to your team’s responsibilities.

Import data and choose a training approach

Connect your data sources. For structured data in BigQuery, reference the table directly. For files, upload CSV, JSON, TFRecord, or image datasets to Cloud Storage and provide the path.

Decide between AutoML and custom training. AutoML is suitable for standard tasks with clean datasets. Custom training is better if you need specific model architectures, complex preprocessing, or more control over the process. You can develop in notebooks using Vertex AI Workbench or Colab Enterprise.

Deploy your model and monitor it

After training, deploy the model to an endpoint. Vertex AI creates a REST API for predictions. You can set auto-scaling for traffic changes and define resource limits to control costs. Endpoint pricing starts at about $0.08 per hour for e2-standard-2 instances in us-central1.

Enable model monitoring to track performance over time. The system checks for feature drift (changes in input data) and prediction drift (changes in model outputs) at $3.50 per GB of data analyzed. Alerts can notify you when drift exceeds thresholds, signaling it may be time to retrain

Use Cases & Real-World Examples

1. Image & Video Recognition

Vertex AI can be used to detect defects in manufacturing or analyze medical images like X-rays or MRIs. In retail, it can support visual search, letting customers find products by uploading photos. 

AutoML image classification costs around $3.47 per training hour and $1.38 per deployment hour.

2. NLP and Chatbots

Vertex AI can power chatbots to handle routine customer questions and support content moderation by flagging text, images, or video that may violate policies. 

Document classification can help organize or extract key information. Agent Builder offers no-code tools for these applications.

3. Predictive Analytics & Recommendation Systems

The platform can support churn prediction, product recommendations, inventory forecasting, and time-series models for risk assessment or fraud detection. 

Vertex AI Forecast provides AutoML for time-series predictions at about $0.20 per 1,000 predictions.

Pros, Cons & Best Practices

Pros	Cons
Integrates with GCP services, reducing data movement.	Pricing can be complex and variable.
Automatically scales compute and prediction endpoints.	Steep learning curve for new users.
MLOps tools support retraining, versioning, and feature consistency.	Some GCP ecosystem lock-in; pipelines and features don’t transfer easily.
Compliance certifications for regulated environments.	AutoML has limited flexibility for complex tasks.

Advantages of Vertex AI

Vertex AI integrates with GCP services, letting you query BigQuery tables during training and store results in Cloud Storage without moving data. IAM policies are consistent across services, simplifying access control.

Training jobs use the compute you request and release it when done. Prediction endpoints can scale with traffic, and short billing increments help manage costs.

MLOps tools include Pipelines for retraining, Feature Store for consistent features, and Model Registry for version tracking. Vertex AI also meets certifications like SOC 1/2/3, ISO 27001, HIPAA, PCI DSS, and FedRAMP.

Limitations and What to Watch Out For

Pricing can be complex. Costs include compute, storage, predictions, and data transfer. For example, a 10GB tabular dataset may cost $5–20 with standard AutoML, or $30–60 on GPU instances. TPU training can cost around $5 per hour, depending on dataset size and model complexity.

The learning curve can be steep for teams unfamiliar with GCP. Knowledge of Cloud Storage, BigQuery, IAM, and networking is needed.

There is some dependence on the GCP ecosystem. Models can export to standard formats, but pipelines, monitoring configurations, and Feature Store setups may not transfer easily to other clouds.

AutoML has limited flexibility. It works best for standard tasks but does not allow full control over feature engineering or model architectures. More specialized tasks require custom training.

Best Practices for Successful Adoption

Begin with a small pilot project to get familiar with the platform. You can start with AutoML for initial experiments and switch to custom training if your needs require it.

Keep an eye on costs from the start. Use billing alerts, resource tags, and the pricing calculator to track spending.

Set up MLOps tools early. Use Pipelines for retraining, Feature Store to manage features, and Model Registry to track versions.

Use role-based access control. Give developers permissions for training, while limiting production deployment to specific team members. Use service accounts and audit logs to record changes and maintain compliance.

Getting Started

Try a small project first to get familiar with the platform. Set up access, connect your data, and run a test model. 

Use the monitoring and MLOps tools to keep track of how it performs as you go.

You can also connect with our experts to get guidance on setting up projects, managing workflows, or optimizing your models on Vertex AI.

Frequently Asked Questions

How much does Vertex AI cost for training a 10GB dataset?

Training costs depend on instance type, duration, and data complexity. For a 10GB tabular dataset using AutoML on standard compute, expect $5-20 for training that completes in 2-4 hours at $21.25 per node hour. Using GPUs (n1-highmem-8 with NVIDIA T4 at $0.94 per hour) increases costs to $30-60 but reduces training time to 30-60 minutes.

Custom training with code costs approximately $0.22 per hour for n1-standard-4 CPU instances, $2-3 per hour for GPU instances with T4 cards, and $5.18 per hour for TPU v2 single pods (8 cores). Storage for the dataset costs about $0.20 per month in standard Cloud Storage. Prediction serving adds $0.04-0.10 per 1,000 predictions depending on model complexity and endpoint configuration.

Use the GCP pricing calculator to estimate costs for your specific configuration. Training time varies based on model complexity, so start with small experiments to gauge actual costs before scaling up.

How do I migrate from AWS SageMaker to Vertex AI?

Models in TensorFlow SavedModel, PyTorch .pt, and ONNX formats can be moved from SageMaker to Vertex AI. Export your model, then import it into Vertex AI’s Model Registry. Use the AWS CLI or Cloud Console transfer jobs to move data to Cloud Storage; egress fees apply.

Code changes are needed due to API differences. SageMaker estimators map to Vertex AI training jobs, but syntax differs. Pipelines require rewriting from SageMaker Pipelines or Step Functions to Vertex Pipelines. Feature engineering may need to be adjusted, and BigQuery can replace Data Wrangler for offline storage.

Other considerations include IAM role mapping, endpoint configurations (Vertex AI doesn’t support scale-to-zero for most deployments), and monitoring setups. Typical migrations, including testing, may take 2-4 weeks.

How long does it take to train a model on Vertex AI vs local?

TPUs can be 10-100x faster than local GPUs for models suited to TPU architecture. For instance, a BERT model that takes 8 hours on a local RTX 3090 may finish in 30–45 minutes on a Cloud TPU v3-8 pod.

Speed depends on hardware, parallelization, and data transfer. Local training avoids network latency but lacks multi-GPU or TPU parallelism. For large datasets, uploading to Cloud Storage can take 1–3 hours, which affects one-off training.

For small datasets or quick experiments, local training is convenient. Use Vertex AI when models take longer than ~30 minutes locally or require resources beyond local memory. TPU v2 pods cost around $27.60 per hour.

What's the minimum team size needed to manage Vertex AI?

A single developer can run AutoML projects for simple tasks, though some learning of GCP and Vertex AI is required. Vertex AI Studio’s no-code interface can reduce setup time for generative AI projects.

For custom training and production pipelines, a small team of 2–3 is typical: one ML engineer, one data engineer, and possibly a DevOps engineer. Larger deployments with multiple models may require 5–10 people, including platform support.

Skill requirements scale with project complexity. Simple AutoML tasks need basic Python and data skills. Production systems with custom models require ML engineering experience, container knowledge, and understanding of distributed systems.

What compliance certifications does Vertex AI have?

Vertex AI holds SOC 1, SOC 2, SOC 3, and ISO 27001 certifications. For regulated industries, it is HIPAA compliant, PCI DSS certified, and holds FedRAMP authorization.

Compliance requires proper setup. HIPAA needs a Business Associate Agreement and security controls like VPC service controls and customer-managed encryption keys. PCI DSS restricts which services can be used and requires regular audits. Review your regulatory requirements before processing sensitive data.