Google Vertex AI: Super Excellent 20 Powerful Services and Playbooks for 2025

Google Vertex AI is Google Cloud’s unified AI/ML platform, integrating data, models, and deployment into one ecosystem. In 2025 it remains one of the most feature-rich platforms for machine learning and generative AItechtarget.comcloud.google.com. Vertex AI brings together Google’s latest foundation models (like Gemini, Imagen, etc.) and end-to-end MLOps tools. It tightly integrates with Google Cloud services (BigQuery, Dataflow, Cloud Storage, Dataplex, etc.) and offers tools for everything from data preparation to responsible AI. Below we highlight key Vertex AI services and capabilities, pricing considerations, usage patterns, and how Vertex AI compares with AWS and Azure offerings.

Best Google Vertex AI Services in 2025

Vertex AI offers many specialized services. Among the standout offerings today are Generative AI Studio, Model Garden, AutoML, Pipelines, Feature Store, and Vector Search.

Generative AI Studio: Vertex AI’s Studio (Generative AI Studio) is a web-based console for prototyping and deploying generative AI applicationscloud.google.com. It supports Google’s latest multimodal LLMs (Gemini models) for text, images, code, and videocloud.google.com. In the Studio, you can design prompts, test model outputs, and iteratively tune or fine-tune models with your data. Studio also integrates with Vertex AI Extensions (BigQuery, Cloud Storage, etc.) so you can ground models in your data. This makes it easy to build chatbots, image generators, or code generators on Vertex AI without deep ML coding.

• Model Garden: A central model library of 200+ foundation models (first-party, open-source, and partner models) curated by Googlecloud.google.com. Model Garden includes Google models (Gemini LLMs, Imagen for image generation, Veo for video, Chirp for speech-to-text, etc.) and popular open models (Gemma, Llama, Mistral, Claude, etc.)cloud.google.comcloud.google.com. You can browse models, run “playspaces” to test them, then one-click customize or deploy them as Vertex AI Model endpoints. Model Garden makes it easy to discover and use high-quality pretrained models without building from scratchcloud.google.com.
• AutoML (Managed Training): Automated model training for tabular data, images, video, and text. Vertex AI’s AutoML lets you train image classifiers, object detectors, video analyzers, NLP classifiers, translation models, and tabular predictors with minimal code. For example, you can point AutoML at a Cloud Storage bucket or BigQuery table, and Vertex AI handles data splits, neural architecture selection, and hyperparameter tuning. AutoML is ideal for users with limited ML expertise. It is fully integrated: when training completes, the model is automatically registered in Vertex’s Model Registry for evaluation and deploymentcloud.google.com.
• Pipelines (MLOps Orchestration): Vertex AI Pipelines orchestrates repeatable ML workflows (data prep, training, evaluation, deployment) in a scalable, serverless waycloud.google.com. You define a pipeline as a DAG (using Kubeflow Pipelines or TFX), and Vertex AI handles compute and scaling. Pipelines includes built-in monitoring, metadata tracking, and lineage. A pipeline task can run on Vertex’s Kubernetes cluster or offload to BigQuery, Dataflow, or Dataproc for data processingcloud.google.com. For example, one pipeline might start with a Dataflow step to transform raw data, then train a model, evaluate it, and finally deploy to a Vertex endpoint. Pipelines makes CI/CD for ML possible, enabling continuous training on fresh data and automated end-to-end workflowscloud.google.comcloud.google.com.
• Feature Store: A central repository for ML features, supporting both real-time and batch scenarios. Vertex AI’s Feature Store lets you manage feature data with consistency. The legacy Feature Store provides online serving (using Bigtable or optimized clusters for low-latency lookups) and offline exporting via BigQuery. A newer Vertex AI Feature Store uses BigQuery as the source and allows storing embeddings and doing vector searches on featurescloud.google.comcloud.google.com. Feature Store integration means your training and serving pipelines use the same feature definitions and transformations, and you can monitor feature consistency and drift. For instance, you can import new data into Feature Store and use it for real-time inference in an endpoint, or export historical feature data for training.
• Vector Search (Matching Engine): A managed service for approximate nearest-neighbor search on vector embeddingscloud.google.com. You can use Vertex AI to generate high-dimensional embeddings from text, images, or other data, index them with Vector Search, and then query for similar items. This powers recommendation engines, semantic search, and RAG-based Q&A. For example, you might embed a product catalog or document corpus, then find nearest embeddings to a user’s query. The Vector Search service handles indexing, nearest-neighbor retrieval, and scaling, enabling very fast similarity search over millions of vectorscloud.google.com.

Together, these services (and others like Vertex AI Experiments, Notebooks, Hyperparameter Tuning, Model Registry, Explainable AI, etc.) form the “20 powerful” tools in the Vertex AI platform for 2025. They allow teams to prototype models quickly, scale training, and deploy intelligent applications in the cloud.

Vertex AI Pricing & Cost Control

Vertex AI is pay-as-you-go, with separate charges for training, prediction (online endpoints vs batch), and other services. Key cost factors include compute usage and data processing. In general:

• Training vs Prediction: Training jobs are billed per-second of machine time. For example, AutoML image classification trains cost ~$3.465 per node-hour, whereas running an online prediction endpoint on the same hardware costs ~$1.375 per node-hourcloud.google.com. (You only pay for training compute while the job runs; if a job fails, you aren’t charged for incomplete timecloud.google.com.) Online endpoints incur hourly charges for each machine (even if idle); you can reduce costs by undeploying models when not in usecloud.google.com.
• Online vs Batch Predictions: Batch predictions (unattended jobs) have different pricing than online endpoints. For instance, an image classification model’s batch prediction costs ~$2.22 per node-hour, slightly above the online ratecloud.google.com. Batch jobs also consume storage/IO. Choose online endpoints for low-latency real-time predictions (with continuous uptime cost) or batch for offline processing of large datasets.
• Quotas and Limits: Vertex AI enforces quotas on resources (e.g. number of GPUs/TPUs, nodes, model count). If you attempt to create very large training clusters without raising quotas, the job will failcloud.google.com. Monitor your Vertex AI quotas (via Cloud Console or the Quotas API) to avoid surprises. For example, new projects start with limited GPU hours, so plan your initial cluster sizes accordingly. If you reach a limit, request quota increases through the Google Cloud console.
• Cost Monitoring: To control costs, use Google Cloud billing exports (e.g. export Vertex AI resource usage to BigQuery) and Vertex’s built-in dashboards. You can tag workloads or use custom labels to attribute costs. Vertex AI’s Pipeline metrics can be exported to Cloud Billing BigQuery for per-pipeline cost analysiscloud.google.com. In summary, budget for training-hour vs runtime-hour (endpoints), and watch for idle endpoints or oversized clusters to optimize spending.

Capabilities & Integrations

Vertex AI integrates extensively with Google Cloud and supports modern AI capabilities:

• LLMs and Multimodal Models: Vertex AI provides access to Google’s Gemini LLMs and multimodal models through its APIs and Studio. Developers can prompt Gemini for text generation, summarization, image captioning, or code completioncloud.google.com. Vertex AI also supports tuning these models with your data. In addition, Vertex offers the open Gemma model family (e.g. CodeGemma, VisionGemma) and dozens of others in Model Gardencloud.google.com. This means you can use state-of-the-art generative models natively on Vertex AI for text, vision, audio, or video tasks.
• Embeddings & Semantic Search: Vertex AI’s Embeddings API can generate vector representations of text or images (e.g. text-embedding-3-small or image embeddings). These embeddings integrate with Vector Search to power semantic search and retrieval. For example, Vertex AI can take an arbitrary query (text or image), generate an embedding, and find nearest neighbors in an index built on your datacloud.google.com. This is key for Retrieval-Augmented Generation (RAG) and recommender systems.
• BigQuery Integration: Vertex AI is tightly integrated with BigQuery. You can use BigQuery tables as data sources for training (especially tabular ML) and as offline serving storage for Feature Store datacloud.google.com. Vertex AI can query BigQuery directly (via BigQuery ML or within Pipelines) for large-scale data preprocessing. Notably, Vertex AI Feature Store can use BigQuery as a source (the BigQuery-native feature store), enabling unified data managementcloud.google.com. In practice, this lets data analysts prepare data in BigQuery and ML engineers easily feed it into Vertex AI workflows.
• Dataflow and Dataproc: In Vertex Pipelines, you can incorporate existing Google Cloud data-processing tools. Pipeline tasks can run on Dataflow or Dataproc (Spark) for heavy data engineering stepscloud.google.com. For example, you might use Dataflow to clean and join large datasets before training. Vertex AI Experiments can also track and reproduce these pipeline steps end-to-end. This flexibility means you can leverage your existing Dataflow/Dataproc jobs in a unified ML pipeline.
• Cloud Run & Serverless: You can deploy model-backed services to Cloud Run. For instance, Google provides tutorials showing how to wrap a Vertex AI inference API behind a Cloud Run service (often in conjunction with Identity-Aware Proxy for securitycloud.google.com). This approach is useful for serving UI or REST applications that call Vertex endpoints. Additionally, Vertex AI has connectors for Cloud Functions and Eventarc, allowing event-driven pipelines (e.g. auto-start training when new data lands in Cloud Storage).
• Other Integrations: Vertex AI works with Vertex AI Workbench (managed notebooks), Cloud Storage, Cloud Pub/Sub, Cloud Logging/Monitoring, and tools like Cloud Composer. It also integrates with Vertex AI Model Registry, Explainable AI, TensorBoard for logs, and Vertex AI Vizier for hyperparameter tuning. Security-wise, it supports VPC Service Controls, customer-managed encryption keys, and IAM roles. In short, Vertex AI is built to be part of the larger Google Cloud ecosystem, making it easy to connect models with data, workflows, and apps across GCP.

How to Build on Vertex AI (Step-by-Step)

Building a model on Vertex AI typically follows these steps:

1. Project Setup: Create or select a GCP project, enable billing, and enable the Vertex AI APIcloud.google.com. Use the Cloud Console or gcloud CLI. Configure a development environment: you can use Cloud Shell or install the Google Cloud SDK and Vertex AI SDK. Make sure you have an active service account with the necessary roles. (For quick starts, Google provides Tutorials and Quickstarts) cloud.google.com.
2. Data Preparation: Gather and clean your training data. For unstructured data (images, audio, video), upload to Cloud Storage and consider using it via Cloud Storage FUSE for efficient accesscloud.google.com. For structured or tabular data, prepare a BigQuery table or CSV/Parquet files in Cloud Storage. You can also use Vertex AI Managed Datasets: create a Dataset resource in Vertex and import data from GCS or BigQuerycloud.google.com. Vertex can automatically split the data into train/validation/test. If you need labeled data, use Vertex AI’s built-in Data Labeling Service. The key is to store data where Vertex AI can access it: GCS for large files, BigQuery for tables, or Bigtable for time-series.
3. Train a Model: Choose AutoML (no-code) or custom training. For AutoML, simply select your Dataset in the Console or via the SDK and start training with a few clickscloud.google.com. Vertex handles model selection and tuning automatically. For custom training, write your training code using frameworks like TensorFlow or PyTorch. Package it in a pre-built Vertex AI container or a custom container. Then submit a Training Job to Vertex AI (specifying the container, data locations, and compute resources). Vertex will spin up the necessary compute (CPU/GPU/TPU) and run your code. You can monitor job logs in the UI or Cloud Logging. When training finishes, a Model resource is created in Vertex AI. The training service is serverless: you pay only for the compute time used.
4. Evaluate the Model: After training, inspect model metrics and outputs. Vertex AI usually generates an evaluation report (accuracy, loss, F1, confusion matrix, etc.) in the Console. You can also run test predictions via the UI or SDK to validate quality. Use tools like Vertex AI Experiments to compare different model runs and select the best one. If fairness or bias is a concern, use Vertex’s Evaluate and Explain features to check metrics across subgroups. At this stage, you might iterate: adjust the model or data and retrain (manually or via Pipelines).
5. Deploy the Model: Once satisfied, deploy the model to a Vertex AI Endpoint for serving. In the Console or via API, create an endpoint and attach the model, choosing machine types and autoscaling options. Vertex will provision an endpoint URL. You can now send online prediction requests (JSON over REST/gRPC) with low-latency responses. Alternatively, for bulk inference, use batch prediction: submit a job pointing to your input files in GCS, and Vertex will output predictions to GCS. For online endpoints, be mindful that you are billed per-hour of uptime (undeploy to stop billing)cloud.google.com.
6. Monitor & Iterate: Enable Model Monitoring to track data and prediction driftcloud.google.com. This can alert you if input data distribution changes or model quality degrades. Use Cloud Monitoring dashboards and logs for performance. If performance drops or new data comes in, automate retraining via Pipelines (e.g. nightly or triggered by data arrival). Use Vertex Experiments or Vizier to tune hyperparameters. Continually version your models in the Vertex Model Registry and update endpoints as needed.
7. Governance & Security: Throughout, use Cloud IAM to control access to Vertex resources. For compliance, consider using VPC Service Controls or customer-managed encryption. Document your model with Vertex Explainable AI or TensorBoard notes. If needed, push metadata (via Cloud Metadata APIs) to enforce audit trails. Follow Google’s Responsible AI guidelines (see next section) to ensure your generative models are safe and fair.

By following these steps—setup, prepare data, train, evaluate, deploy, and monitor—you can build production-grade ML applications on Google Vertex AI. Each step can leverage other Google Cloud tools (BigQuery for data, Dataflow for preprocessing, Dataplex for governance, etc.), all integrated under the Vertex AI platform.

Roadmap & Competitive Landscape

Generative AI & Roadmap: Google continues to evolve Vertex AI with new AI capabilities. In 2024/2025, major enhancements include improved grounding (e.g. Vertex AI can now ground LLMs with Google Search in preview) and new tooling. Cloud Next ’24 announced features like hybrid search (combining vector and keyword search), Agent Builder (for building LangChain-style agents on Vertex), and new specialized embedding modelscloud.google.comcloud.google.com. Google also launched Gemini 2.5 (now available on Vertex AI) and released new text embeddings (English and multilingual 0409 models)cloud.google.comcloud.google.com. This indicates a focus on LLM fine-tuning, retrieval-augmented generation (RAG), and AI tooling. We expect Vertex AI to keep integrating Google’s research advances (Gemini, Imagen, etc.) and to enhance MLOps (e.g. prompt management, auto-evaluation) for 2025.

Competitive Landscape: Vertex AI competes with AWS and Azure machine learning services. AWS offers SageMaker for end-to-end ML and Bedrock for foundation modelstechtarget.com. Azure has Azure Machine Learning and integrates with Azure OpenAI. According to industry analysts, Vertex AI is “arguably the most feature-rich ML platform”techtarget.com due to its advanced tools and Google’s model zoo. However, it can have a steeper learning curve and complex pricingtechtarget.com. AWS’s strength is breadth of services (and Bedrock’s multi-vendor model access), while Azure excels at hybrid cloud and Microsoft AI stack. Google’s advantages lie in data integration (tight BigQuery couplingcloud.google.com), generative AI innovations (Gemini, Imagen), and in its enterprise search/data governance through Dataplex. For example, Google’s Dataplex now automatically catalogs Vertex AI models, datasets, and feature store assetscloud.google.com and even captures pipeline lineagecloud.google.com. This unified data/AI governance is a unique Google strength in regulated environments. In summary, each platform has strengths: Vertex AI leads in generative AI and data services, AWS in infrastructure breadth, and Azure in enterprise/hybrid integration.

Responsible AI & Governance: Google emphasizes responsible AI across Vertex AI. Google’s AI Principles (published in 2018) guide Vertex AI developmentcloud.google.com. Vertex AI provides built-in features for bias detection, fairness evaluation, and content safety. The Generative AI Studio documentation includes safety filters, bias metrics, and best-practice guidelinescloud.google.comcloud.google.com. For example, Vertex AI’s Model Monitoring can detect data drift or skew, and there are tools for explainability. Dataplex integration (as noted) means all Vertex models and data are cataloged for audit. AWS and Azure also have responsible AI toolkits, but Google’s approach is backed by its research and transparent principles. Enterprises using Vertex AI can leverage Google’s governance features (Dataplex, VPC Service Controls, IAM) plus Vertex’s monitoring to build compliant AI solutions.

In conclusion, Google Vertex AI in 2025 offers a very broad platform covering modern AI needs—from pipeline automation and feature management to cutting-edge generative models and governance tools. Its integration with Google Cloud’s data stack (BigQuery, Dataplex, etc.) and the 20+ managed services make it a powerful choice. However, users must navigate its complex pricing and quotas, and weigh its offerings against AWS SageMaker/Bedrock and Azure AI based on their specific needs. Overall, Vertex AI continues to lead in bringing Google’s AI innovations and best practices to cloud machine learning.