Vertex AI
Vertex AI is Google's umbrella platform for machine learning on Google Cloud. By 2026 it is not a single service but a large family of products that ship under one brand: managed notebooks (Workbench), custom training and hyperparameter tuning, ML pipelines, a model registry, prediction endpoints, a model discovery and deployment hub (Model Garden), feature storage, AutoML (now de-emphasized in favor of foundation-model tuning and agent building), an agent builder, and the enterprise platform for accessing, tuning, deploying, and governing generative models on Google Cloud. In 2026 Google rebranded the platform as the Gemini Enterprise Agent Platform; the SDKs, APIs, and capabilities described here carry over under that name.
The central architectural question is when Vertex AI as a platform earns its place versus lighter tooling. For a one-off experiment, Colab is faster. For pure LLM calls, the Gemini API direct is simpler. For everything between — production training pipelines, model serving with autoscaling, governed access to generative models, ML workflows the team will run for years — Vertex AI is the right default on GCP.
The Vertex AI Umbrella
The components most teams interact with:
- Workbench — managed JupyterLab instances for development.
- Training — custom training jobs, distributed training, hyperparameter tuning.
- Pipelines — ML workflow orchestration on Kubeflow Pipelines, integrated with the rest of Vertex.
- Model Registry — versioned model storage with lineage and metadata.
- Endpoints — real-time and batch prediction serving.
- Model Garden — a catalog of pre-built and third-party models you can deploy: Gemini, Llama, Claude, Mistral, open vision models, and many more.
- Agent Builder — RAG and conversational AI building blocks for production agents.
- Feature Store — online and offline feature serving for ML applications.
- AutoML — automated training for tabular, vision, and language tasks; older feature kept for non-ML teams.
Workbench — Managed Notebooks
Vertex AI Workbench provides managed JupyterLab instances with GCP-integrated auth, pre-installed ML libraries, and direct access to BigQuery, Cloud Storage, and Vertex APIs. The right place for development and exploration. Today there is a single unified offering — Workbench Instances — which replaced the earlier "user-managed" and "managed" notebook variants (both retired in 2026). For team development, Workbench is the home; Colab Enterprise is a lighter alternative for individual exploration that does not need persistent state.
Training and Hyperparameter Tuning
For training that outgrows a single notebook, Vertex AI Training runs custom jobs on managed infrastructure. Submit a container or a Python package; Vertex provisions workers, runs the job, captures logs and metrics, and tears down. Distributed training across multiple GPUs or TPUs is built in. Vizier (the hyperparameter tuning service) lets you define a search space and runs parallel trials to find better hyperparameters than manual search would.
Vertex AI Pipelines
For production ML, training cannot be a one-off notebook run — it has to be repeatable, parameterized, and auditable. Vertex AI Pipelines is the managed Kubeflow Pipelines runtime on GCP: define a pipeline as a Python DSL, each step is a containerized component, and Vertex orchestrates execution. The benefit over hand-rolled orchestration via Cloud Composer or Workflows is the native ML integrations — Pipelines knows how to register a model to the Registry, deploy to an Endpoint, run evaluation, all as standard components.
Model Registry and Endpoints
The Model Registry is the source of truth for production models: each model has versions, lineage back to the training run that produced it, metadata, and an audit trail. Without a Registry, models end up as files in Cloud Storage with version conventions in filenames, which falls apart quickly.
Endpoints serve models for real-time prediction. They autoscale, support traffic-split deployment (canary rollouts), and handle authentication via IAM. Batch prediction is the right alternative for offline scoring on large datasets — no always-on capacity, only pay for the run.
Model Garden and Generative AI on Vertex
Model Garden is the catalog: deploy Gemini, Llama, Claude, Mistral, open-source vision and NLP models, and many more — each into a Vertex endpoint with GCP-native IAM, logging, and quota. Generative AI on Vertex is the enterprise gateway to the same Gemini family that the AI Studio API exposes directly. The trade-off is detailed on the Gemini API page; in short: Vertex when governance matters, direct API when it does not.
Generative AI on Vertex AI — Gemini access through Vertex with IAM, audit logs, regional data residency, quota controls, VPC Service Controls, and integration with the rest of Vertex. The right choice for enterprise workloads where governance matters.
Gemini API (AI Studio) — direct API access with an API key. Faster to set up, simpler SDK, no Vertex overhead. The right choice for prototypes, personal projects, and apps that do not need enterprise controls.
AutoML — When It Earns Its Place
AutoML automates the model-selection-and-tuning step: upload labeled data, AutoML trains a competitive model for you. For teams without dedicated ML engineers — a business analyst who needs a tabular classifier, a small team prototyping image classification — AutoML lets them ship without learning training infrastructure. For teams with ML expertise, custom training in Vertex usually produces better models at lower cost. AutoML earns its place at the prototype-and-small-team end of the spectrum, not in mature ML organizations.
- Building custom GKE plus Python ML infrastructure for production training and serving when Vertex AI already provides exactly that, managed.
- Provisioning Workbench for one-off experiments that Colab Enterprise would run faster and cheaper.
- AutoML used when the team has ML engineers who could train better models with custom training. The convenience tax is not worth it.
- Models stored as files in Cloud Storage with version conventions in filenames, no Model Registry. Lineage and audit fall apart on the first incident.
- Real-time Endpoints provisioned at high minimum replicas when batch prediction would handle the workload at a fraction of the cost.
- Hand-rolling ML pipelines in Cloud Composer when Vertex AI Pipelines has native ML components that handle Registry, Endpoints, and evaluation as first-class steps.
- Calling Gemini through Vertex AI for a quick prototype where the AI Studio API would have shipped the demo in an afternoon.
- Vertex AI as the default platform for production ML on GCP. Custom infrastructure only when Vertex has a genuine gap.
- Workbench for development; Vertex AI Pipelines for production. The two have different roles — keep them separate.
- Model Registry as the single source of truth for production models. Every deployment traces back to a Registry version.
- Endpoints with autoscaling and a sensible minimum-replicas setting. Use batch prediction for offline scoring.
- Generative AI through Vertex AI when enterprise governance, IAM, audit, or regional residency is needed. Direct Gemini API otherwise.
- AutoML for prototypes and non-ML teams; custom training when you have ML expertise that can beat AutoML's defaults.
- Vertex AI Model Monitoring on every production endpoint — drift detection catches silent quality degradation that simple latency metrics miss.
Knowledge Check
What is Vertex AI?
- A single managed Jupyter notebook service for ML development, exploration, and interactive experimentation by data scientists
- An umbrella platform that bundles Workbench, Training, Pipelines, Model Registry, Endpoints, Model Garden, Agent Builder, and Generative AI access under one brand
- A managed LLM serving service built specifically for Gemini models, exposing them through tuned, autoscaling prediction endpoints with per-model token quotas and no support for any other model family
- An ML model marketplace where third parties list and sell pre-trained models to other Google Cloud customers for a fee
When is Vertex AI Workbench the right choice over Colab Enterprise?
- Always — Workbench is the modern replacement and Colab Enterprise has been deprecated for all enterprise notebook use on Google Cloud
- For team development that needs persistent state, customizable VMs, and tight integration with the rest of Vertex AI infrastructure
- When the workload requires a GPU or TPU runtime; Colab Enterprise supports only CPU-backed notebook instances
- When the team needs to run notebooks that query data directly from Cloud SQL or AlloyDB through native connectors
What does the Vertex AI Model Registry give you over storing trained models as files in Cloud Storage?
- Cheaper per-GB storage costs than a standard Cloud Storage bucket, since the Registry compresses and deduplicates model files across versions and tiers cold artifacts to archive automatically
- Versioned models with lineage back to the training run, metadata, audit trail, and direct integration with Endpoints for deployment
- Automatic conversion of any uploaded model framework into ONNX format for portable cross-runtime inference
- A built-in serving runtime that runs registered models for live prediction without ever deploying them to an Endpoint
When is calling Gemini through Vertex AI preferable to calling the AI Studio Gemini API directly?
- When the workload is a quick prototype or demo that needs to ship in an afternoon with minimal setup overhead
- When enterprise governance matters — IAM, audit logs, regional data residency, quota controls, VPC Service Controls
- When the model variant needed is unavailable in the AI Studio API but available in Vertex
- When the use case is multimodal with image, audio, or video; the AI Studio Gemini API accepts only plain text inputs
When does AutoML earn its place over custom training in Vertex AI Training?
- For all production workloads — AutoML consistently produces more accurate models than hand-tuned custom training does, at lower total cost
- For training workloads at petabyte scale that exceed the per-job resource limits of custom Vertex AI Training and need AutoML's automated data sharding across hundreds of worker nodes to finish in time
- For teams without ML engineers (business analysts, small teams prototyping) where convenience matters more than squeezing out the last few percentage points of model quality
- Only when the data is image data; AutoML is restricted to vision use cases and cannot train on tabular or text data
You got correct