Vertex AI is Google Cloud’s managed platform for the whole life of an AI project, from preparing data to serving predictions and answers to real users.

The plain-English definition

Machine learning, often shortened to ML, is a way of teaching computers by showing them many examples instead of writing every rule by hand. A model is the result of that teaching: a saved “brain” that can look at new data and make a prediction, such as “this email is spam” or “this photo shows a cat.” Generative AI is a newer branch where models create new content, such as text, images, code, or summaries, in response to a request called a prompt.

Vertex AI is the Google Cloud service where you do all of this. It offers ready-made models you can use right away, tools to train your own models, places to run them at any size, and ways to watch and improve them over time. It is managed, which means Google runs the servers, the special chips, and the software underneath.

The problems it solves

• Too many separate tools. Teams used to stitch together dozens of tools for data, training, deployment, and monitoring. Vertex AI brings them together with shared security and billing.
• Heavy infrastructure. Training and running big models needs expensive chips such as GPUs and TPUs. Vertex AI provides and scales them for you.
• From prototype to production. Many AI experiments never reach real users because moving them to a stable, secure service is hard. Vertex AI gives a clear path.
• Trust and control. Companies need to know who can use a model, where their data goes, and how a model is behaving. Vertex AI adds permissions, logging, and monitoring.
• Keeping up. New models appear constantly. A central place that offers many of them makes trying and switching easier.

Where is it used?

Teams use Vertex AI for customer support chatbots, document summaries, search over company knowledge, product recommendations, fraud detection, demand forecasting, image and video analysis, code assistance, and many more tasks. It suits both a startup trying a single model and a large company running hundreds of models.

Eight simple ideas explain almost everything on Vertex AI.

Training and inference

Training is the teaching phase, where a model learns from example data. It takes a lot of computing power and happens before the model is used. Inference, also called prediction or serving, is the using phase, where the trained model answers new questions. A useful memory trick: training is going to school, inference is going to work.

Prediction types

Classic ML models are built for a specific job. A classification model puts things into categories. A regression model predicts a number, such as next month’s sales. A forecasting model predicts how values change over time. A recommendation model suggests items a user may like. Generative models are different: one model can handle many tasks, depending on the prompt.

Foundation models

A foundation model is a very large model trained on huge amounts of data so that it can do many different tasks without being built for just one. The Gemini family of models from Google is an example. Because they are already trained, you can start using them in minutes with a simple request.

Prompts, tokens, and context

A prompt is the instruction and information you give a generative model. Models read and write in small pieces called tokens, which are roughly parts of words. Pricing and limits are often measured in tokens. The context window is how much text a model can consider at once, covering both your input and its answer.

Fine-tuning

Fine-tuning means giving an existing model extra training with your own examples, so it behaves more the way you want, such as using your company’s tone or understanding your special terms. It is far cheaper than training a model from nothing.

Endpoints

An endpoint is a web address that applications call to get predictions from a deployed model. Your app sends data to the endpoint and receives an answer.

Pipelines and MLOps

A pipeline is a repeatable chain of steps, such as clean data, train, test, and deploy. MLOps is the set of habits for running ML in production the way software teams run software: automated, tested, versioned, and monitored.

Grounding and RAG

Generative models can sound confident while being wrong, a problem called hallucination. Grounding means connecting the model to trusted sources, such as your own documents or search results, so that its answers are based on real information. A popular way to do this is RAG, short for retrieval-augmented generation: first find the most relevant pieces of information, then give them to the model along with the question.

Vertex AI is a family of connected services. Here is the map of the main parts.

The diagram below shows how a typical team moves from data to a live AI feature. Notice that the platform covers every stage, and that other Google Cloud services supply the data and receive the results.

flowchart LR
  D["Data in BigQuery and Cloud Storage"] --> W["Workbench and Colab Enterprise"]
  W --> TR["Training: AutoML or Custom Training"]
  MG["Model Garden: Gemini and other models"] --> TU["Tuning and Evaluation"]
  TR --> REG["Model Registry"]
  TU --> REG
  REG --> EP["Online Endpoint"]
  REG --> BP["Batch Prediction"]
  EP --> APP["Applications and Agents"]
  BP --> OUT["Results in BigQuery or Cloud Storage"]
  VS["Vector Search and RAG Engine"] --> APP
  APP --> MON["Model Monitoring and Logging"]
  MON --> PL["Pipelines trigger retraining"]
  PL --> TR
    

Walking through the picture

Data usually lives in BigQuery, Google’s analytics warehouse, or in Cloud Storage, its file storage. Data scientists explore it in Workbench and Colab Enterprise, which are managed notebook environments. A notebook is an interactive document that mixes code, results, and notes.

For classic ML, you then train a model using AutoML, which builds models with little code, or custom training, where you bring your own code and choose the chips. For generative AI, you start from Model Garden, a catalog of ready models, then test, tune, and evaluate them.

Finished models are saved in the Model Registry, a catalog with versions and labels. From there, a model can be deployed to an online endpoint for instant answers or used for batch prediction to process large piles of data at once. Vector Search and the RAG Engine supply relevant knowledge to generative apps. Model Monitoring watches live behavior, and Pipelines can trigger retraining when quality drops.

The main services at a glance

The fastest way to add AI to an application is to call a ready-made generative model.

Model Garden

Model Garden is a catalog where you find and use many models in one place. It includes Google’s own Gemini family, open models that you can run and customize, and models from partner companies, offered through the same Google Cloud account, billing, and security controls. The exact list changes often, so use the console to see what is available right now.

Vertex AI Studio

Vertex AI Studio is a web workspace where you can type prompts, upload images or documents, change settings, compare models, and see results at once. It is the best place to experiment before writing any code, like a test kitchen where you adjust a recipe by tasting.

Calling a model from your application

Once a prompt works well in Studio, your application calls the model through an API, which is a web interface for programs. Your code sends the prompt and any settings, and the model sends back its answer. You can ask for the whole answer at once or receive it word by word as it is created, called streaming, which makes chat apps feel fast.

sequenceDiagram
  participant U as User
  participant A as Application
  participant R as Retrieval Layer
  participant M as Gemini Model on Vertex AI
  participant S as Safety Filters
  U->>A: Ask a question
  A->>R: Find relevant company documents
  R-->>A: Return best matching passages
  A->>M: Send question plus passages as prompt
  M->>S: Draft an answer
  S-->>M: Check for unsafe content
  M-->>A: Return grounded answer with sources
  A-->>U: Show answer
    

Getting better answers

• Prompt design. Be clear and specific. Give the model a role, the goal, the format you want, and a few examples. Good prompts are often the cheapest way to improve quality.
• System instructions. Rules that stay in force for the whole conversation, such as “answer politely and never share internal data.”
• Settings. A setting often called temperature controls creativity. Low values give steady, predictable answers. High values give more varied ones.
• Structured output. You can ask the model to reply in a fixed format such as JSON, so other programs can use the result easily.
• Function calling. The model can decide to call a tool you define, such as “look up order status,” and your code runs the tool and returns the result. This lets a model act on real systems.
• Context caching. If you send the same long document many times, caching can reduce cost and delay by reusing it.

Multimodal abilities

Many current models are multimodal, which means they can work with more than one kind of content, such as text, images, audio, and video, in the same request. You could upload a photo of a damaged parcel and ask the model to describe the damage, or send a recording and ask for a summary.

Safety features

Vertex AI provides safety filters that check inputs and outputs for harmful content such as hate, harassment, or dangerous instructions, with settings you can adjust. These help but do not replace your own review, testing, and policies, especially for public-facing apps.

To make a model answer from your facts instead of its memory, you connect it to your data.

Why plain models are not enough

A foundation model learned from data collected up to some date. It does not know what happened yesterday, and it knows nothing private about your company, such as your return policy or your product catalog. If asked, it might guess. Grounding solves this.

Embeddings: turning meaning into numbers

An embedding is a list of numbers that captures the meaning of a piece of text, an image, or other content. Items with similar meaning get similar numbers. “How do I return a shirt?” and “What is your refund process for clothes?” use different words but land close together in number space. This lets a computer search by meaning instead of exact words.

Vector Search

Vector Search is the Vertex AI service that stores huge numbers of embeddings and finds the closest matches to a question very quickly, even among billions of items. It powers semantic search, recommendations, and the retrieval step in RAG.

The RAG recipe

Managed options

Vertex AI offers managed building blocks to reduce the work. The RAG Engine helps connect, chunk, index, and retrieve your data for generative apps. Vertex AI Search provides ready-made search and answer experiences over websites and company data. You can also ground answers using Google Search for current public information, where appropriate.

Grounding versus fine-tuning

When a ready-made model is not enough, Vertex AI helps you teach your own.

Choosing a training path

Data comes first

The quality of a model depends mostly on the quality of its data. If the data is wrong, biased, or messy, the model learns the wrong lessons. Typical data steps include collecting, cleaning, labeling (adding the correct answer to each example), and splitting into three sets. The training set teaches the model, the validation set helps tune it during development, and the test set is kept hidden until the end to give an honest score.

Hardware: CPUs, GPUs, and TPUs

A CPU is a general-purpose processor. A GPU is a processor that does many calculations at the same time, which suits the math of AI. A TPU is Google’s own custom chip made specially for AI math. Vertex AI lets you pick the machine type and the number of chips for each job and shuts them down when the job finishes, so you pay only while training runs. Chips can be in short supply, so plan ahead for large jobs.

Experiments and tracking

Vertex AI Experiments records each training run: the settings used, the data version, and the scores. Without tracking, teams quickly forget which combination gave the best result. With tracking, you can compare runs and repeat the best one. Metadata and lineage features show which data and code produced each model, which is valuable for audits.

Measuring quality

Different tasks need different scores. A classification model may be measured by accuracy (share of correct answers), precision (when it says yes, how often it is right), and recall (how many of the real yes cases it catches). For a fraud detector, catching more fraud may matter more than avoiding false alarms, so choose scores that match the business goal.

A model is only valuable when real users or systems can use it. Serving is the step that makes that happen.

Online prediction

With online prediction, you deploy a model to an endpoint. Applications send a request and get an answer in a fraction of a second. You choose the machine type and the minimum and maximum number of machines, and the service autoscales between them as traffic changes. Online prediction suits live features such as fraud checks during payment or product suggestions on a page.

Batch prediction

With batch prediction, you give the model a large set of inputs, usually in Cloud Storage or BigQuery, and it processes them all and writes the results back. Nobody waits for each answer. This is cheaper and simpler for jobs such as scoring all customers overnight.

flowchart TB
  REG["Model Registry: version 3 approved"] --> DEP{"How will the model be used?"}
  DEP -->|Instant answers| EP["Deploy to online endpoint with autoscaling"]
  DEP -->|Large offline jobs| BP["Run batch prediction"]
  EP --> SPLIT["Traffic split between version 2 and version 3"]
  SPLIT --> WATCH["Watch quality and latency"]
  WATCH -->|Healthy| FULL["Send all traffic to version 3"]
  WATCH -->|Problems| BACK["Roll back to version 2"]
  BP --> RES["Results saved to BigQuery or Cloud Storage"]
    

Safe releases

When you deploy a new model version, do not switch everyone at once. An endpoint can host several versions and split traffic between them, for example ninety percent to the old version and ten percent to the new. If the new version performs well, increase its share. If not, send traffic back. This is a canary release, named after the canary birds miners once used as early warnings.

Serving generative models

For Google’s own models, you simply call the managed API, and Google runs the model for you. For many models, you pay per token. For steady, high-volume workloads where speed must be guaranteed, you can buy reserved capacity, often called provisioned throughput, so your traffic is not slowed by other users. For open models you deploy yourself, you choose machines with the right chips and tune settings such as batch size to balance speed and cost.

Performance ideas

• Right-size machines. Test with real traffic to choose the smallest machine that meets your speed goal.
• Set minimum instances when you cannot tolerate slow first requests, and accept the steady cost.
• Cache answers to repeated requests where safe.
• Reduce prompt size for generative models, since shorter prompts are faster and cheaper.
• Choose smaller models for simple tasks. A lighter model that is fast and cheap may be enough, and you can reserve the largest model for hard requests.
• Run in the same region as your data and application to reduce delay.

A model that was trained once and then forgotten will slowly go stale. MLOps keeps AI healthy.

Why models go stale

The world changes. Customers change habits, new products appear, and fraudsters invent new tricks. A model trained on last year’s data may slowly become less accurate. Two ideas describe this. Data drift means the incoming data no longer looks like the training data. Concept drift means the relationship between inputs and the right answer has changed.

Vertex AI Pipelines

Vertex AI Pipelines lets you define the steps of an ML process as a chain, using popular open tools such as Kubeflow Pipelines or TFX. Each step runs in its own container, passes its outputs to the next step, and is recorded. A typical pipeline reads fresh data, validates it, trains a model, evaluates it, and, only if the scores are good enough, registers and deploys it. You can run pipelines on a schedule or when new data arrives.

flowchart LR
  A["New data arrives"] --> B["Validate and prepare data"]
  B --> C["Train candidate model"]
  C --> D["Evaluate against current model"]
  D --> E{"Better and safe?"}
  E -->|Yes| F["Register new version"]
  F --> G["Deploy with traffic split"]
  E -->|No| H["Keep current model and alert team"]
  G --> I["Monitor in production"]
  I -->|Drift or quality drop| A
    

Model Monitoring

Model Monitoring compares the data your live model receives against the data it was trained on and raises an alert when they drift apart. It can also watch the model’s prediction patterns. Combined with real outcomes, such as whether a flagged transaction was truly fraud, it helps you decide when retraining is needed.

Evaluation of generative models

Measuring a chatbot is harder than measuring a spam detector, since there is not one right answer. Vertex AI provides evaluation tools that score generative outputs, using automatic metrics and judge models, and let you compare prompts and model versions against a set of test questions. Keep a fixed set of important test questions, run it after every change, and watch for new failures. This habit is sometimes called a regression test for AI.

Versioning and approval

Store models in the Model Registry with clear versions, labels, and notes. Add an approval step, so that a person or an automated check must sign off before a model reaches production. Keep data, code, and settings versioned too, so you can rebuild any model you shipped.

AI systems handle valuable data and can cause harm if misused. Vertex AI offers layers of protection, but you must use them well.

flowchart TB
  A["Layer 1: IAM roles and service accounts limit who can use models and data"] --> B["Layer 2: Private networking and VPC Service Controls limit data movement"]
  B --> C["Layer 3: Encryption at rest and in transit, with optional customer keys"]
  C --> D["Layer 4: Data residency choices and storage region controls"]
  D --> E["Layer 5: Safety filters, input checks, and output review"]
  E --> F["Layer 6: Audit logs, monitoring, and human oversight"]
    

Access control

IAM, short for Identity and Access Management, is Google Cloud’s permission system. Roles control who may call models, create endpoints, view training data, or approve deployments. Give each person and each application a service account, a robot identity, with only the rights it needs. This is the principle of least privilege. A chatbot service should be able to call a model, not delete datasets.

Your data and Google’s models

Google states that customer data sent to Vertex AI is not used to train its models without the customer’s permission, and that your tuned models and data stay under your control. Terms and options can change and differ by feature, so read the current data governance and terms documents, and confirm with your legal and security teams before sending sensitive data.

Network and boundary controls

You can reach Vertex AI privately without sending traffic over the public internet, using private connectivity options. VPC Service Controls draw a protective perimeter around services so data cannot easily be copied out, even by someone with valid credentials. You can also choose locations for processing and storage to meet rules about where data must stay.

Encryption

Data is encrypted in transit and at rest by default. Organizations with stricter needs can use customer-managed encryption keys for supported resources, giving them control over turning access off.

Risks specific to generative AI

• Prompt injection. Someone hides instructions inside content, such as a web page or a document, trying to trick the model into ignoring its rules or leaking data. Treat all external text as untrusted, limit what tools the model can use, and check actions before they run.
• Data leakage. Models may reveal sensitive information they were given in the prompt or in retrieved documents. Retrieve only what the current user is allowed to see, and apply document-level permissions.
• Harmful or biased output. Test with a wide range of inputs, use safety filters, and review results with diverse reviewers.
• Overreliance. People may trust confident answers too much. Show sources, label AI-generated content, and keep humans in the loop for important decisions.

Responsible AI habits

Document what each model is for, what data it learned from, and where it should not be used. Test for fairness across different groups of users. Provide a way for people to report bad outputs. Review models regularly, and plan how to switch a model off quickly if it misbehaves.

You cannot manage what you cannot see. Watch the machines, the model, and the money.

Operational metrics

Cloud Monitoring collects numbers over time. For endpoints, the most useful ones include request count, error rate, latency percentiles, CPU and accelerator use, and the number of running machines. For generative models, also track tokens used, request rate, and rejected requests.

Quality metrics

Operational health is not the same as good answers. A fast, error-free model can still be wrong. Track quality signals too: user thumbs up and down, how often people correct the output, the share of answers with valid sources, and scores from regular evaluation runs on your test questions. Model Monitoring adds drift detection for classic models.

Logging

Cloud Logging records requests and responses where you enable it. Logs of prompts and answers are extremely helpful for debugging and improving, but they may contain private data, so decide carefully what to log, who can read it, and how long to keep it. Cloud Audit Logs record who created, changed, or deleted resources and who called models.

Tracing multi-step apps

Modern AI apps often chain several steps: retrieve documents, call a model, call a tool, call the model again. When something goes wrong, you need to see which step failed or slowed down. Use tracing and a shared request identifier across steps so you can follow one user request from start to end.

Alerts worth setting

• Error rate or latency above your service goal.
• A sudden jump in token use or cost, which may mean a bug or abuse.
• Drift alerts on key input features.
• A drop in evaluation scores after a change.
• A rise in safety-filter blocks, which may signal misuse or a new kind of attack.
• Quota nearly exhausted for an important model or region.

No tool fits every job. A good architect knows the strengths, the limits, and the price drivers.

What you pay for

Vertex AI has many parts, and each is priced differently. Generative model calls are usually priced by the amount of input and output, measured in tokens, characters, or media units, with different rates for different models. Reserved capacity is billed for the commitment you make. Training and custom serving are priced by node hours, which means the machine type multiplied by the time it runs, including the chips attached. Managed services such as Vector Search and notebooks have their own charges for the capacity they keep running. Storage, data processing in BigQuery, networking, and logging add to the total. Prices and free allowances change often and differ by model, region, and feature, so always check the official pricing page before making budget decisions.

Ways to control cost

• Use the smallest model that meets your quality needs, and route hard questions only to larger ones.
• Shorten prompts, trim retrieved text, and cap the length of answers.
• Use context caching for long, repeated content.
• Use batch prediction for work that does not need instant answers.
• Scale endpoints down when idle, and delete unused endpoints and notebooks. A forgotten deployed model keeps costing money.
• Use preemptible or spot machines for training that can restart, where suitable.
• Set budgets, quotas, and alerts, and label resources so costs can be tracked per team.

Vertex AI compared with nearby choices

The trade-off in one sentence

Vertex AI trades some control and a learning curve over many services for speed, scale, security, and a single place to run AI from experiment to production.

A design pattern is a proven way to solve a repeated problem. These fit Vertex AI naturally.

Example: the model cascade

A support center receives thousands of messages each hour. Most are simple, such as “where is my order” or “reset my password.” A small, fast model classifies each message and answers the easy ones using a lookup tool. Messages that look complex, angry, or risky are sent to a larger model with more knowledge, and the hardest are passed to a human agent with a summary prepared by the model. Cost falls sharply, speed improves, and customers with serious problems still get careful help.

Anti-patterns to avoid

Small habits, repeated every day, separate a lasting AI product from a flashy demo.

Best practices

• Define success first. Write down the business goal and the measure of a good answer before you pick a model.
• Build a test set early. Collect real questions and their ideal answers, and use them for every change.
• Start simple. Begin with prompts and managed models, add retrieval, and consider tuning only after that.
• Keep data clean and versioned. Good data is the foundation, and versioning lets you reproduce results.
• Separate environments. Use different projects for development, testing, and production.
• Use least-privilege IAM with separate service accounts per application and pipeline.
• Automate with pipelines, including evaluation gates and approvals.
• Release gradually. Use traffic splits and have a fast way to roll back.
• Monitor quality, not just uptime. Track drift, evaluation scores, and user feedback.
• Protect sensitive data. Minimize what you send to models, apply permissions in retrieval, and choose regions and keys that match your rules.
• Control cost. Pick right-sized models, cache, batch, and clean up.
• Plan for model changes. Models are updated and retired. Keep prompts and tests in version control so you can move to a newer model and check that quality holds.
• Keep a human path. For serious or uncertain cases, make it easy to reach a person.

Common mistakes

Here is how teams commonly apply Vertex AI in practice.

These are common patterns described in general terms, rather than claims about any one named company’s private setup. They show how the ideas in this guide combine.

Lessons that repeat

Across these scenarios, the same lessons appear again and again: start with the simplest approach that works, ground generative answers in trusted data, test against real questions before every release, release gradually, monitor quality as well as uptime, protect sensitive data with narrow permissions, and keep people involved in important decisions.

Quick, clear answers to the questions beginners ask most often.

Gemini is a family of models. Vertex AI is the Google Cloud platform that offers those models with enterprise features such as access control, private networking, data governance, tuning, evaluation, and monitoring, alongside tools for training and serving other models.

No. You can call ready-made foundation models right away. Training or tuning is only needed when ready models, prompts, and retrieval are not enough for your task.

RAG fetches relevant documents at question time, so the model can use fresh and private facts and cite sources. Fine-tuning changes how the model behaves by training it on examples, useful for style, format, or special tasks. Use RAG for knowledge and fine-tuning for behavior.

An endpoint is a web address that serves predictions from a deployed model. Applications send requests to it and receive answers, while Vertex AI handles scaling and machines behind it.

Use batch prediction when you have many inputs and nobody needs the answers instantly, such as scoring all customers overnight. It is simpler and cheaper. Use online prediction for live, per-request answers.

Ground the model in trusted data with retrieval, give clear instructions, ask it to say when it does not know, show sources, evaluate on test questions, and keep a human review step for important uses.

Google states that customer data in Vertex AI is not used to train its models without permission. Terms can differ by feature and change over time, so confirm in the current documentation and with your own legal and security teams.

They are chips that speed up AI math. GPUs are widely used. TPUs are Google’s custom AI chips. For using managed Gemini models you need neither. For training or serving large models yourself, you choose machines with these chips.

Pick the smallest model that meets your needs, shorten prompts, cache repeated content, use batch jobs, delete idle endpoints and notebooks, and set budgets and alerts. Review spending often.

MLOps is the practice of running machine learning in production with automation, version control, testing, and monitoring, much like DevOps for software. On Vertex AI, pipelines, the Model Registry, and Model Monitoring support it.

Yes. Model Garden includes open models and models from partner companies, and you can also bring your own trained models and deploy them to endpoints. Availability changes, so check the current catalog.

Vertex AI is the school, workshop, and job placement office for AI on Google Cloud. You can borrow ready-made models, teach your own, give them access to your trusted knowledge, release them carefully to real users, and keep watching how they perform. Google runs the heavy machinery, so your team can focus on the problem to be solved.

Used with clear goals, good data, strong testing, and careful security, it lets teams move from idea to production quickly. Used carelessly, it can produce wrong answers, leak data, or create surprising bills. The ideas in this guide give you the map to avoid those traps.