Machine learning stopped being experimental in 2026 and became operational. According to Google Cloud, enterprise adoption of the Vertex AI platform keeps accelerating. The reason is simple: companies want fewer tools and clearer workflows. In 2026, over 65% of enterprise machine learning workloads on Google Cloud now run through the Vertex AI platform.

Throughout this article, we will explain what Google Vertex AI is, why Google built it, and how companies use it across engineering, product, and data science teams. You will see common use cases, from fraud detection at fintech companies to recommendation engines at e-commerce platforms, and understand why 97% of users plan to renew it.

We will also compare Vertex AI with other ML platforms, show where it works best, and where it may fall short depending on the company size or goals. Google positions Vertex AI as the unified ML platform for enterprises, but does it deliver that promise for teams moving from proof-of-concept to production?

What Is Google Vertex AI

Vertex AI is a platform from Google Cloud where companies build, train, and run machine learning models. Instead of juggling multiple separate tools, everything happens in one place: preparing data, training models, deploying them to real products, and monitoring how they perform.

The main difference from older approaches is that you don’t need to be a machine learning expert to use it. AutoML lets non-technical teams build models without writing code, while developers can still write custom code when they need more control. The platform also includes AI powered search through Vertex AI Search that works with your company’s own documents, and tools to build AI assistants through Agent Builder that can actually complete tasks, not just answer questions. These capabilities show up in customer support, internal knowledge search, content workflows, and ops automation.

Vertex AI is being built for real company budgets, not for flashy demos. One small but telling detail is pricing: from January 28, 2026, Google starts charging based on usage for parts of Agent Builder like Sessions, Memory Bank, and Code Execution.

That usually happens when a product is mature enough that teams are running it often, not just testing it once and forgetting it.

How Companies Actually Use Vertex AI

Companies use Vertex AI for seven main workflows: personalizing customer experiences, forecasting demand, detecting fraud, powering smarter search, automating internal processes, building AI agents that complete real tasks, and supporting business decisions. This is useful for teams across industries, from retail and finance to logistics and customer support.

Product personalization at scale

One of the most common use cases is personalization. Companies use machine learning models to recommend products, tailor content, or adjust pricing based on user behavior. Because everything runs inside the same platform, teams can train models on fresh data and update predictions without rebuilding systems every time customer behavior changes.

Forecasting and demand planning

Many enterprises rely on Vertex AI to forecast sales, inventory needs, or traffic patterns. Retailers and logistics teams use it to predict demand more accurately and reduce waste. Model training allows these forecasts to improve over time as new data comes in, which matters when small errors can cost millions.

Fraud detection and risk management

Financial companies use Vertex AI to spot unusual transactions and flag potential fraud before it happens. This is useful for banks and fintech platforms that need to catch suspicious activity in real time. They train models on transaction patterns, user behavior, and historical fraud data. The system learns from new fraud attempts, so detection improves continuously without manual rule updates.

Search and knowledge access

It is often used to power internal and external search. With tools like Vertex AI Search, companies connect their own documents, product catalogs, or help centers to smarter search systems. This helps employees find answers faster and customers get relevant results instead of generic keyword matches.

Automation of internal processes

Process automation is another strong use case. Finance, support, and operations teams use the platform to classify documents, route tickets, or flag anomalies. These systems run quietly in the background, saving time and reducing manual work while staying integrated with existing tools and workflows.

AI agents for customer support and operations

A growing use case in 2026 is building AI agents that handle real tasks, not just chat. Using Agent Builder, companies create assistants that can answer questions, look up internal data, trigger workflows, or update systems. These agents are used in customer support, IT help desks, and internal ops, where speed and consistency matter more than flashy responses.

Decision support for business teams

Vertex AI is also used to support human decisions rather than replace them. Sales, risk, and marketing teams use models to score leads, predict which deals are likely to close, or suggest next actions. The value here is not automation for its own sake, but giving teams clearer signals at the right moment, directly inside the tools they already use.

Google Vertex AI vs Other Machine Learning Platforms

Vertex AI competes with AWS SageMaker, Azure Machine Learning, and Databricks. The best choice depends on your company’s existing cloud infrastructure, team size, and whether you prioritize ease of use over customization. Teams already on Google Cloud get the most value, while others may find better fits elsewhere.

When people compare Vertex AI to other platforms, the first thing to understand is what problem it solves. The platform focuses on helping companies move from experiments to production with fewer moving parts. It bundles data prep, training, deployment, monitoring, and agents into one environment. This makes sense for teams that value structure and want fewer tools to manage.

AWS SageMaker

• Deeper AWS integration with S3, Lambda, CloudWatch, and Step Functions
• More infrastructure control and customization options
• Supports multi-model endpoints that host multiple models on single infrastructure
• Can scale asynchronous endpoints down to zero instances
• Steeper learning curve, requires solid AWS knowledge
• Better for teams already invested in AWS ecosystem
• More flexible but more complex to maintain

Azure Machine Learning

• Built for Microsoft ecosystem with native Azure services integration
• Strong governance tools through Azure Policy and compliance frameworks
• Better for regulated industries needing built-in audit trails
• Pricing similar to Vertex AI with compute-heavy costs
• Easier for teams already familiar with Azure tools
• Lacks native multi-model endpoint support like SageMaker

Databricks

• Dual billing (Databricks fees plus cloud infrastructure costs)
• Cloud agnostic, runs on AWS, Azure, or Google Cloud
• Best for data-heavy workloads that combine analytics and ML
• Strong Apache Spark integration for distributed computing
• Higher learning curve for teams unfamiliar with data engineering
• Pricing based on Databricks Units which can be hard to predict
• Works well for companies that need unified data and ML platform

Open source options like Kubeflow and MLflow

• Complete control over infrastructure and deployment
• No vendor lock-in, can run on premises or any cloud
• Require stronger engineering teams to build and maintain
• Lower upfront cost but higher operational overhead
• Best for teams with deep technical expertise

Where Vertex AI stands out is in teams that already work inside Google Cloud. If your data lives there and your products run there, it fits naturally into existing workflows. Training models, deploying them, and keeping them updated feels less fragmented. This is useful for companies that want reliability over constant customization.

That said, Vertex AI is not always the best choice. Smaller teams with simple needs may find it heavier than necessary. Others may prefer platforms that are cloud neutral or cheaper at small scale. Vertex AI works best when machine learning is part of daily operations, not an occasional experiment. The right platform depends less on features and more on how your team actually works.

How Teams Actually Use Vertex AI

Inside a company, Vertex AI rarely sits with one person or one team. Data teams usually start the process. They prepare datasets, clean inputs, and define what the model should learn. With the platform, this work happens close to where the data already lives, which reduces handoffs and confusion. The goal at this stage is clarity, not perfection, so other teams can build on it.

Engineering teams come next. They use the platform to train models, test them, and connect results to existing systems. This is where workflows become practical. Models are not built in isolation but plugged into products, dashboards, or internal tools. Engineers care about stability, updates, and cost control, and the system is designed to fit into that rhythm without constant rework.

Product teams stay involved throughout the process. They define what success looks like and how outputs are used. In many companies, generative AI features show up here through search tools, assistants, or content workflows that help users find answers faster or complete tasks with less friction. Product decisions guide what gets trained and when models should be adjusted based on real usage.

The biggest challenge is not the technology but the coordination. Models fail when teams work in silos or when updates happen without shared visibility. The platform helps by keeping datasets, models, and deployments in one environment with shared monitoring and version control. This matters because when one team updates a model or changes data processing, other teams see it immediately instead of discovering issues in production.

Who Vertex AI Is Really For

Vertex is useful for companies that already take data seriously and want to turn their data into working systems, not just reports. Medium and large organizations with steady data flows, clear use cases, and teams that ship products regularly tend to get the most value from the platform. In these environments, it helps keep machine learning organized and predictable, which matters when models are tied to revenue, operations, or customer experience.

The platform makes the most sense when machine learning is not a side project. Companies running personalization, forecasting, search, or automation at scale benefit because it reduces the friction of moving models into production and keeping them there. This is especially useful for teams that already rely on Google Cloud, since data access, security, and deployment are part of the same ecosystem instead of being bolted on later.

On the other hand, the platform is often too heavy for smaller teams. Early-stage startups, small teams, or companies with very simple models might find it heavier than necessary. In those cases, simpler tools or managed services can be enough. It shines when consistency and long-term operation matter more than speed on day one.

What Do Users Say About Vertex AI

Based on user reviews, most teams describe the platform as a relief once their team is tired of juggling separate tools. The most common praise is how it pulls training, deployment, and monitoring into one workflow, especially for teams already on Google Cloud. The most common complaint is not the idea, but the learning curve and the feeling that costs can be hard to predict until you understand what drives usage.

User experience tends to improve after the first few weeks, when teams set up a repeatable process and stop treating it like a one-off experiment. People often mention that the best part is not a single feature, but the day-to-day confidence that models can be updated without breaking everything. That is when the platform starts to feel less like a project and more like a normal part of shipping product work.

According to SoftwareReviews, 97% of users plan to renew Vertex AI, with 88% likely to recommend it and 80% satisfied with cost relative to value.

That matters because renewal is where the truth usually shows up. Teams do not renew because a demo looked good, they renew because the tool became part of daily work and ripping it out would be painful. This also suggests that once companies understand pricing and set clear usage limits, they feel comfortable running the platform long-term instead of treating it like a short experiment.

Costs, Pricing, and What to Expect

Pricing is not one flat fee. It is pay for what you use, and the bill depends on volume and choices. On the platform, most costs come from compute time and request traffic, not from simply having the service turned on. The practical takeaway is that pricing becomes part of product design, because usage patterns decide the final number.

Most teams see three main buckets first: training, serving, and the quiet extras. Training cost depends on machine type and how long jobs run. Serving cost depends on whether you use real-time endpoints or batch prediction and how many requests you handle. Then come storage, logging, and data movement, which can grow over time if nobody sets limits.

If your project includes generative AI features, costs often track tokens and request volume. Long prompts, long responses, and high traffic are the classic budget killers, especially once real users show up and stop behaving like your polite test script. This is why many teams add rules early, like shorter outputs by default and caching for repeated questions. Google provides detailed pricing tables for generative services on their documentation site.

Many companies budgeting for the platform also think in cost per outcome instead of monthly guesswork. They estimate cost per training run, cost per thousand predictions, and cost per thousand chats, then set guardrails and alerts before launch.

Google begins usage-based charges for Agent Builder components like Sessions, Memory Bank, and Code Execution starting January 28, 2026. This makes it even more important to budget per conversation and per tool call, not just per model request.

Conclusion

Vertex AI is not magic and it is not meant to be. It is a practical platform built for companies that want machine learning to behave like the rest of their software stack. When teams use it with clear goals, defined limits, and real ownership, it stops feeling like an experiment and starts acting like infrastructure that quietly supports products, decisions, and operations.

The real value shows up over time. Not in the first demo, but after months of retraining models, adjusting costs, and shipping updates without drama. For companies willing to treat machine learning as a long-term capability instead of a side project, Vertex AI becomes less about tools and more about consistency, reliability, and trust in how systems behave.

FAQs

What problems does Vertex AI solve for companies?

Vertex AI helps companies move machine learning from isolated experiments into daily operations. The platform solves three main problems: fragmented tooling, operational risk from handoffs, and difficulty maintaining models over time. Instead of juggling separate tools for data prep, training, deployment, and monitoring, teams work inside one platform. This reduces handoffs, lowers operational risk, and makes it easier to keep models updated over time. For many teams, the biggest win is not speed, but predictability and the ability to run machine learning reliably inside real products and workflows.

Is Google Vertex AI free?

No, Vertex AI is not free. It uses pay-as-you-go pricing based on compute time, model serving, and data storage. Google offers $300 in free credits for new Google Cloud customers to test services, and some features remain free during preview periods.

Is Vertex AI only for large enterprises?

No, Vertex AI works for mid-size organizations too. The platform works best for enterprises, but it is not limited to big companies. Mid-size organizations with steady data and clear use cases can benefit as well. That said, very small teams or early-stage startups may find it heavier than necessary. It makes the most sense when machine learning is expected to run continuously and support core business functions rather than quick one-off experiments.

How is Vertex AI different from building ML systems manually?

Building machine learning manually often means stitching together storage, compute, training scripts, deployment pipelines, and monitoring tools. Vertex AI consolidates these components into a single environment with unified controls. The tradeoff is less customization in some areas, but far less operational overhead. Many teams choose it because it reduces maintenance work and lets engineers focus on improving models instead of managing infrastructure.

What drives costs the most on Vertex AI?

Vertex AI costs are driven by usage, not access. Training time, model serving traffic, and data storage are the biggest factors. For text-based systems, request volume and output size matter a lot. Companies that manage costs well usually set limits early, choose batch processing where possible, and monitor usage closely. Pricing becomes manageable when it is treated as part of system design, not an afterthought.

Is Vertex AI a good choice for generative use cases?

Yes, Vertex AI supports generative systems for search, assistants, and content workflows. The key is discipline. Generative features work best when connected to real company data and monitored like any other service. Teams that plan for usage patterns, cost controls, and evaluation from day one tend to get steady results instead of surprises once real users start interacting with the system.

Origianl Creator: Paulo Palma
Original Link: https://justainews.com/blog/what-is-vertex-ai-how-companies-use-it/
Originally Posted: Tue, 20 Jan 2026 06:01:16 +0000