Vertex AI has a free tier with limited usage for trying the platform. Beyond that it’s usage-based, so you pay for model calls, training, runtime, and storage as you use them. The ADK itself is open-source and free; the Agent Engine runtime that hosts a deployed agent is billed.

What is Vertex AI used for?

It’s used to build, train, deploy, and manage machine learning and generative AI models, and increasingly to build and run production AI agents. Common uses include customer support agents, internal employee assistants, document and search AI, and custom ML models.

Is Vertex AI an agent builder?

It includes one. Vertex AI Agent Builder is the agent-building layer, made up of the no-code console, the code-first Agent Development Kit (ADK), and the Agent Engine runtime. The wider Vertex AI platform also covers model training and serving beyond agents.

What’s the difference between Vertex AI and Gemini?

Gemini is Google’s family of models. Vertex AI (now the Gemini Enterprise Agent Platform) is the platform you use to access those models, alongside Claude, Llama, and others, and to build and deploy applications on top of them.

Vertex AI vs. Amazon Bedrock

Both are managed platforms for building on foundation models. Vertex AI is Google Cloud’s, while Amazon Bedrock is AWS’s. The choice usually follows whichever cloud you already run.

Vertex AI vs. AWS Sagemaker

Both offer end-to-end tools for building, training, and deploying ML models. Vertex AI is integrated with Google Cloud; SageMaker is tailored for AWS.

Vertex AI vs. ChatGPT

Vertex AI is a platform for developing and deploying many kinds of ML and generative models. ChatGPT, from OpenAI, is a specific consumer-facing assistant. They solve different problems.

Google Vertex AI Tutorial: How To Build AI Agents [2026]

If you’re building AI agents inside Google Cloud, Vertex AI is the platform you’ll end up in. It started as Google’s managed home for training and serving machine learning models, and by 2026 it has become the place you build, deploy, and govern production agents too.

It also has a new name. At Google Cloud Next 2026, Google rebranded Vertex AI as the Gemini Enterprise Agent Platform and folded Vertex AI Agent Builder into it. Most people still search and say “Vertex AI,” so that’s what we’ll use here, but it’s worth knowing the branding has shifted.

This guide covers what Vertex AI is, the parts that matter for building agents, what it actually costs, and the alternatives worth weighing, including Voiceflow.

What’s Vertex AI?

Vertex AI is Google Cloud’s fully managed platform for building, deploying, and managing machine learning and generative AI models. It launched in May 2021 to pull Google’s scattered ML tools into one place, and it’s grown into a full stack for agentic AI work.

Here’s what it gives you:

End-to-end ML management. Train, tune, and deploy models in one platform, with Model Garden offering access to 200+ foundation models.
MLOps tooling. Pipelines for continuous integration and deployment of models, so a model going to production isn’t a manual handoff.
Generative AI with Gemini. Build on Google’s current Gemini family, with long context and multimodal inputs across text, image, audio, and video.
Agent building. A no-code console plus a code-first kit for building agents that reason over your data and take actions.
Specialized solutions. Pre-built options like document and search AI for teams that want to skip building from scratch.

Vertex AI Model Garden

Model Garden is where you pick a model. As of 2026 it carries 200+ options, and the lineup is no longer Google-only:

Gemini 3.5 Flash: Google’s current default, tuned for agentic execution and coding at scale.
Gemini 3.1 Pro: the heavier reasoning model for complex, multimodal tasks.
Gemini 3.1 Flash-Lite: the fastest, cheapest Gemini 3 tier.
Claude (Opus, Sonnet, Haiku): Anthropic’s Claude models are first-class citizens in Model Garden as of Cloud Next 2026.
Llama and Gemma: Meta’s open models and Google’s own open Gemma family.

That last point matters more than it sounds. Google opening Model Garden to Claude and Llama is a real shift from the Gemini-only days, though you’re still running everything inside Google Cloud.

Vertex AI Studio

Vertex AI Studio (formerly Generative AI Studio) is where you prototype against a model before writing code. Pick a model in Model Garden, open the prompt design view, and you can test prompts, compare outputs, and tune a model on your own data through a UI. It’s the fastest way to see whether a model fits your use case without standing up infrastructure first.

Comparing data-platform AI stacks? See what Snowflake AI offers.

Vertex AI Agent Builder

Agent Builder is the part most readers come here for. In 2026 it’s built around three pieces:

The Agent Development Kit (ADK). A code-first framework, available in Python, Go, Java, and TypeScript and stable at v1.0, that gives developers precise control over agent behavior, tool use, and multi-agent orchestration.
Agent Engine. The managed runtime that handles deploying and scaling your agent, so you’re not running the serving layer yourself.
A no-code console. A visual builder for teams that want to assemble an agent without writing the orchestration by hand.

Underneath, it does what you’d expect from an enterprise agent platform: retrieval-augmented generation to ground answers in your own data, strong natural-language understanding across languages, and integrations with frameworks like LlamaIndex and LangChain. The Agent2Agent (A2A) protocol, now in production, lets agents from different systems talk to each other.

The honest read: ADK is genuinely capable, but it’s a developer’s tool. If your team building agents is conversation designers and CX folks rather than engineers, factor that in.

Vertex AI Agent Use Cases

Google groups the agents you can build into four types:

Customer agents: travel booking, order management, FAQ handling.
Employee agents: onboarding, invoice processing, inventory lookups.
Knowledge agents: legal, sales, and support assistants grounded in internal docs.
Voice agents: contact center, food ordering, in-car assistants.

The common thread is the same one across every agent platform in 2026: take the repetitive, high-volume conversations off people, and route the rest to a human with context attached.

Get started

See how leading teams design, test, and deploy AI agents at scale.

What Are the Disadvantages of Vertex AI Agent Builder?

Vertex AI Agent Builder is capable, but it comes with real tradeoffs:

Google Cloud lock-in. The platform is deeply tied to Google Cloud. That’s fine if you’re already all-in on GCP, and a friction point if you’re not.
Complexity. The breadth that makes it capable also makes it heavy. There’s a learning curve even with the no-code console, and more so with ADK.
Cost at scale. Usage-based pricing across models, runtime, and supporting services can climb in ways that are hard to forecast.

None of these are dealbreakers. They’re the reasons to look at how it compares before committing.

Vertex AI Pricing

Vertex AI has a free tier with limited usage so you can try the platform at no cost. Past that, pricing is usage-based across the services you touch: model calls, training, runtime, and storage.

The biggest variable is the model. Gemini API pricing is per million tokens and varies by tier. Gemini 3.1 Flash-Lite, for example, launched at $0.25 input and $1.50 output per million tokens, while the Pro tiers cost more. On the agent side, the ADK is open-source and free to use, but the Agent Engine runtime that hosts your deployed agent is billed by usage. The practical takeaway: model your expected token volume before you commit, because that’s where the bill actually lives.

Vertex AI Competitors and Alternatives

Vertex AI isn’t the only way to build an agent, and for a lot of teams it isn’t the right one. The main alternatives are Voiceflow, Dialogflow (also Google’s, and a simpler entry point), Rasa, Amazon Lex, and IBM Watson Assistant.

Here’s how the common options compare for building a customer-facing agent:

Platform	Best for	Model approach	Channels	Notable
Voiceflow	Building, deploying, and observing agents without cloud lock-in	Model-agnostic (Gemini, Claude, GPT, or your own)	Chat and voice	Workflows + Playbooks, built-in observability and evals, SOC 2 Type 2
Vertex AI Agent Builder	Teams already all-in on Google Cloud	Model Garden (Gemini, Claude, Llama, Gemma)	Chat and voice	Code-first ADK + Agent Engine runtime; deep GCP integration
Dialogflow	Simpler conversational flows on Google	Gemini	Chat and voice	Lighter entry point than Vertex; same Google ecosystem
Rasa	Teams wanting open-source control	Bring your own / open models	Chat and voice	Self-hosted, code-first, full data control
Amazon Lex	AWS-native voice and chat bots	Amazon models	Chat and voice	Tight AWS and Amazon Connect integration
IBM Watson Assistant	Regulated enterprises on the IBM stack	watsonx plus third-party models	Chat and voice	Strong governance; heavier setup

The clearest reason to look past Vertex AI is model lock-in. Vertex keeps everything inside Google Cloud, and even with Claude and Llama in Model Garden, you’re building on Google’s stack. Voiceflow takes the opposite approach: it’s model-agnostic, so you can run Gemini, Claude, and GPT side by side and switch as cost and quality shift, without re-architecting your agent.

A few things Voiceflow gives you that a cloud-native ML platform isn’t built around:

Build the agent, not the infrastructure. A visual canvas with Workflows for the deterministic paths that must behave the same way every time, and Playbooks for open-ended reasoning.
Knowledge grounding. A Knowledge Base that answers from your docs and URLs instead of the model’s guess.
See what it’s doing. Observability gives you turn-by-turn transcripts, Evaluations score them automatically against your criteria, and Environments keep staging separate from production. If you’ve looked at standalone tools like Arize or Braintrust, the difference is that this monitoring is wired into the same place you build.
Chat and voice in one place. The same agent runs on a web widget and a phone line.
Enterprise security. SOC 2 Type 2 and PII masking, which procurement asks about early.

Teams like Turo, StubHub International, Sanlam Studios, and Trilogy build their agents this way. If you want the broader field before narrowing, our roundup of the best AI chatbots goes wider.