AI Agents or Vertex AI
Agents or Vertex?
Decide.
Vertex AI Agent Builder is at its best for companies already committed to Google Cloud, with data in BigQuery, engineers fluent in GCP, and a platform team to run it. That strength has a boundary. Access control, usage billing, grounding and the engineers who maintain it all sit inside GCP. The alternative is an agent built around the systems you already run and handed over working, wherever your customer data sits.
Quick verdict
Choose Vertex AI when you already run on Google Cloud, your data sits in BigQuery, Cloud Storage, or Google Workspace, and you have the ML or platform engineers to build agents on it and keep them governed. Its grounding on your own enterprise data, the open-source Agent Development Kit, and the managed Agent Engine runtime are real strengths on Google's turf. Pick a custom agent system if the workflow depends on data held outside Google Cloud, if nobody on staff should have to build and operate a GCP project, or if paying across several usage meters would make the bill hard to forecast at your volume. Either route can work. What settles it is where the data your agent needs actually lives.
Side by side
AI Agents vs Vertex AI at a glance.
What Vertex AI asks of your engineers, where your data has to sit, and how governance works, next to an agent built for you.
| Dimension | AI Agents | Vertex AI |
|---|---|---|
| What it is | An agent system for one workflow, on whichever models and tools suit it, built and handed over by our team. | Google Cloud's agent platform (renamed Gemini Enterprise Agent Platform in 2026). Build agents in the ADK or a low-code studio, grounded on your Google Cloud data, running Gemini and other models. |
| Who builds and runs it | We do the building, connect your systems and hand over a working agent, so nobody on your team has to learn a new platform. | Your ML or platform engineers build agents in the ADK or Agent Studio, then own the tuning, evals, IAM, and upkeep inside GCP. |
| Data and system reach | Whatever the workflow touches, on any cloud: the CRM, billing, a warehouse, internal tools, even data from outside the company. | Strongest inside Google Cloud, grounded on BigQuery, Cloud Storage, and Workspace via Vertex AI Search. Reaching outside needs connectors or MCP tools you wire up. |
| Hosting and data control | Your choice of cloud and model provider, including a private network or your own servers when compliance calls for it. | Runs on Google Cloud with GCP security and compliance (HIPAA, FedRAMP options). Data and agents live inside your GCP project. |
| Governance and evals | Tests, guardrails and a full record of what the agent did, all yours to adjust. Review steps match how much risk you can accept. | Native GCP IAM, tool governance, and grounding controls. Strong enterprise controls, governed inside Google Cloud's model. |
| Time to value | A matter of weeks, starting with the workflow closest to revenue: discovery, then the build, testing and launch. | Quick for a team already on GCP with clean data. Otherwise IAM setup, data grounding and staffing the build all come first. |
| Best fit | Companies whose data is spread across several clouds and tools, or who would rather not staff an agent build. | GCP-native orgs with data in BigQuery or Workspace and the engineers to build and govern agents. |
Vendor features and limits change frequently. Check current Vertex AI details, including plans, on the vendor’s own site before committing.
Choose AI Agents
When this path fits.
- Your infrastructure is split across clouds, or Google Cloud is not where you run at all.
- The agent would need records from your CRM, billing, a warehouse or a product database that BigQuery never sees.
- You would rather receive a working agent than open a GCP project and recruit engineers to run it.
- Testing, guardrails and review rules should belong to you and move with you if you change clouds.
Choose Vertex AI
When this path fits.
- You are already on Google Cloud. Your data lives in BigQuery, Cloud Storage, or Google Workspace.
- You have ML or platform engineers who can build in the ADK and run agents inside GCP.
- You need agents grounded on your own enterprise data with citations, and Vertex AI Search fits that job well.
- You value native GCP security and compliance (HIPAA, FedRAMP) and want governance kept inside one cloud.
- You are building complex multi-agent systems and want Google's managed runtime and wide model choice behind them.
How we would actually decide
Follow your data and your engineers.
Start with a map of where the data lives. If the records an agent needs already sit in BigQuery, Cloud Storage or Workspace, and your engineers know GCP, build on Vertex AI. If those records are spread across tools Google Cloud does not hold, or no one on staff should be running an agent platform, have the agent built.
A company that already runs on Google Cloud has a real head start. Grounding works against data Vertex can already reach, the Agent Development Kit is open source, and Agent Engine takes care of hosting. An outside team would spend weeks recreating what that company gets on day one, and in that situation we recommend Vertex without hesitation.
Many companies look different. Customer history sits in a CRM, payments in a billing tool, order status in a warehouse system, and the notes that explain a deal in apps Google Cloud never touches. Pulling each of those into GCP means connectors to build, usage meters running for runtime, memory, search and tokens, and engineers to keep all of it working. An agent wired directly into those tools, and handed over already running, avoids that detour.
We have no cloud to sell, so the answer depends only on your systems. Our AI System Plan works it out: it names the workflows where an agent would pay off and says whether Vertex or a custom build gets you there for less.
Frequently asked
AI Agents vs Vertex AI questions answered.
Is Vertex AI Agent Builder better than a custom AI agent?
It depends on your setup, not on the products. On Google Cloud, with data in BigQuery or Workspace and engineers who can build and govern agents, Vertex AI comes out ahead. A custom agent comes out ahead when the workflow needs systems outside Google Cloud, when you want a finished agent rather than a platform, or when usage billing at your volume would be hard to predict. Where the needed data lives usually settles it.
Do I need Google Cloud to use Vertex AI Agent Builder?
For the managed version, effectively yes. Agent Builder lives in Google Cloud and draws its grounding from data stored there, ideally in BigQuery, Cloud Storage or Workspace. The open-source Agent Development Kit can run elsewhere, but the hosted runtime, grounding and governance only run inside GCP. Without an existing Google Cloud footprint, a custom build is usually the shorter road to a working agent than moving onto the platform first.
Can Vertex AI agents work with systems outside Google Cloud?
They can. Connectors and registered MCP tools let a Vertex agent call services outside Google. Each connection is extra engineering, though, and grounding still starts from what GCP holds. If the CRM, billing and warehouse data matter more than anything in BigQuery, connecting an agent to them directly tends to be less work and cheaper to run.
Isn't Vertex AI now called Gemini Enterprise Agent Platform?
Yes. At Google Cloud Next 2026, Google folded Vertex AI Agent Builder and Agentspace into a single product it now calls the Gemini Enterprise Agent Platform. The pieces are the same, the Agent Development Kit, the managed runtime, grounding, and Gemini models, so most teams still shop for it as Vertex AI Agent Builder. The rename does not change the trade-off in this comparison.
How much does Vertex AI Agent Builder cost?
Google publishes current pricing on cloud.google.com, now under the Gemini Enterprise Agent Platform name, and we do not repeat Google pricing here.
Model it against a realistic month of your own volume rather than reading a single rate, because one agent request can touch several Google Cloud services at once.
How do we decide between Vertex AI and a custom build?
List the systems the agent has to read and write. When they all sit inside GCP and the data in BigQuery or Workspace is clean, Vertex already has what it needs and the ADK gives your engineers a solid base. When the list includes tools Google Cloud does not hold, or you do not want to operate a platform, a custom build reaches more of it and keeps control with you. Our AI System Plan puts that answer in writing for your own systems.
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Next step
See whether Vertex AI fits your team as it is.
The free plan checks where your data lives and what your engineers can take on, then says which path would reach production.