Can Vertex AI Agent Builder use OpenAI models?

Not natively. Vertex AI is Google's platform and defaults to Gemini. You can call OpenAI APIs from a Vertex-hosted agent, but at that point you're writing an integration layer yourself.

Does Workspace Agents work with Google Cloud data?

Yes. BigQuery is a native connector. Google Workspace (Drive, Gmail, Calendar, Sheets, Docs) is deeply supported. For Google-heavy business workflows, Workspace Agents are often a cleaner fit than pulling everyone into Vertex tooling.

What about Agentspace?

Agentspace is Google's emerging multi-agent product built on Vertex. It targets enterprise search + agent orchestration. It's closer conceptually to Microsoft Copilot than to Workspace Agents, and the audience is Google Cloud-centric enterprises.

Which is cheaper at scale?

Depends on workload. Per-token Gemini pricing is competitive and often cheaper than credit-based Workspace Agent runs. But Vertex requires you to run the infrastructure, which has its own cost. For low-volume internal agents, Workspace Agents is usually cheaper all-in; for high-throughput customer-facing agents, Vertex can be cheaper — but requires engineering investment.

Can I migrate from one to the other?

Prompts translate conceptually. Tool integrations don't — you'd rebuild them. Migration cost is typically 40–60% of the original build. Better to pick the right platform up front than to migrate later.

OpenAI Workspace Agents vs Vertex AI Agent Builder

This comparison trips up a lot of teams because the product names sound similar. They're aimed at different audiences. Workspace Agents is a managed team product for ChatGPT Business and Enterprise users. Vertex AI Agent Builder is a developer toolkit inside Google Cloud for building custom agents against Gemini models. This page walks through the real differences and when each wins.

Verdict

OpenAI Workspace Agents is the right call for mid-market business teams shipping internal automation this quarter. Vertex AI Agent Builder is the right call for engineering teams building customer-facing AI features inside Google Cloud, especially when your data lives in BigQuery, Vertex AI Search, or Agentspace.

Pick Workspace Agents when

You're automating internal workflows (sales, support, finance, ops) — not building a product feature
Your team is ops or GTM, not an ML engineering team
Your data lives in best-of-breed SaaS (HubSpot, Slack, Notion) more than in BigQuery
You want to ship a working agent in days, not weeks
You're already on ChatGPT Business / Enterprise

Pick Vertex AI Agent Builder when

You're building an AI feature into a customer-facing product
Your data stack is Google Cloud: BigQuery, Cloud Storage, Vertex AI
You want choice over Gemini models (Pro, Flash) and deep Vertex AI Search integration
You have engineers and want SDK-level control
Your procurement strongly prefers GCP as a single vendor

Side-by-side

Dimension	OpenAI Workspace Agents	Vertex AI Agent Builder
Audience	Business teams (sales, support, finance, ops)	Developers building product features
Interface	No-code authoring inside ChatGPT	SDK + console (Python, Java), code-level
Models	Latest OpenAI models including Codex	Gemini family (Pro, Flash)
Deployment	OpenAI-hosted inside ChatGPT workspace	Google Cloud (Cloud Run, Vertex endpoints)
Primary integrations	Google Workspace, Microsoft 365, Slack, HubSpot, Salesforce, GitHub, Linear, Notion, BigQuery	BigQuery, Cloud Storage, Vertex AI Search, Agentspace, GCP services
Pricing model	Credit-based on top of ChatGPT Business/Enterprise seats	Per-token model usage + Google Cloud compute + storage
Governance	Admin controls in ChatGPT workspace (connectors, audit logs)	Google Cloud IAM + VPC Service Controls + audit logging
Time to first agent	3–5 days for a scoped workflow	2–6 weeks for a production-ready custom agent

Different layers of the stack

This is the key insight. Vertex AI Agent Builder is closer to a framework like LangChain than to Workspace Agents — it gives developers the primitives to build agents. Workspace Agents is a finished product sitting on top of primitives. Comparing them feels natural because of the names, but operationally they solve different problems at different layers. Teams often end up using both: Workspace Agents for internal ops, Vertex for customer-facing product features.

When Google Cloud depth matters

If your data warehouse is BigQuery, your unstructured content lives in Cloud Storage, and your team already runs Dataflow pipelines, Vertex AI Agent Builder's integration depth is genuinely hard to beat. Workspace Agents can read BigQuery via the native connector but you won't get Vertex AI Search's embedding-backed retrieval or Agentspace's enterprise search capabilities.

When OpenAI's authoring speed matters

The gap between 'describe the workflow' and 'the agent is running' is measured in hours on Workspace Agents and weeks on Vertex. For internal team automation where speed > sophistication, that gap is decisive. Vertex will always be more capable; Workspace Agents will usually ship first.

The honest recommendation

If a single agent is worth building internally and the main barrier is 'nobody has time to write a framework-level build,' go Workspace Agents. If you're building a product feature where the agent is part of what customers see, and your stack is GCP, Vertex is the right tool. Don't force one decision for both use cases — they're separate.

Questions

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