Google Vertex AI vs. WorkOS: ML Platform Meets Enterprise Authentication

What Google Vertex Offers

Vertex AI Agent Builder is Google’s answer to enterprise-grade agent development. It combines:

• the Agent Development Kit (ADK), an open-source framework for building agents and multi-agent systems
• Vertex AI Agent Engine, a managed runtime for deploying and scaling agents
• an ecosystem of open protocols (MCP, A2A) and connectors into Google Cloud and third-party systems  
  

The platform centers on ADK and Agent Engine. ADK is a code-first Python framework that can interoperate with OSS tools like LangChain, LangGraph, CrewAI, and LlamaIndex via integrations and templates, while Agent Engine handles deployment, scaling, observability, and memory.

You don’t have to rewrite your stack around Google-only abstractions, but you do need to understand Google Cloud concepts to run it in production.

On the model side, Vertex AI exposes a model garden with 200+ foundation models from Google and partners—Gemini, Gemma, plus models from Anthropic, Meta, Mistral, AI21 Labs, and others.  That gives you real choice of LLMs without re-architecting your deployment each time.

Security and Identity in Vertex AI

Vertex AI Agent Builder leans heavily on Google Cloud’s security primitives:

• IAM roles and service accounts govern agent access to GCP resources.
• Vertex AI Agent Engine supports VPC Service Controls, Private Service Connect, CMEK, and data residency for agents, giving you perimeter-based and key-based controls on where data can move.
• Model Armor provides runtime protections against prompt injection, sensitive data leaks, and harmful content, and can screen prompts, responses, and tool calls.
  

On the identity side, Agent Engine already has “agent identity” (preview) based on IAM, and Google has announced “Agentic IAM,” which will auto-provision agent identities across runtimes with broad credential types and end-to-end observability. That capability is coming “later this year,” not GA everywhere today.

In practice, that means: Vertex AI gives you strong in-cloud agent identity and perimeter control for workloads that live inside Google Cloud.

A2A and MCP: Multi-Agent and Data Connectivity

Google is pushing two key open standards through Vertex AI Agent Builder:

• Agent2Agent (A2A) protocol for multi-agent coordination. A2A gives agents a common way to publish capabilities (AgentCards), discover each other, and invoke each other across frameworks and vendors. Google cites 50+ partners participating in the A2A ecosystem.
• Model Context Protocol (MCP) support in ADK and Agent Builder, so agents can call MCP tools and data sources.
  

ADK supports MCP tools directly, and Agent Builder can connect agents to 100+ enterprise systems via Integration Connectors (ERP, CRM, HR, etc.), Application Integration workflows, Apigee APIs, and MCP toolchains.  

Observability, Runtime, and Compliance

Vertex AI Agent Builder and Agent Engine bring proper production controls:

• An observability layer with dashboards for token usage, latency, error rates, tool calls, and traces, integrated with Cloud Logging, Monitoring, and Trace.
• Session and Memory Bank for short- and long-term context, with guidance on using Model Armor and sandboxing for sensitive operations.
  

On compliance, Vertex AI Search and related generative services support HIPAA (when configured under a BAA), SOC 1/2/3, and ISO 2700x variants, with DRZ, VPC-SC, Access Transparency, and CMEK in the higher tiers.  For sectors like healthcare and finance, that matters.

Customer-wise, Google publicly cites organizations like Mayo Clinic and Vodafone building on Vertex AI and, specifically, using Agent Builder to search petabytes of clinical data and contracts.

Pricing and Cost Profile

Vertex AI follows Google Cloud’s usual usage-based model:

• Vertex AI Search (used heavily underneath Agent Builder patterns) runs around $1.50 per 1,000 queries for Standard edition and $4.00 per 1,000 queries for Enterprise with generative answers, with additional charges for advanced answer modes.
• Custom training nodes for Vertex AI models generally start around the low $20s per hour per node, plus storage and pipeline costs.
• GPU costs vary by region and hardware; older GPUs land well under $1/hour; newer A100/H100-class GPUs are several dollars per hour.
  

New customers typically get $300 in free credits to experiment, which is enough to get an agent prototype off the ground.  After that, you’re in pure usage-pricing land: great if you tune carefully, painful if you mis-estimate workloads or let agents run “chatty” chains.

Vertex AI vs WorkOS: Different Problems, Different Layers

At a high level:

• Vertex AI is a machine learning and agent platform: models, agent runtimes, MCP tools, A2A, observability, and in-cloud IAM/perimeter controls.
• WorkOS is authentication and authorization infrastructure: enterprise SSO, directory sync, MFA, fine-grained authorization (FGA), audit logs—and now, an OAuth 2.1 authorization server for MCP via AuthKit.
  

Vertex AI’s IAM and Agent identity features solve identity inside Google Cloud. They don’t solve integrating your B2B SaaS product with your customers’ IdPs (Okta, Entra ID, Google Workspace, etc.) or providing spec-compliant OAuth 2.1 flows for external MCP servers that live outside GCP.

WorkOS is designed for exactly that.

Imagine you’re building an AI-powered analytics SaaS:

• Vertex AI can host your agents, run retrieval, ground responses, and secure access to your GCP resources.
• Your enterprise customers still expect: SSO with their IdP, directory sync, SCIM lifecycle, audit logs, fine-grained permissions, and now OAuth 2.1 flows for MCP agents.
  

Vertex AI does not give you enterprise SSO into your application nor directory sync into your tenant model. WorkOS does.

Where Vertex Ends and WorkOS Begins

Vertex AI assumes:

• you control the GCP project
• you want to secure ML and agent workloads within that environment
• agent identity is bound to GCP IAM and VPC perimeters
  

WorkOS assumes:

• your customers control their identity providers
• you need to plug your app (and now your MCP servers) into dozens of IdPs
• you want infrastructure-agnostic auth that works on AWS, GCP, Azure, or on-prem equally well
  

For example, in a typical enterprise scenario:

• Employees must log into your B2B SaaS using their corporate SSO.
• IT needs automated provisioning/deprovisioning via SCIM.
• Security asks for per-tenant audit logs and fine-grained, document-level authorization.
• Your AI agents (possibly running in Vertex AI) are fronted by MCP servers that must require OAuth 2.1 access tokens, with Dynamic Client Registration, PKCE, and proper JWT validation.
  

Vertex AI helps immensely with the “agents running in a secure cloud perimeter” part. It doesn’t solve the customer-identity and MCP OAuth flows. That’s WorkOS.

Why WorkOS Is the Proven Choice for Enterprise Authentication (and MCP)

WorkOS gives you three major capabilities that Vertex AI does not attempt to cover:

Enterprise SSO and Directory Sync

WorkOS supports SAML and OIDC SSO against all major enterprise IdPs (Okta, Entra ID, Google Workspace, OneLogin, JumpCloud, Auth0, etc.), plus Directory Sync (SCIM) for automated user provisioning and deprovisioning.

Instead of building a different integration for each customer, you integrate once with WorkOS and let it normalize the weirdness of each IdP.

Fine-Grained Authorization (FGA)

WorkOS FGA is a Zanzibar-inspired, centralized authorization service that lets you define per-resource permissions, hierarchical org structures, sharing semantics, and complex policies (RBAC/ReBAC/ABAC hybrids) without building your own auth graph.

That gives you the “who can do what on which resource” layer your app and AI features need, independent of cloud provider.

AuthKit for MCP and AI Agents

‍WorkOS AuthKit is an OAuth 2.1-compatible authorization server specifically aligned with the MCP authorization model. It provides:You can run AuthKit in two main modes:

• Hosted auth: WorkOS manages user accounts, login UI, OAuth flows, and tokens for MCP.
• Standalone Connect: you keep your existing login system; AuthKit acts as the OAuth bridge for MCP, redirecting to your app for login and then finishing the flow.

That means you can secure MCP servers and agents even if they don’t run on GCP at all. Vertex AI’s IAM cannot do that; it’s tied to Google Cloud.

1. OAuth 2.1 flows with PKCE for MCP clients
2. Authorization Server Metadata and Protected Resource Metadata for discovery (RFC 8414, RFC 9728)
3. Dynamic Client Registration so MCP clients can self-register
4. JWT issuance and JWKS endpoints for token verification
5. Tool scopes and permissions that align with MCP resources

On top of that, WorkOS keeps pricing reasonably transparent: user management is free for up to 1M MAUs, staging is free, and SSO/SCIM are priced per-connection with published volume discounts.  That’s a different economic model than “pure usage-based compute and tokens.”

Putting It Together

Google Vertex AI and WorkOS solve fundamentally different problems in the AI security story:

• Vertex AI Agent Builder is how you build, host, and secure agents and models inside Google Cloud, with strong observability, VPC perimeters, Model Armor, and emerging Agentic IAM for agent identities.
• WorkOS is how those agents and your app connect to enterprise identity and authorization, across any cloud, with SSO, directory sync, FGA, audit logs—and now a spec-compliant OAuth 2.1 layer for MCP.

Many serious systems will use both:

• Agents and RAG pipelines run on Vertex AI Agent Builder.
• The B2B SaaS front-end, customer SSO, user lifecycle, fine-grained permissions, and MCP authorization flows all run through WorkOS.
  

The right question is not “Vertex or WorkOS?” It’s:

• “Do I need to secure ML workloads and agent infrastructure inside Google Cloud?” → Vertex AI.
• “Do I need to integrate with customer IdPs, implement fine-grained app-level permissions, and secure MCP servers and tools?” → WorkOS.
  

For B2B SaaS products where enterprise authentication and authorization are on the critical path to revenue, WorkOS is the tool that actually solves your problem. Vertex AI is the tool that powers your agents once you’ve solved that auth problem.

They’re complementary, not substitutes.