Google Vertex AI vs AWS Bedrock: Cloud AI Platforms Compared (2025)
Google Vertex AI and AWS Bedrock are the two leading cloud AI platforms for enterprise. Here's an honest head-to-head comparison, where both fall short, and why enterprises are choosing a third path.
Google Vertex AI (Gemini models, end-to-end MLOps, open-source ADK, Agent Engine) and AWS Bedrock (multi-model marketplace with Claude, Llama, Titan, and others, deep AWS ecosystem integration) are the two dominant managed cloud AI platforms. Vertex AI leads for teams standardized on Google Cloud and Gemini; Bedrock leads for AWS-native organizations wanting model flexibility. Both require significant engineering investment to operationalize at enterprise scale.
This comparison covers both platforms honestly — real strengths, real limitations, and the structural gap they share that matters more than any feature difference.
Google Vertex AI vs AWS Bedrock: Architecture and Philosophy
The two platforms reflect fundamentally different design philosophies.
Google Vertex AI is built around a unified developer platform on Google Cloud. It combines model serving (Gemini-native, third-party via Model Garden), managed MLOps infrastructure (pipelines, evaluation, monitoring), and a developer toolkit (ADK, Agent Engine, Conversational Agents). The open-source Agent Development Kit (ADK) can be deployed outside GCP — a genuine architectural flexibility advantage. Google has also introduced the Agent-to-Agent (A2A) protocol as an open standard for multi-agent communication across systems.
AWS Bedrock is built around a managed model marketplace. It provides access to a wide range of foundation models from Anthropic (Claude 3.5/3.7), Meta (Llama 3), Mistral, Cohere, AI21, Stability, and Amazon Titan — all through a single API. The managed runtime removes infrastructure overhead. For AWS-native organizations, Bedrock integrates natively with Lambda, S3, DynamoDB, SageMaker, IAM, and CloudTrail, which significantly reduces integration complexity.
Neither platform is trying to solve the same problem. Vertex AI is a developer platform with model access. Bedrock is a model marketplace with developer tools layered on top.
Side-by-side comparison
| Dimension | Google Vertex AI | AWS Bedrock | Nexus |
|---|---|---|---|
| What it is | Developer AI platform on GCP. ADK (open-source), Agent Engine, Conversational Agents, Gemini models | Managed AI service on AWS. Multi-model access, Agents for Bedrock, Knowledge Bases, Guardrails | Autonomous agent platform + Forward Deployed Engineers. Production agents, not tools |
| Primary models | Gemini (native, including 1.5 Pro with 1M token context). Third-party via Model Garden | Claude 3.5/3.7, Llama 3, Mistral, Cohere, Titan, AI21 (multi-model native) | Model-agnostic. Any model. No lock-in |
| Agent-building tools | ADK (open-source, Python/Java), Agent Engine (managed runtime), Conversational Agents | Agents for Bedrock (multi-step orchestration), built-in tool use, action groups | Business teams define agents with Forward Deployed Engineer support. No code required |
| Who builds agents | Engineering teams (Python/Java, GCP expertise required) | Engineering teams (Python, AWS expertise required) | Business teams with Forward Deployed Engineer support |
| Integration breadth | 100+ connectors. Strongest in GCP/Workspace ecosystem | AWS ecosystem connectors. Lambda, S3, DynamoDB, SageMaker native | 4,000+ native integrations. CRMs, ERPs, messaging, databases, custom APIs |
| RAG/Knowledge | Vertex AI Search, Grounding with Google Search | Knowledge Bases (S3, Confluence, SharePoint, web crawl) | Built-in knowledge management across all connected systems |
| Governance | GCP IAM, VPC-SC, CMEK, audit logging, model evaluation | IAM, VPC, CloudTrail, Guardrails (content filtering, PII detection, topic blocking) | SOC 2 Type II, ISO 27001, ISO 42001, GDPR. Full audit trails. Every decision logged |
| Deployment speed | Weeks to months (GCP setup + development + testing) | Weeks to months (AWS setup + development + testing) | Days to weeks. Most POCs live in 2–6 weeks |
| Open-source component | ADK is open-source. Agent Engine is managed | No major open-source component. AWS-managed service | N/A (platform + service, not a framework) |
| Enterprise user layer | Gemini Enterprise ($30/user/month) for business users | No equivalent business-user layer. Developer-only | Business teams are the primary users |
| Pricing model | Usage-based (vCPU-hours, model tokens, connectors, storage) + per-user Gemini Enterprise fee | Usage-based (per-model token pricing; on-demand and provisioned throughput options) | Per-agent, tied to value. Not per-user. Not usage-based |
| Best for | GCP-native orgs with AI engineering teams, Gemini-first strategies | AWS-native orgs with AI engineering teams, multi-model strategies | Any enterprise needing production agents fast, without engineering dependency |
Where Google Vertex AI wins
Open-source flexibility with the ADK. Google's Agent Development Kit is open-source, Python/Java, and can be deployed outside GCP on any container runtime. This is a genuine advantage for teams that want architectural control without full platform lock-in at the framework level. ADK is available on GitHub with active community development. Bedrock's agent framework is AWS-managed only.
Gemini 1.5 Pro extended context. Gemini 1.5 Pro supports up to 1 million tokens of context — a meaningful differentiator for use cases involving long documents, extended conversation history, or large codebases. This capability is first-class on Vertex AI and not replicated by any model natively available on Bedrock. Google's official Vertex AI documentation covers Gemini model capabilities in detail.
The A2A protocol. Google's Agent-to-Agent protocol is an open standard for multi-agent communication across frameworks and vendors. If your architecture requires multiple agent systems from different providers to communicate, A2A provides a vendor-neutral foundation. AWS has no published equivalent.
Business user layer. Gemini Enterprise ($30/user/month, per Google Workspace pricing) gives non-developers access to AI capabilities and published agents inside Google Workspace. Bedrock has no equivalent for business users — it is developer-only. For organizations that need both developer tooling and a business user interface, Vertex AI covers both at additional cost.
Google Workspace integration. For organizations running Gmail, Drive, Docs, Sheets, and Calendar, native integration between Vertex AI agents and Workspace is strong. Agents can access and act on Workspace data without custom connectors.
Where AWS Bedrock wins
Multi-model access. Bedrock provides native access to models from Anthropic (Claude 3.5 Sonnet, Claude 3.7), Meta (Llama 3), Mistral, Cohere, AI21, Stability, and Amazon Titan through a single API. AWS documents all available foundation models with per-model token pricing. You can switch models without switching platforms. Vertex AI leads with Gemini and offers third-party models through Model Garden, but Bedrock's multi-model breadth is wider out of the box.
Guardrails. Bedrock's Guardrails feature provides an integrated governance layer applied across all models: content filtering, topic blocking, PII detection and redaction, grounding checks, and hallucination detection. It is structured and enforced at the API level — not a post-hoc evaluation layer. Vertex AI has safety settings and model evaluation tools, but Bedrock's Guardrails are more comprehensive as a standalone governance feature. AWS Bedrock Guardrails documentation covers the full configuration options.
AWS ecosystem depth. For organizations deeply invested in AWS (Lambda, S3, DynamoDB, SageMaker, CloudFormation, IAM, CloudTrail), Bedrock fits existing infrastructure without new tooling or credentials. According to Synergy Research Group's 2024 cloud infrastructure data, AWS holds approximately 31% of the global cloud infrastructure market — more enterprises have existing AWS investments to build on than GCP.
Knowledge Bases. Bedrock's Knowledge Bases support S3, Confluence, SharePoint, web crawlers, and custom data sources, with managed chunking, embedding, and vector storage. The Bedrock Knowledge Bases documentation shows the setup is simpler than building equivalent RAG pipelines on Vertex AI, and the integration with Agents for Bedrock is tight.
Pricing transparency. Bedrock's per-model token pricing is published and predictable: you pick a model and pay per input/output token. AWS Bedrock pricing lists on-demand and provisioned throughput options per model. Vertex AI's pricing combines vCPU-hours for Agent Engine, model token costs, code execution fees, session storage, and connector fees — total cost of a production deployment is harder to forecast. Vertex AI pricing documents each billing dimension.
Fine-tuning support. Bedrock supports custom fine-tuning for a subset of models. Organizations that need domain-specific model customization without managing GPU infrastructure have a clearer path on Bedrock than Vertex AI's equivalent options.
Vertex AI vs Bedrock: Shared Limitations
Here's where the comparison becomes more important than which platform wins on individual features.
Both require your engineering team to build, deploy, and maintain everything
Vertex AI gives developers ADK, Agent Engine, and connectors. Bedrock gives developers Agents for Bedrock, Knowledge Bases, and Guardrails. Both are toolkits. Your engineering team designs the agent architecture, writes the code, configures the infrastructure, handles the integrations, tests the edge cases, deploys to production, monitors performance, and maintains everything — indefinitely.
For enterprises with dedicated AI engineering teams and the capacity to invest months of development, this works. For the majority of enterprises where the business processes that need AI (customer onboarding, sales intelligence, compliance monitoring, support triage) are owned by operations teams competing for scarce engineering resources, it doesn't.
Both are strongest within their own cloud ecosystem
Vertex AI is strongest within GCP and Google Workspace. Bedrock is strongest within AWS. Enterprise business processes don't live in a single cloud. They span Salesforce, SAP, HubSpot, ServiceNow, Slack, Teams, WhatsApp, email, telephony, and dozens of legacy systems. Both platforms offer connectors and APIs to reach outside their ecosystems, but every external integration adds engineering complexity and ongoing maintenance burden.
Neither includes the service layer that determines success or failure
Deploying enterprise AI at scale is 10% technology and 90% organizational change. Use case identification, process design, change management, and ongoing optimization are the factors that separate successful AI deployments from expensive experiments.
Neither Vertex AI nor Bedrock includes this. Google offers partner-led professional services. AWS has solution architects and partner networks. But neither embeds engineers with your team who stay through deployment and beyond, own the outcome, and optimize continuously.
Neither has per-agent pricing tied to outcomes
Both platforms use consumption-based pricing. You pay for compute, model inference, storage, and connectors. Costs scale with usage, not with value delivered. If your agent handles 10x more interactions, your bill increases 10x — regardless of whether those interactions generated revenue or freed capacity. Enterprise budgets need predictable cost structures tied to business outcomes, not variable infrastructure bills.
Beyond Cloud AI Platforms: What's Missing
The pattern in enterprise AI adoption isn't "Vertex AI or Bedrock." It's "cloud AI toolkit or production outcome."
Some enterprises have the engineering teams and organizational capacity to turn cloud AI toolkits into production agents. For them, Vertex AI and Bedrock are solid choices — the question is which ecosystem aligns with their existing infrastructure.
But a growing number of enterprises find the toolkit approach doesn't match their reality. Engineering teams are stretched. Business processes span 20+ systems. The organizational change required to turn a toolkit into production outcomes is larger than the technology challenge. And the opportunity cost of redirecting engineering from core product work is measurable.
Orange Group (120,000+ employees, multi-billion euro telecom) had access to every cloud platform. Business teams built autonomous customer onboarding agents on Nexus. Deployed in 4 weeks across multiple European markets. 50% conversion improvement. 90% autonomous resolution.
A major European telecom (13,000+ employees, EUR 500M+ revenue) evaluated platform-based approaches for months without delivering production use cases at scale. With Nexus, they deployed a dozen agents in 12 weeks. 40% of support capacity freed across millions of interactions.
The common thread: these enterprises didn't lack technology options. They lacked the engineering bandwidth, cross-system integration capability, and organizational change capacity to turn toolkits into outcomes. Nexus provides all three through 4,000+ integrations, a business-team-first platform, and Forward Deployed Engineers who embed with your team.
How to decide
Choose Google Vertex AI if: Your infrastructure runs on GCP, you've standardized on Gemini models (particularly Gemini 1.5 Pro for long-context use cases), your AI engineering team has capacity, and you want the open-source flexibility of ADK. Strong fit if your organization also runs on Google Workspace.
Choose AWS Bedrock if: Your infrastructure runs on AWS, you want multi-model access (Claude, Llama, Mistral, Titan) through a single API, your AI engineering team has capacity, and you need Bedrock's Guardrails for structured cross-model governance. Strong fit if you're already deep in the AWS ecosystem (Lambda, S3, SageMaker).
Choose Nexus if: Your business processes span multiple systems (not just one cloud), your engineering team is already stretched, you need production agents in weeks rather than months, and you need measurable business outcomes — not a successful technical deployment. Nexus connects to 4,000+ systems and deploys to any channel. Forward Deployed Engineers handle the complexity. Business teams own the agents.
The question isn't which cloud AI toolkit is better. It's whether a cloud AI toolkit is what your enterprise actually needs.
Frequently asked questions
Is Google Vertex AI or AWS Bedrock cheaper?
It depends on workload. Bedrock's on-demand token pricing is straightforward — you pay per input/output token per model, published on the AWS Bedrock pricing page. Vertex AI bundles vCPU-hours (for Agent Engine), model token costs, code execution fees, session storage, and connector fees — each billed separately per the Vertex AI pricing documentation. For pure model inference at scale, Bedrock pricing is more predictable. For teams already in GCP using managed MLOps pipelines, Vertex AI's consolidated billing may be simpler. Both offer provisioned capacity options for predictable high-volume workloads.
Can AWS Bedrock access Google Gemini models?
Not natively. Bedrock's model marketplace includes models from Anthropic, Meta, Mistral, Cohere, AI21, Stability, and Amazon — but not Google Gemini. To use Gemini models, you use Vertex AI directly or access them via Google's API. Vertex AI does not restrict access to Google models only — third-party models are available through Model Garden — but Gemini is not available as a native Bedrock model.
How does Azure OpenAI Service compare to Vertex AI and Bedrock?
Azure OpenAI Service gives enterprises managed access to OpenAI models (GPT-4o, o3, o1) with Microsoft's enterprise security, compliance, and Azure Active Directory integration. It is the strongest option for organizations standardized on Microsoft 365 and Azure, and the only way to access OpenAI's latest models in a fully managed enterprise environment. The three-way choice typically follows cloud alignment: GCP-first teams evaluate Vertex AI, AWS-first teams evaluate Bedrock, Microsoft-first teams evaluate Azure OpenAI. Organizations spanning multiple clouds often use more than one.
Does AWS Bedrock support fine-tuning custom models?
Yes. Bedrock supports fine-tuning for a subset of its foundation models, including Amazon Titan and select Cohere and Meta Llama models. Fine-tuning jobs are managed — you provide training data (stored in S3) and AWS handles the compute. Continued pre-training is also available for some models. Not every model in the Bedrock catalog supports fine-tuning; the Bedrock customization documentation lists which models are eligible.
Which platform has better enterprise security: Vertex AI or Bedrock?
Both meet enterprise security requirements for most large organizations. Vertex AI offers VPC-SC (perimeter-level network isolation), CMEK (customer-managed encryption keys), GCP IAM, and audit logging via Cloud Audit Logs. Bedrock offers VPC endpoints (PrivateLink), AWS IAM, CloudTrail logging, and the Guardrails feature for content-level governance. The more relevant question is which platform's security model integrates with your existing controls. GCP-native teams already using VPC-SC and GCP IAM will find Vertex AI easier to govern. AWS-native teams already using IAM policies and CloudTrail will find Bedrock easier to govern. Both are suitable for regulated industries with proper configuration.
Worth exploring?
Every Nexus engagement starts with a 3-month proof of concept tied to measurable outcomes. Forward Deployed Engineers embed with your team from day one. You see results before committing.
See the full Nexus vs Google Vertex AI comparison →
