AWS Bedrock vs Vertex AI: Enterprise LLM Infra Showdown

AWS Bedrock and Vertex AI represent the same category: fully managed, cloud-native infrastructure for running large language models in enterprise production environments. Both abstract the infrastructure management of running foundation models. Both provide compliance certifications. Both offer multi-model access. Both handle the security controls that enterprise buyers require.

The difference between them is not primarily a technology difference. It is a cloud infrastructure alignment decision. Teams standardised on AWS will build on Bedrock. Teams standardised on Google Cloud will build on Vertex AI. The meaningful engineering decisions come after that alignment: which models, which compliance controls, and how to architect the agent and RAG layer on top of the managed model access.

The AWS Generative AI Adoption Index, surveying 3,739 senior IT decision-makers across nine countries, found 45% selected generative AI tools as their top budget priority in 2025. Both Bedrock and Vertex AI are positioned as the enterprise on-ramp for that investment within their respective cloud ecosystems.

What AWS Bedrock provides

Amazon Bedrock is a fully managed service that provides access to foundation models from leading AI providers through a single API, without requiring teams to manage infrastructure, model hosting, or scaling. AWS describes Bedrock as providing industry-leading security, privacy, and compliance for generative AI applications, with customer data never stored or used to train foundation models.

Model access

• Anthropic Claude series: Claude 4.5 Sonnet, Claude 4.5 Haiku, and the broader Claude family. AWS documentation cites a specific example of evaluating the trade-off between Claude 4.5 Sonnet at 87% tool selection accuracy and 1.8 second P50 latency versus Claude 4.5 Haiku at higher accuracy but slower response
• Amazon Nova: Amazon’s own foundation model family covering text, image, document, and video understanding, image and video generation, interactive speech, and code generation
• Third-party models: Meta Llama, Mistral, AI21 Labs, Stability AI, and others available through the same API. AWS engineers are strengthening Bedrock’s runtime to allow customers to run models hosted on different clouds through Bedrock’s API
• Model evaluation: Bedrock Model Evaluation supports LLM-as-a-judge for automated assessment across quality, user experience, instruction following, and safety metrics

Security and compliance

• Certifications: SOC 2, ISO, HIPAA eligible, GDPR compliance. AWS’s compliance documentation confirms Bedrock is in scope for these standards and is CSA STAR Level 2 certified
• Bedrock Guardrails blocks up to 88% of harmful content and identifies correct model responses with up to 99% accuracy via Automated Reasoning checks
• Data encryption: encrypted in transit and at rest. Optional customer-managed keys via AWS KMS
• Data residency: AWS Outposts and Local Zones allow teams to extend Bedrock to on-premises and edge locations for workloads requiring data to remain within specific geographic boundaries, per AWS’s hybrid data residency documentation
• IAM integration: access controlled through AWS Identity and Access Management with least-privilege, MFA, and role-based access support

Agentic infrastructure

• Amazon Bedrock AgentCore: generally available as of October 2025, providing VPC, AWS PrivateLink, CloudFormation, and resource tagging support for deploying AI agents with enterprise security and infrastructure automation
• Framework compatibility: AgentCore supports Strands Agents, CrewAI, LangGraph, and LlamaIndex alongside the Amazon Bedrock native agent framework

What Vertex AI provides

Vertex AI, currently transitioning to Gemini Enterprise Agent Platform, is Google Cloud’s unified AI development and deployment platform. It provides access to Google’s Gemini model family, open-source Gemma models, and third-party models through the Google Cloud infrastructure with enterprise security controls built in.

Model access

• Gemini 2.5 Pro: 1-million-token context window, advanced reasoning and coding, native multimodal understanding across text, image, audio, and video. Generally available in Vertex AI
• Gemini 2.5 Flash and Flash-Lite: optimized for latency and throughput respectively, with controllable thinking budgets and 1-million-token context. Suitable for high-volume workloads where cost efficiency matters
• Gemma open models: Google’s open-source model family including Gemma 4, Gemma 3, and Gemma 2, supporting multilingual text generation, multimodal input, and code tasks. Can be deployed and fine-tuned within Vertex AI
• Model Garden: includes third-party models and, as noted in Vertex AI release notes, now includes OpenAI’s GPT series (gpt-oss-120b and gpt-oss-20b) as Model-as-a-Service options
• Model Optimizer: automatically routes queries between Gemini model variants based on task complexity, optimising cost and quality without manual model selection in the application code

Security and compliance

• Certifications: HIPAA, SOC 2, ISO 27001, GDPR. Vertex AI Agent Engine supports HIPAA workloads with agents able to handle sensitive workloads in highly regulated industries with full confidence, per Google Cloud documentation
• VPC Service Controls: prevent data exfiltration by restricting Vertex AI API calls to within a defined VPC perimeter. Private Service Connect interface for private VPC deployment of agents
• Customer-Managed Encryption Keys: encrypt data at rest using customer-owned keys managed through Google Cloud KMS
• Data residency: Google Cloud’s data residency controls, combined with DRZ compliance requirements, allow teams to constrain where data is processed and stored
• Shared responsibility model: Google’s documented model assigns Google responsibility for infrastructure security, platform security, and regulatory compliance maintenance, while customers retain responsibility for IAM configuration, data governance, and application-level security

Agentic infrastructure

• Vertex AI Agent Engine: supports HIPAA workloads, VPC deployment, bidirectional streaming, and Agent-to-Agent (A2A) protocol as of 2025 releases
• Google’s ROI of AI Report (2025) found 88% of agentic AI early adopters on Google Cloud saw positive ROI on generative AI, per Vertex AI Agent Builder documentation

AWS Bedrock vs Vertex AI: the production comparison

Claude on Bedrock: why it changes the model selection decision

One of the most significant features of AWS Bedrock from an enterprise architecture perspective is that it provides access to Anthropic’s Claude family within the AWS compliance and security envelope. For teams already on AWS who want Claude’s performance characteristics, Bedrock eliminates the need to call out to Anthropic’s API directly, keeping all inference within the AWS security perimeter.

• Claude on Bedrock preserves Bedrock’s data handling guarantees: customer data is not used to train models, and all data handling takes place within the AWS infrastructure
• AWS documentation uses Claude 4.5 Sonnet and Claude 4.5 Haiku as reference examples for Bedrock’s LLM-as-a-judge evaluation and latency benchmarking, indicating these are the primary production models for enterprise Bedrock deployments
• For teams evaluating between Claude via Bedrock and Claude via Anthropic’s API directly, the Bedrock path adds IAM access control, CloudWatch logging, Guardrails content filtering, and the full AWS compliance certification stack. The direct Anthropic API path offers the latest model availability and tighter integration with Anthropic’s own tooling

When the cloud decides the platform

The most common pattern in enterprise LLM deployment is straightforward: teams choose the managed platform that aligns with their existing cloud infrastructure. An organization with 90% of its data and applications on AWS will choose Bedrock. An organization deeply invested in Google Cloud’s data and analytics stack will choose Vertex AI. The integration density between the LLM platform and the surrounding data infrastructure is more valuable than any model-level capability difference.

• AWS Bedrock advantage for AWS-native teams: native IAM, VPC, S3, CloudWatch, and Lambda integration. Bedrock’s Guardrails, Model Evaluation, and AgentCore all plug into existing AWS infrastructure without cross-cloud data movement
• Vertex AI advantage for Google Cloud teams: native BigQuery, Cloud Storage, and Dataflow integration. Gemini models can query and generate reports over BigQuery datasets directly. Google Workspace integration allows Gemini to act on Docs, Sheets, and Gmail within the enterprise environment
• Multi-cloud strategies: AWS engineers noted that many customers want to keep their preferred models but run them on AWS. Vertex AI’s Model Garden now includes OpenAI models. Both platforms are moving toward cross-cloud model hosting, reducing the lock-in argument but increasing the case for choosing a primary platform and using the other as a secondary option

WebOsmotic works with clients on both platforms, and the selection is almost always determined by the existing cloud infrastructure first. For clients building on AWS, we use Bedrock with Claude and Nova. For clients on Google Cloud, we use Vertex AI with Gemini. For clients with mixed infrastructure or no strong cloud preference, we evaluate based on model requirements, compliance needs, and total cost at volume.

Frequently asked questions

What is the main difference between AWS Bedrock and Vertex AI?

Both are fully managed cloud platforms for deploying LLMs in enterprise production environments. The primary difference is cloud ecosystem alignment: Bedrock is the AWS path, integrating natively with IAM, S3, CloudWatch, and AWS VPC. Vertex AI is the Google Cloud path, integrating natively with BigQuery, Cloud Storage, Google Workspace, and Google’s data services. Model access differs: Bedrock provides Anthropic Claude, Amazon Nova, Meta Llama, and others. Vertex AI provides Google Gemini 2.5, open-source Gemma models, and a growing third-party Model Garden.

Is AWS Bedrock HIPAA compliant?

Yes. AWS documentation confirms that Amazon Bedrock is HIPAA eligible, with a Business Associate Agreement available for organizations processing protected health information. Data is encrypted in transit using TLS and at rest using AWS KMS. Customer data is never stored or used to train foundation models. HIPAA compliance on Bedrock operates under the AWS Shared Responsibility Model, where AWS secures the infrastructure and the customer is responsible for application-level security controls and proper IAM configuration.

Is Vertex AI HIPAA compliant?

Yes. Vertex AI Agent Engine supports HIPAA workloads, per Google Cloud’s Agent Builder documentation. Google provides a Business Associate Agreement for HIPAA-covered workloads. Additional controls include VPC Service Controls for data exfiltration prevention, Customer-Managed Encryption Keys, and Private Service Connect for in-VPC deployment. Not all Vertex AI features meet data-at-rest commitments: teams should verify the specific services in scope for their workload against Google’s current security controls documentation.

Can I use Claude on AWS Bedrock?

Yes. Anthropic’s Claude family, including Claude 4.5 Sonnet and Claude 4.5 Haiku, is available through Amazon Bedrock. This is one of Bedrock’s most widely used model options for enterprise deployments. Using Claude through Bedrock keeps all inference within the AWS security perimeter, with Bedrock’s IAM, CloudWatch, and Guardrails controls applied. Customer data is not stored or used to train models. AWS documentation uses Claude 4.5 Sonnet and Haiku as primary reference examples in Bedrock’s evaluation and benchmarking documentation.

What is Bedrock Guardrails and why does it matter?

Bedrock Guardrails is AWS’s built-in safety and content moderation layer for foundation models. AWS documents it as blocking up to 88% of harmful content and identifying correct model responses with up to 99% accuracy via Automated Reasoning checks. It allows organizations to configure content filters, topic denials, and hallucination detection without building these controls at the application layer. For enterprise deployments in regulated industries where content safety and response accuracy are compliance requirements, Guardrails provides an auditable control layer between the model and the application.

Should teams use Bedrock or Vertex AI for multi-cloud deployments?

For multi-cloud deployments, teams typically choose a primary platform based on where the majority of their data and applications live, then use the secondary platform selectively. Both platforms are moving toward cross-cloud model hosting: AWS is building a runtime that allows models from Microsoft, OpenAI, and Google to run through Bedrock’s API, while Vertex AI’s Model Garden now includes OpenAI models. For most teams, the practical recommendation is to align the primary LLM platform with the primary cloud and route specific model needs to the secondary platform only where a compelling model-level reason exists.