Can I use Claude or GPT-4 on Amazon Bedrock?

Amazon Bedrock provides access to Anthropic Claude models (Claude 3.5 Sonnet, Claude 3 Opus, Claude 3 Haiku) as first-class foundational models alongside Amazon's own Nova and Titan models, Meta's Llama, Mistral, Cohere, and AI21 models. GPT-4 is not available on Bedrock — that's an OpenAI-exclusive model. If GPT-4 is a requirement, you'd access it directly via OpenAI's API or Azure OpenAI Service rather than through Bedrock.

Which platform has better RAG support for enterprise documents?

Both platforms have mature RAG capabilities. Amazon Bedrock Knowledge Bases provides managed vector storage with OpenSearch Serverless or Pinecone integration, S3 data source sync, and hybrid search. Google Vertex AI provides similar capabilities through its Data Store agent tool with Vertex AI Search integration. For organizations with data in Google Drive, Google Cloud Storage, or BigQuery, Vertex AI's native connectors offer advantages. For organizations with data in S3 or other AWS services, Bedrock's connectors provide a more seamless path.

How does pricing compare for high-volume agent workloads?

Both platforms charge for model tokens at rates set by the underlying model provider, plus platform-level charges for managed services like vector stores, knowledge bases, and agent orchestration. At high volume, Bedrock's Provisioned Throughput option provides predictable pricing and guaranteed capacity. Vertex AI offers committed use discounts for sustained workloads. For precise cost comparison, you need to run your specific workload characteristics through each platform's pricing calculator, as the mix of models, retrieval operations, and agent executions significantly affects total cost.

Is Amazon Bedrock or Vertex AI better for regulated industries?

Both platforms support regulated industry requirements with SOC 2, ISO 27001, HIPAA eligibility, and similar certifications. Amazon Bedrock operates within AWS's extensive compliance certification portfolio, which is the deepest in the industry and covers over 140 security standards. Google Vertex AI carries Google Cloud's compliance certifications including FedRAMP High, HIPAA, and PCI DSS. For US federal government workloads, AWS GovCloud regions have a longer track record and broader FedRAMP authorization coverage. For most enterprise regulated use cases, both platforms are viable — the decision often comes down to existing cloud relationships and negotiated compliance agreements.

Digital cloud network connections representing AWS Bedrock and Google Vertex infrastructure — Photo by Solen Feyissa on Unsplash

Amazon Bedrock vs Google Vertex AI: Cloud AI Platform Comparison (2026)

Enterprise AI agent deployments increasingly happen inside existing cloud infrastructure. For organizations building on AWS, Amazon Bedrock Agents provides the natural path to managed AI agents integrated with familiar AWS services. For organizations on Google Cloud, Vertex AI Agent Builder offers equivalent managed agent capabilities within Google's platform.

But cloud affiliation isn't the only factor. Model selection, pricing structure, RAG capabilities, security posture, and developer tooling all differ between these platforms in ways that matter for production deployments. This comparison examines both platforms across the dimensions that determine which fits your organization's AI agent strategy.

For context on enterprise platform approaches, see the enterprise AI agent directory and the comparison of open-source vs commercial AI agent frameworks.

Quick Verdict#

Choose Amazon Bedrock Agents if your infrastructure lives in AWS and you need seamless integration with IAM, S3, Lambda, and the full AWS service catalog, with broad model provider choice.
Choose Google Vertex AI Agent Builder if you're on Google Cloud, need best-in-class multimodal capabilities via Gemini, or want native integration with Google Search grounding, BigQuery, and Google Workspace data sources.

Amazon Bedrock Agents Overview#

Amazon Bedrock is AWS's managed foundation model service, launched in GA in 2023 and significantly expanded through 2025. Bedrock Agents is the orchestration layer on top of Bedrock's model access — it provides a managed environment for building agents with tool use, multi-step reasoning, and knowledge base retrieval.

Core components:

Foundation Models: Access to 20+ models including Claude, Llama, Mistral, Cohere, AI21, Amazon Nova, and Amazon Titan
Agents: Managed agent runtime with action groups (tool definitions via OpenAPI spec or Lambda functions), knowledge bases (RAG), and guardrails
Knowledge Bases: Managed RAG with S3 sync, vector storage via OpenSearch Serverless, Aurora, or Pinecone, and hybrid search
Guardrails: Content filtering, PII detection, hallucination reduction, and topic avoidance
Multi-agent collaboration: Supervisor agents that delegate to specialized sub-agents

Bedrock integrates natively with AWS IAM for access control, AWS CloudTrail for audit logging, Amazon VPC for network isolation, and AWS KMS for encryption — capabilities that enterprise security teams expect as baseline requirements.

See the Amazon Bedrock Agents profile for platform-specific details.

Google Vertex AI Agent Builder Overview#

Google Vertex AI is Google Cloud's unified machine learning platform. Vertex AI Agent Builder (formerly Vertex AI Conversational AI, evolved from Dialogflow CX) provides a managed environment for building AI agents powered primarily by Google's Gemini model family.

Core components:

Foundation Models: Gemini 1.5 Pro, Gemini 1.5 Flash, Gemini 2.0, plus access to third-party models via Model Garden
Agent Builder: Visual and API-driven agent configuration with tool calling, grounding, and multi-agent orchestration
Data Stores: RAG backed by Vertex AI Search, with connectors for Google Cloud Storage, BigQuery, Google Drive, and web crawling
Grounding: Native Google Search grounding — agents can cite live web results with verifiable sources
Vertex AI Studio: Interactive testing and evaluation environment

Vertex AI's integration story centers on Google's data ecosystem: BigQuery, Google Workspace, Google Cloud Storage, and Google Search. For organizations whose data lives in Google's infrastructure, these native connectors significantly reduce integration work.

See the Google Vertex AI Agents profile for additional context.

Feature-by-Feature Comparison#

| Feature | Amazon Bedrock Agents | Google Vertex AI Agent Builder | |---|---|---| | Foundation model access | 20+ models (Claude, Llama, Mistral, Nova) | Gemini family + Model Garden | | Multimodal capabilities | Limited (model-dependent) | Strong (Gemini native, video/audio/image) | | RAG / Knowledge Bases | Managed (S3, OpenSearch, Pinecone) | Managed (GCS, BigQuery, Drive, web) | | Web grounding | External via tools | Native Google Search grounding | | Multi-agent orchestration | Supervisor + sub-agent architecture | Agent chaining + multi-agent | | Security & compliance | IAM, KMS, VPC, CloudTrail | Google IAM, CMEK, VPC-SC | | Guardrails / Safety | Bedrock Guardrails (built-in) | Safety filters + responsible AI | | Observability | CloudWatch + CloudTrail | Cloud Logging + Vertex AI monitoring | | Developer tooling | AWS CDK, Terraform, Boto3 | Vertex AI SDK, gcloud CLI, Terraform | | Primary cloud ecosystem | AWS | Google Cloud |

Pricing Comparison#

Both platforms charge at multiple layers: model tokens, managed service operations, and storage.

Amazon Bedrock pricing model:

Foundation model tokens: Varies by model (e.g., Claude 3.5 Sonnet ~$3/M input, ~$15/M output)
Bedrock Agents: Per agent invocation plus underlying model costs
Knowledge Bases: Per query plus vector store storage
Guardrails: Per unit processed
Provisioned Throughput: Committed capacity for predictable pricing at scale

Google Vertex AI pricing model:

Gemini token pricing: Gemini 1.5 Flash from $0.075/M input tokens; Gemini 1.5 Pro from $1.25/M input
Agent Builder: Per session or per query depending on configuration
Vertex AI Search (Data Stores): Per query
Google Search grounding: Per grounding call

At moderate volume, both platforms are cost-competitive. At high scale, Bedrock's Provisioned Throughput and Google's committed use discounts both provide cost predictability — the right choice depends on your volume profile and model selection.

Developer Experience#

Amazon Bedrock development typically happens through the AWS Management Console for configuration and Boto3 (Python SDK) or AWS CDK for programmatic control. The Bedrock Agents API follows AWS conventions that AWS developers already know. The main friction is the OpenAPI-based action group definition, which requires more upfront specification work than ad-hoc function definitions. AWS's documentation is thorough if dense.

Google Vertex AI development uses the Vertex AI SDK (Python) or REST API, with Vertex AI Studio providing an interactive web interface for agent testing. Google's documentation has improved substantially in 2025. Agent Builder's visual interface is approachable for non-developer stakeholders who want to configure agents without code. The grounding capability — where agents can cite live Google Search results — is unique and particularly useful for knowledge-intensive applications.

When to Choose Amazon Bedrock Agents#

Bedrock Agents fits best when:

Your organization is AWS-primary with existing IAM roles, VPC configurations, S3 buckets, and Lambda functions that agents need to interact with
You need model provider flexibility — the ability to use Claude for reasoning-heavy tasks and Llama for cost-sensitive tasks in the same agent system
Your compliance requirements are already covered by existing AWS BAAs, SOC 2 reports, and FedRAMP authorizations
You want Bedrock Guardrails as a managed content safety layer without building custom filtering
Your data sources are in S3, DynamoDB, or other AWS services — Bedrock's native connectors make retrieval simple
You need fine-grained IAM control at the agent action level

The tool use glossary entry explains how action groups and function calling work at the conceptual level — useful background before designing Bedrock Agents action schemas.

When to Choose Google Vertex AI Agent Builder#

Vertex AI Agent Builder fits best when:

Your organization is Google Cloud-primary with data in BigQuery, Google Cloud Storage, or Google Drive
You need leading multimodal capabilities — Gemini's native video, audio, and image processing is ahead of alternatives for complex multimodal agent tasks
Google Search grounding is a business requirement — the ability to cite live, verifiable web sources is unique to Vertex AI
You need BigQuery integration for agents that query structured enterprise data at scale
You're building on Google Workspace data and need agents that understand Google Docs, Sheets, and Drive natively
Your organization has an existing Google Cloud relationship with committed use discounts and negotiated support

Verdict#

For most enterprise teams, this decision is made before the technical comparison begins: you build on the cloud platform your organization already uses, where your data already lives, and where your security and compliance approvals already exist.

Where the technical comparison matters is at the capability edge. Gemini's multimodal capabilities and Google Search grounding are genuine differentiators — if your agent applications involve image, video, or real-time web knowledge, Vertex AI has capabilities that Bedrock currently can't match on its own. Bedrock's model diversity and deep AWS ecosystem integration are genuine differentiators for teams that need provider flexibility or have complex AWS service interactions.

Both platforms are production-grade, actively developed, and suitable for enterprise-scale agent deployments. The comparison of open-source vs commercial AI agent frameworks provides additional context if you're weighing managed cloud platforms against self-hosted alternatives.

Frequently Asked Questions#

Can I use both Amazon Bedrock and Google Vertex AI in the same application?

Yes, architecturally — nothing prevents API calls to both platforms from the same application. Some organizations use both strategically: Bedrock for core business logic agents that integrate with AWS services, and Vertex AI for specific multimodal tasks that leverage Gemini's capabilities. However, managing two cloud AI platforms adds operational complexity (separate monitoring, billing, IAM configurations, and SDK dependencies) that most teams avoid unless the capability difference justifies it.

How do Bedrock Guardrails compare to Vertex AI's safety features?

Bedrock Guardrails is a configurable content filtering layer that includes topic avoidance, word filtering, PII detection, hallucination reduction, and sensitive information redaction. It applies to both inputs and outputs. Vertex AI's safety filters are integrated at the model level (Gemini's built-in safety) plus configurable content policies in Agent Builder. Both platforms meet enterprise safety requirements; Bedrock Guardrails is more explicitly configurable as a standalone service, while Vertex AI's safety is more deeply integrated into the model itself.

Which platform has better support for agentic workflows that run for hours?

Long-running agent workflows are better served by Bedrock's agent runtime, which supports asynchronous invocation with polling — agents can run without a persistent connection. Vertex AI Agent Builder is better suited to interactive, lower-latency agent sessions. For batch or background agent workflows (overnight data processing, multi-hour research tasks), Bedrock's asynchronous model is the more practical choice. Both platforms support return-of-control patterns for human-in-the-loop workflows.