Amazon Bedrock and Google Vertex AI are the primary managed AI platforms from their respective cloud providers. Both offer access to foundation models, fine-tuning capabilities, and RAG infrastructure, but they differ in model selection, ecosystem integration, and architectural approach. Note that at Google Cloud Next 2026 (April 2026), Google began rebranding Vertex AI as the Gemini Enterprise Agent Platform, folding the former Vertex AI, Agentspace, and Gemini API surfaces into one namespace. Much of the documentation and SDK still carry the Vertex AI name, so this page uses both.

Overview

AspectAmazon BedrockGoogle Vertex AI / Gemini Enterprise Agent Platform
Model AccessMulti-vendor catalog (18+ providers)Google models + Model Garden (200+ models)
Flagship ModelsClaude, Llama, Mistral, Amazon Nova, TitanGemini (3.x family), Imagen, Veo
Fine-tuningSupported for select modelsSupported with Vertex AI Studio
RAGBedrock Knowledge BasesVertex AI Search
AgentsBedrock Agents, Bedrock AgentCoreVertex AI Agent Builder, Agent Platform
SafetyBedrock GuardrailsResponsible AI toolkit
Pricing ModelPer-tokenPer-token (character-based for some legacy models)

Model Selection

Bedrock’s primary advantage is model diversity. You access Claude (Anthropic), Llama (Meta), Mistral, Cohere, Amazon Nova, and Amazon Titan models through a single API, with the catalog spanning more than a dozen providers. In early 2026 Bedrock also added several open weights models including DeepSeek, GLM, Kimi, MiniMax, and Qwen variants, so you can evaluate multiple model families without changing your integration code. Cross-region inference distributes requests across regions for higher throughput, and global cross-region inference can route to commercial AWS Regions worldwide for additional capacity.

Vertex AI centers on Google’s own Gemini models (the current generation is the Gemini 3.x family), which are competitive across benchmarks. The Model Garden provides access to open models like Llama, Gemma, Qwen, and DeepSeek, as well as partner models from Anthropic and Mistral AI, and you can deploy custom models to Vertex AI endpoints. The first-class experience is still optimized for Gemini, though Google now markets the platform as a full agent stack rather than a model catalog alone.

RAG and Knowledge Management

Bedrock Knowledge Bases provide managed RAG with automatic document chunking, embedding, and vector storage in OpenSearch Serverless or Pinecone. You point it at an S3 data source, and it handles ingestion and retrieval. The retrieval API integrates directly with Bedrock model invocation.

Vertex AI Search (formerly Enterprise Search) provides similar managed RAG capabilities with support for unstructured documents, structured data, and websites. It includes advanced retrieval features like extractive answers and search tuning. Vertex AI Search integrates with Google’s broader search technology stack.

Agents

Bedrock Agents support multi-step task execution with tool use, knowledge base access, and code interpretation. Agents use a ReAct-style reasoning loop and support action groups that map to Lambda functions or API schemas. For production agents, AWS added Amazon Bedrock AgentCore, which reached general availability in October 2025. AgentCore is a framework-agnostic platform to deploy and operate agents at scale, with a managed runtime (extended execution windows and session isolation), short and long term memory, a gateway that exposes APIs and Lambda functions as tools, and support for the Model Context Protocol (MCP) and the Agent2Agent (A2A) protocol.

Vertex AI Agent Builder provides a visual and code-based interface for building agents. It integrates with Dialogflow CX for conversational agents and supports extensions for connecting to external services. With the 2026 rebrand to the Gemini Enterprise Agent Platform, Google has consolidated its agent tooling and adopted the Agent2Agent (A2A) protocol as the default interoperability layer. The agent platform benefits from integration with Google Workspace and Google Search grounding.

Fine-tuning

Both platforms support fine-tuning, but the experience differs. Bedrock offers fine-tuning for select models (Titan, Llama, Cohere) through an S3-based workflow. You upload training data to S3 and create a fine-tuning job through the API.

Vertex AI provides fine-tuning through Vertex AI Studio with a more interactive experience. Supervised fine-tuning, reinforcement learning from human feedback (RLHF), and distillation are supported for Gemini models. The Vertex AI notebook integration makes experimentation more fluid.

Enterprise Integration

Bedrock integrates natively with the AWS ecosystem: IAM for access control, CloudWatch for monitoring, CloudTrail for audit logging, VPC endpoints for private connectivity, and S3 for data storage. For organizations already running on AWS, Bedrock requires no new infrastructure patterns.

Vertex AI integrates with Google Cloud’s ecosystem: IAM, Cloud Logging, Cloud Monitoring, and VPC Service Controls. It has unique advantages for organizations using Google Workspace, BigQuery, and Google’s data analytics stack. BigQuery ML allows direct model invocation from SQL queries.

When to Choose Bedrock

Choose Bedrock when model diversity matters - when you need to evaluate or switch between multiple model providers. If your infrastructure is on AWS, Bedrock is the path of least resistance. Bedrock is also strong for RAG workloads that need tight S3 integration and for organizations that want Anthropic’s Claude models as their primary LLM.

When to Choose Vertex AI

Choose Vertex AI when you want deep integration with Google’s data and analytics stack, when Gemini models meet your needs, or when you need Google Search grounding for factual accuracy. Organizations using BigQuery, Google Workspace, or Dialogflow will find Vertex AI provides the most integrated experience.

Practical Recommendation

The model availability question often drives this decision. If your evaluation shows that Claude or Mistral is the best model for your use case, Bedrock is the natural platform. If Gemini performs best, Vertex AI is the clear choice. For organizations not locked into either cloud, run parallel evaluations - the API integration cost is low, and the performance differences between models can be significant for specific tasks.

Sources and Further Reading