Amazon Bedrock vs Google Vertex AI - Cloud AI Platforms Compared

AWS 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.

Overview

Model Selection

Bedrock’s primary advantage is model diversity. You access Claude (Anthropic), Llama (Meta), Mistral, Cohere, and Amazon Titan models through a single API. This lets you evaluate multiple model families without changing your integration code. Cross-region inference distributes requests across regions for higher throughput.

Vertex AI centers on Google’s own Gemini models, which are competitive across benchmarks. The Model Garden provides access to open models like Llama and Mistral, and you can deploy custom models to Vertex AI endpoints. However, the first-class experience is optimized for Gemini, and third-party model access is less seamless than Bedrock’s unified API.

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.

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. Google’s 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.