Vertex AI vs AWS Bedrock comparison
Quick answer
Use
Google Vertex AI for seamless integration with Google Cloud services and access to Gemini models. Choose AWS Bedrock for a multi-vendor model marketplace with easy deployment on AWS infrastructure and support for Anthropic Claude and Meta Llama models.VERDICT
For Google Cloud-centric projects,
Vertex AI is the best choice due to native integration and Gemini models; for multi-cloud flexibility and diverse model access, AWS Bedrock is the winner.| Tool | Key strength | Pricing | API access | Best for |
|---|---|---|---|---|
| Google Vertex AI | Native Google Cloud integration, Gemini models | Pay-as-you-go, detailed pricing on GCP site | Python SDK vertexai, REST API | Enterprise ML workflows on GCP |
| AWS Bedrock | Multi-vendor model marketplace, easy AWS deployment | Pay-per-use, pricing varies by model provider | Boto3 bedrock-runtime client, REST API | Hybrid cloud, multi-model access on AWS |
| Model variety | Gemini, custom Vertex models | N/A | N/A | Google ecosystem users |
| Model variety | Anthropic Claude, Meta Llama, AI21, Stability AI | N/A | N/A | Users needing diverse third-party models |
Key differences
Google Vertex AI offers deep integration with Google Cloud Platform and access to proprietary Gemini models optimized for various AI tasks. AWS Bedrock provides a multi-vendor marketplace allowing users to access models from Anthropic, Meta, AI21, and Stability AI, all deployable on AWS infrastructure. Pricing models differ, with Vertex AI charging per usage on GCP and Bedrock pricing varying by model provider.
Side-by-side example: Vertex AI chat completion
import os
import vertexai
from vertexai.generative_models import GenerativeModel
vertexai.init(project=os.environ["GOOGLE_CLOUD_PROJECT"], location="us-central1")
model = GenerativeModel("gemini-2.0-flash")
response = model.generate_content("Explain the benefits of AI cloud platforms.")
print(response.text) output
Explain the benefits of AI cloud platforms, including scalability, integration, and access to advanced models.
AWS Bedrock equivalent example
import os
import boto3
client = boto3.client("bedrock-runtime", region_name="us-east-1")
response = client.converse(
modelId="anthropic.claude-3-5-haiku-20241022",
messages=[{"role": "user", "content": [{"type": "text", "text": "Explain the benefits of AI cloud platforms."}]}]
)
print(response["output"]["message"]["content"][0]["text"]) output
AI cloud platforms provide scalable infrastructure, easy integration with services, and access to powerful AI models from multiple vendors.
When to use each
Use Vertex AI when your infrastructure is primarily on Google Cloud and you want access to Google's Gemini models with seamless integration. Choose AWS Bedrock if you require a variety of third-party models, prefer AWS infrastructure, or want flexibility in model selection across vendors.
| Scenario | Recommended Platform |
|---|---|
| Google Cloud native projects | Vertex AI |
| Multi-cloud or AWS-centric projects | AWS Bedrock |
| Need for Gemini models | Vertex AI |
| Access to Anthropic Claude or Meta Llama | AWS Bedrock |
Pricing and access
| Option | Free | Paid | API access |
|---|---|---|---|
| Google Vertex AI | No permanent free tier, some GCP credits | Pay-as-you-go per usage | Python SDK vertexai, REST API |
| AWS Bedrock | No free tier | Pay-per-use, varies by model | Boto3 bedrock-runtime client, REST API |
Key Takeaways
-
Vertex AIexcels in Google Cloud integration and Gemini model access. -
AWS Bedrockoffers a diverse model marketplace with Anthropic and Meta models. - Pricing and API access differ; choose based on cloud environment and model needs.