AWS Bedrock vs Google Vertex AI Compared
A comprehensive comparison of AWS Bedrock and Google Vertex AI for deploying and managing AI models in production, covering model selection, pricing, and enterprise capabilities.
AWS Bedrock and Google Vertex AI are the two leading cloud platforms for accessing and deploying foundation models in production environments. Both provide managed access to multiple model providers, enterprise security, and integration with their respective cloud ecosystems. AWS Bedrock offers a unified API to models from Anthropic (Claude), Meta (Llama), Mistral, Cohere, Amazon (Titan and Nova), and others. It integrates deeply with AWS services like S3, Lambda, SageMaker, and IAM, making it the natural choice for organisations already on AWS. Google Vertex AI provides access to Google's Gemini models alongside third-party models including Claude and Llama. It integrates with BigQuery, Cloud Storage, and Google's MLOps tooling. Vertex AI also offers Model Garden, a curated catalog of open and proprietary models with one-click deployment.
Head to Head
Feature comparison
| Feature | AWS Bedrock | Google Vertex AI |
|---|---|---|
| Model selection | Claude, Llama, Mistral, Cohere, Amazon Titan/Nova, Stability AI | Gemini, Claude, Llama, Mistral, and 150+ models via Model Garden |
| Proprietary models | Amazon Titan and Nova (text, embeddings, image generation) | Google Gemini family (text, multimodal, code, embeddings) |
| Pricing model | Pay-per-token with provisioned throughput options | Pay-per-token with provisioned throughput and committed use discounts |
| Fine-tuning | Supported for select models (Llama, Titan, Mistral) | Supported for Gemini and select third-party models |
| RAG support | Knowledge Bases for Bedrock with managed vector store | Vertex AI Search with grounding and managed RAG pipelines |
| Agent framework | Bedrock Agents with action groups and knowledge bases | Vertex AI Agent Builder with tool use and orchestration |
| Security | IAM, VPC endpoints, KMS encryption, CloudTrail audit logging | IAM, VPC Service Controls, CMEK encryption, Cloud Audit Logs |
| Data residency | Regional deployment across AWS regions globally | Regional deployment across GCP regions globally |
| MLOps integration | SageMaker for custom model training; Step Functions for orchestration | Vertex AI Pipelines, Experiments, and Model Registry for full MLOps |
| Evaluation tools | Model evaluation through Bedrock console; basic metrics | Vertex AI Evaluation with automated benchmarking and human eval tools |
Analysis
Detailed breakdown
The choice between AWS Bedrock and Google Vertex AI is primarily driven by your existing cloud infrastructure. If your organisation runs on AWS, Bedrock provides seamless integration with your existing VPC, IAM policies, and data stores. The same is true for Vertex AI on Google Cloud. Cross-cloud deployment is possible but adds complexity and cost. From a model perspective, both platforms offer access to Claude and Llama, which means the most popular third-party models are available on either platform. The differentiation comes from proprietary models: Bedrock offers Amazon's Titan and Nova models, while Vertex AI provides Gemini. For most use cases, Gemini's capabilities exceed Titan's, giving Vertex AI a slight edge in proprietary model quality. Vertex AI has a more mature MLOps story with integrated pipelines, experiment tracking, and model evaluation tools. Bedrock is more focused on model consumption than model operations, though it integrates with SageMaker for teams that need full ML lifecycle management. For teams that primarily need to consume foundation models through APIs, Bedrock's simplicity is an advantage.
When to choose AWS Bedrock
- Your infrastructure is built on AWS and you want native integration
- You need access to Amazon Titan or Nova models for specific use cases
- Your team prefers Bedrock's simpler, consumption-focused API
- You want Bedrock Agents for building orchestrated AI workflows on AWS
- Your security and compliance requirements are already configured around AWS IAM and VPC
When to choose Google Vertex AI
- Your infrastructure runs on Google Cloud
- You want access to Gemini models natively alongside third-party options
- Model Garden's breadth of 150+ models matters for your evaluation process
- You need integrated MLOps with pipelines, experiments, and model evaluation
- Vertex AI Search grounding and managed RAG capabilities fit your architecture
- You want the best price-performance ratio through Gemini Flash
Our Verdict
FAQ
Frequently asked questions
Yes, though it adds complexity. Some organisations use Bedrock on AWS for certain workloads and Vertex AI on GCP for others. Abstraction layers like LiteLLM can help manage multi-cloud model access.
Token pricing for third-party models (like Claude) is similar on both platforms. Gemini Flash on Vertex AI is among the cheapest high-quality options available. Amazon Nova offers competitive pricing on Bedrock. Compare based on your specific model and volume.
Generally yes, though new Claude model versions may reach one platform before the other. Both offer the full Claude family (Haiku, Sonnet, Opus) through their managed APIs.
Both offer managed RAG capabilities. Bedrock's Knowledge Bases provide a straightforward setup with S3 data sources. Vertex AI Search offers more advanced grounding options. Evaluate both against your specific data and query patterns.
Both support custom model deployment, though the process differs. Bedrock allows custom model import for select architectures. Vertex AI supports custom model deployment through Model Garden and custom containers.
Related Content
Azure AI vs AWS AI Services
Compare AWS AI services with Microsoft's Azure AI platform.
AWS Bedrock vs Azure OpenAI
Compare AWS Bedrock with Microsoft's Azure OpenAI Service.
Cloud AI vs Local AI
Explore when cloud deployment beats local AI hosting.
Managed AI vs Self-Hosted AI
Compare fully managed platforms with self-hosted AI infrastructure.
Not sure which to choose?
Book a free strategy call and we'll help you pick the right solution for your specific needs.