Amazon BedrockPaid
AWS's fully managed service for accessing foundation models from multiple providers, with agents, guardrails, and knowledge bases built in.
AI Infrastructure comparison
Amazon Bedrock vs Google Vertex AI
Pricing, pros, cons, and ideal use cases — side by side.
Google Vertex AIPaid
Google Cloud's unified AI platform — access to Gemini and partner models, plus tools to build, deploy, and govern AI and agents.
At a glance
| Amazon Bedrock | Google Vertex AI | |
|---|---|---|
| Pricing | PaidUsage-based pricing (per token, or provisioned throughput). Billed through AWS. | PaidUsage-based pricing across model and platform services, billed through Google Cloud. |
| Category | AI Infrastructure | AI Infrastructure |
| Ideal for | Enterprises already standardized on AWSTeams needing models inside their cloud security perimeterRegulated organizations with strict compliance needs | Enterprises already on Google CloudTeams wanting Gemini under enterprise governanceOrganizations consolidating the AI lifecycle on one platform |
Pros & cons
Amazon Bedrock
Pros
- Multiple model providers through one managed service
- Stays inside AWS security, IAM, and compliance
- Managed RAG, agents, and guardrails included
- Familiar billing and governance for AWS shops
Cons
- Ties your AI stack to AWS
- Features can lag native provider platforms
- Pricing and quota management add complexity
Google Vertex AI
Pros
- Gemini plus a broad Model Garden
- End-to-end build, deploy, evaluate, and govern
- Inside Google Cloud IAM and compliance
- Strong data and analytics integration
Cons
- Large, complex platform surface area
- Most valuable only if you are already on GCP
- Getting value requires real platform investment
Which should you choose?
Amazon Bedrock is built around enterprises already standardized on aws; Google Vertex AI leans more toward enterprises already on google cloud. Shortlist the one whose strengths line up with your biggest constraint.