Comparison · 5 minute read
Vertex AI vs AWS Bedrock: Choosing a Cloud AI Platform
Vertex AI is Google Cloud's managed AI platform with Gemini models, tuning, and MLOps tooling; Amazon Bedrock is AWS's managed service offering multiple model providers through one API with AWS security and integration. Choose by cloud alignment, model access needs, and data residency, and route through a gateway so the choice can change later.
Google Cloud's Vertex AI and Amazon's Bedrock are the two broadest managed foundation-model platforms outside Microsoft's ecosystem, and both aggregate multiple model providers under their cloud's enterprise controls. For most enterprises the decision follows existing cloud estate, adjusted by which models evaluation favors and by data controls. This comparison covers the dimensions that matter, drawing on FISTA Solutions' AI enablement practice. Related comparisons are aws bedrock vs azure openai and the framework in how to choose a cloud platform for ai.
What is Vertex AI?
Vertex AI is Google Cloud's managed AI platform. It provides Google's own foundation models, a catalog of partner and open models, and an integrated toolchain for data preparation, training, tuning, model registry, deployment, evaluation, and monitoring, alongside features for retrieval, agents, and grounding. Access uses Google Cloud identity, networking, and regional deployment, and billing runs through Google Cloud. Its distinguishing features are Google's models and the breadth of the surrounding MLOps platform.
What is Amazon Bedrock?
Bedrock is AWS's managed service for foundation models from multiple providers, including Anthropic and Amazon, with surrounding features for knowledge bases, agents, guardrails, evaluation, and fine-tuning depending on model. Access uses AWS identity and access management, private connectivity, and regional deployment, with billing through AWS. Its distinguishing feature is multi-provider choice under AWS controls, complemented by AWS's broader machine-learning services.
How do they compare?
| Dimension | Vertex AI | AWS Bedrock |
|---|---|---|
| First-party models | Google's frontier and specialized models | Amazon's models |
| Third-party and open models | Partner catalog and open models | Multiple providers including Anthropic |
| MLOps platform | Integrated end to end | Bedrock plus broader AWS machine-learning services |
| Retrieval and agents | Built-in grounding and agent tooling | Knowledge bases and agent features |
| Identity and networking | Google Cloud identity; private connectivity | AWS identity; private connectivity |
| Data handling | Google Cloud terms; provider-specific for partners | AWS framework; provider-specific terms |
| Regional availability | Varies by model | Varies by model |
| Ecosystem fit | Google Cloud data and analytics services | AWS data, compute, and services |
| Billing | Google Cloud commitments | AWS commitments |
Catalogs, features, and regions change frequently; verify current documentation.
How does cloud estate decide?
Where your data, identity, networking, security tooling, and commitments live shapes everything: latency to data, transfer costs, integration effort, and consistency of controls. Teams skilled in one cloud move faster on it. The default is the incumbent cloud; overrides come from model needs and specific capabilities.
How do model needs override estate?
If evaluation on your golden datasets shows a model family available only on one platform performing best on a critical task, running that workload there is justified even outside your primary cloud. Both catalogs are broad and overlapping, so this override is less common than assumed, and it should rest on measurement. Method is in the AI evaluation and testing whitepaper and the provider view in openai vs anthropic for enterprise.
How do MLOps needs factor in?
Organizations training and tuning custom models alongside foundation models may value Vertex AI's integrated platform; organizations already invested in AWS's machine-learning services have equivalent capabilities there. Either way, the platform's tooling should complement rather than replace the evaluation gates, registry discipline, and observability you own. Practice is in the mlops maturity checklist.
How should data handling be assessed?
Both platforms operate within their cloud's compliance frameworks with regional deployment, private connectivity, and enterprise terms excluding customer data from provider training by default; terms vary by model provider and feature. Map current terms to your data classification and apply redaction or private deployment where needed. Guidance is in ai data privacy compliance and ai data residency.
How much platform coupling should you accept?
Both platforms offer retrieval, agent, and guardrail features that accelerate early builds and couple you to the platform. Decide deliberately which to adopt and which to own: a client-owned gateway, permission-aware retrieval, evaluation harness, and observability keep the core portable and let platform features be used where they add value without becoming lock-in. The ownership view is in the LLM production readiness whitepaper and how to build an llm gateway.
When does multi-cloud make sense?
When critical models split across platforms, when cross-cloud fallback is required for concentration risk, or when business units run different clouds. The gateway routes by logical model and unifies evaluation, logging, and cost; the price is operating across two identity and networking estates. Risk context is in ai third-party risk management.
What does the decision look like in practice?
A Google Cloud-centric analytics company with data in its warehouse runs Vertex AI for grounded assistants and custom model tuning, with evaluation showing Google's models performing well on its tasks. An AWS-centric logistics firm runs Bedrock with Anthropic models for document and agent workloads under AWS identity and private networking. A conglomerate with both estates runs each platform for its own units behind one gateway policy and one evaluation harness, and routes a small number of workloads cross-cloud where evaluation favors a specific model.
How FISTA Solutions works across cloud AI platforms
FISTA Solutions builds on Vertex AI, Bedrock, Azure OpenAI, direct provider APIs, and private deployments according to client estate, evaluation results, and data requirements, always behind a client-owned gateway with unified evaluation and observability. The AI enablement practice delivers the platform, AI agents run across it, and forward deployed engineers run evaluation and integration with your cloud and security teams. The record behind the approach is 150+ projects with 99.9% uptime.
To decide your cloud AI platform, message FISTA on WhatsApp, or read how to choose an llm provider for the model-provider layer.
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01What is the difference between Vertex AI and Bedrock?
Vertex AI is Google Cloud's end-to-end AI platform with Google's models, a model garden of partner and open models, and integrated training, tuning, deployment, and MLOps tooling. Bedrock is AWS's managed service for foundation models from multiple providers with surrounding features for retrieval, agents, and guardrails, under AWS controls.
02Which has better models?
Both offer strong catalogs that overlap and differ; Vertex includes Google's own frontier models and partners, Bedrock includes Anthropic, Amazon, and other providers. Which is better depends on your tasks, measured on your golden datasets, and changes with releases.
03Should we choose based on our cloud?
As the default, yes. Identity, private networking, data gravity, existing commitments, and team skills favor the incumbent cloud. Override when required models exist only elsewhere or when a specific platform capability is decisive, and consider both through a gateway.
04How do MLOps capabilities compare?
Vertex AI provides an integrated platform spanning data, training, tuning, registry, deployment, and monitoring for custom and foundation models. AWS provides equivalent capabilities across Bedrock and its broader machine-learning services. Both can support mature MLOps; the integration surface differs.
05Can you use both?
Yes. A gateway can route tasks to the platform and model that suit them and provide cross-cloud fallback, with unified evaluation, logging, and cost, at the cost of operating across two clouds' identity and networking.
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