The three hyperscalers each offer a mature, secure platform for enterprise generative AI. All three provide multiple models, managed RAG, agent tooling, guardrails and enterprise security. The right choice depends mostly on where your data and users already are, and which models matter most to you.
The platforms in brief
- Amazon Bedrock — managed, multi-provider model access on AWS, with Knowledge Bases, Agents, AgentCore and Guardrails.
- Azure OpenAI & Azure AI Foundry — OpenAI models plus a broad catalogue on Azure, with Azure AI Search, an agent service and deep Microsoft 365 integration.
- Google Vertex AI — Gemini and Model Garden on Google Cloud, with Vertex AI Search, Agent Builder and close BigQuery integration.
Side-by-side comparison
| Amazon Bedrock | Azure OpenAI / AI Foundry | Google Vertex AI | |
|---|---|---|---|
| Flagship models | Anthropic Claude, Amazon Nova, Llama, Mistral and more | OpenAI models plus a wide catalogue | Gemini plus Model Garden (incl. Claude, Llama) |
| Managed RAG | Knowledge Bases | Azure AI Search “on your data” | Vertex AI Search, RAG Engine |
| Agents | Bedrock Agents, AgentCore | Foundry agent service, Copilot Studio | Agent Builder, ADK |
| Safety | Guardrails for Bedrock | Azure AI Content Safety | Safety filters, evaluation |
| Ecosystem fit | AWS-native apps & data | Microsoft 365, Teams, Dynamics | BigQuery, Workspace, Search |
| Signature strength | Model choice + AWS integration | Enterprise Microsoft integration | Multimodal + data/analytics |
All three platforms evolve quickly; model availability differs by region. Check current documentation before deciding.
How to choose
- Follow your data and identity. The platform closest to your core data and identity provider usually wins on security, latency and simplicity.
- Test the models on your tasks. Run the same evaluation set across the candidate models; results often differ by use case.
- Consider the channel. If most users live in Teams and Microsoft 365, Azure has a head start; if AI must be embedded in AWS-hosted products, Bedrock fits naturally.
- Check commercial factors. Existing commitments, credits and enterprise agreements can change the economics significantly.
- Plan for change. Abstract model calls behind your own interface so you can switch models — or platforms — as the market evolves.
Is multi-platform AI a good idea?
Sometimes. Using a second platform for a specific model or capability is reasonable, especially if you keep a common evaluation framework, gateway and governance. Running every use case on every platform, however, multiplies cost and complexity without adding much value.
We build on all three — see our Amazon Bedrock, Azure AI and Google Vertex AI services, or ask us for a vendor-neutral platform assessment.
Frequently asked questions
Which is better: Amazon Bedrock, Azure OpenAI or Vertex AI?
None is universally better. Bedrock fits AWS-centric organisations wanting model choice, Azure fits Microsoft-centric organisations, and Vertex AI suits teams focused on Gemini, multimodal use cases and BigQuery. Test models on your own tasks.
Can I use Claude on Azure or Google Cloud?
Anthropic Claude models are available on Amazon Bedrock and Google Vertex AI, and model catalogues on other platforms are expanding. Check each provider's current model list and regional availability.
How do I avoid lock-in to one AI platform?
Put model calls behind your own interface or gateway, keep prompts and evaluations portable, and use open standards such as MCP for tools where possible.