The short version
Three hyperscalers. Three credible answers for enterprise AI. AWS wins on model catalog and enterprise governance. Azure wins on OpenAI integration and Microsoft-stack fit. GCP wins on Google research models, TPUs, and vector capability. The right choice is usually driven by existing cloud posture — enterprises rarely switch hyperscalers for AI alone.
Side-by-side
| Dimension | AWS | Azure | GCP | |---|---|---|---| | Primary AI platform | Amazon Bedrock + SageMaker | Azure AI Foundry + Azure OpenAI | Vertex AI | | Proprietary model family | Nova, Titan | OpenAI partnership (GPT family) | Gemini (Google DeepMind) | | Third-party models | Anthropic, Meta, Mistral, Cohere, others | OpenAI primary, Llama, Mistral | Anthropic (via partnership), Llama, Mistral | | Open-weight hosting | Bedrock + SageMaker | AI Foundry + Azure ML | Vertex + GKE | | Vector search | OpenSearch, Kendra, dedicated services | Azure AI Search | Vertex AI Vector Search, AlloyDB | | Data integration depth | Comprehensive across AWS data services | Deep with Fabric, Synapse, Purview | Deep with BigQuery, Dataplex | | Governance and BAA | Mature across regulated categories | Mature; often primary for healthcare/BAA | Mature; strong analytics governance | | Specialized hardware | Trainium, Inferentia | Maia accelerators | TPU v5 and successors | | Multi-cloud posture | Best for AWS-centric orgs | Best for Microsoft-centric orgs | Best for data/research-centric orgs |
When AWS wins
- Enterprise is already AWS-dominant across infrastructure and data.
- Model catalog breadth matters — Bedrock offers Anthropic, Meta, Mistral, Cohere, Amazon's own, and others in a single API.
- Enterprise governance, IAM, and compliance posture are a priority.
- SageMaker is already in use for ML engineering; the AI workloads extend naturally.
- Cost-sensitive inference may benefit from Trainium/Inferentia specialized hardware.
When Azure wins
- Enterprise is Microsoft-centric (Entra, M365, Dynamics, Power Platform).
- OpenAI integration depth matters — Azure OpenAI is the enterprise path to GPT-family models.
- Microsoft Fabric is the data platform (see our Microsoft Fabric answer).
- HIPAA BAA, FedRAMP, or government workloads benefit from Microsoft's compliance posture.
- Copilot-family integration across the Microsoft stack is a strategic advantage.
When GCP wins
- Gemini models matter for the workload — Google DeepMind research produces top-tier capability.
- BigQuery is the primary analytics platform.
- TPU access for training or specialized inference.
- Vertex AI Vector Search and the data-integration patterns between BigQuery and AI workloads.
- The enterprise culture leans toward Google's developer experience and open-source posture.
The multi-cloud posture
Many enterprises end up with more than one. The common patterns:
- Azure primary, AWS secondary. Microsoft-centric enterprises with a specific workload (data lake, ML training) on AWS.
- AWS primary, Azure secondary. AWS-centric enterprises that adopted Azure OpenAI specifically.
- AWS primary, GCP secondary. For BigQuery-driven analytics workloads alongside AWS compute.
- All three. Large enterprises with independent business units or acquisitions.
Multi-cloud AI works when:
- The model catalog and capability per cloud is chosen workload-by-workload.
- Identity, audit, and data governance span the clouds — not one model per cloud.
- FinOps discipline prevents runaway cost across multiple AI services.
It fails when cloud selection becomes a free-for-all and nobody owns the portfolio decision.
What to evaluate before committing
For enterprises making a primary-cloud-for-AI decision:
- Model capability against your actual workload. Run a 2-week evaluation on each candidate cloud's top model against representative input.
- Data integration cost. Which cloud's analytics layer matches your data platform?
- Governance maturity. Which cloud's audit, BAA, and compliance posture matches your regulatory regime?
- Cost at projected volume. Token pricing, hardware pricing, and data-egress cost.
- Team skill. Which cloud's AI tooling does your team already know?
- Vendor trajectory. Which cloud's AI roadmap looks strongest three years out?
How Thoughtwave approaches this
We are cloud-neutral. Our engagements run on AWS, Azure, and GCP, and we recommend the execution platform based on the client's workload, existing stack, and regulatory regime. For the deeper framework on cloud vs self-hosted LLMs, see the cloud vs self-hosted comparison.
For broader context, see our AI & Generative AI service and the accelerators portfolio.