AWS, Google Cloud, and Azure can all host a production AI application. The useful decision is not which cloud is “best,” but which one fits your model access, data location, identity system, GPU needs, operating skills, and commercial constraints.

Decision summary

Priority	Strong starting point	Verify before choosing
Broad infrastructure catalog	AWS	Service complexity, regional availability, cost controls
Google model/data ecosystem	Google Cloud	Product and region support for your exact workload
Microsoft identity and enterprise estate	Azure	Quotas, deployment availability, networking requirements
Portable API-based AI SaaS	Any	Choose based on team operations and data services
Self-hosted GPU inference	Any	GPU quota, price, region, capacity, networking, operations

This is a starting framework, not a guarantee. Managed AI products, model catalogs, quotas, and prices change frequently; confirm current official documentation and account eligibility.

Managed AI services

Compare the exact model, region, API capabilities, data-use terms, networking options, evaluation tools, and support commitment—not only brand names. A model available through a provider’s direct API may have different versions or controls on a managed cloud platform.

As one current example, AWS made OpenAI GPT-5.6 Terra and Luna generally available through Amazon Bedrock in both AWS GovCloud (US-East) and AWS GovCloud (US-West) on August 24, 2026. That announcement did not include GPT-5.6 Sol. Bedrock availability is not the same product surface as OpenAI’s direct API: verify the Bedrock model identifier, account eligibility, region, controls, and price before designing around it.

AWS also expanded Amazon Bedrock AgentCore to US West (N. California) and Asia Pacific (Hyderabad) on August 27, 2026. AWS lists AgentCore Runtime, identity and access control, policy management, session persistence, tool connectivity, evaluations, and observability as available at launch in those regions. This expands where teams can run managed agent infrastructure, but it does not mean every Bedrock model or adjacent AWS service is available in the same regions. Check the AgentCore region documentation for the exact feature and region combination your architecture needs.

Keep your application contract independent where practical. An AI gateway can normalize authentication, model aliases, quotas, and usage while preserving access to provider-specific capabilities when they matter.

GPU inference and training

For self-hosted models, verify accelerator type, memory, interconnect, quota, actual capacity in the target region, storage throughput, and egress. A published instance type is not proof that your account can provision it on demand.

Include idle capacity, autoscaling lag, model-loading time, observability, and engineer time in the comparison. For intermittent traffic, an API or managed inference service can be cheaper than an always-on GPU; sustained predictable traffic may reverse that calculation.

Serverless and background work

All three clouds offer functions, containers, queues, schedulers, and workflow services. Short request handlers fit serverless well. Long-running agents, batch inference, and media processing need durable jobs, cancellation, retries, and explicit state.

Do not rely on a single long HTTP request. Connect queues to idempotent workers and use webhooks for completion.

Databases and AI state

Choose data services based on access patterns: relational product data, document metadata, object storage, caches, queues, and vector retrieval have different requirements. Review the AI Data Infrastructure hub before selecting a database because it shares a cloud logo with the model service.

Identity, networking, and enterprise fit

For enterprise systems, existing identity, private networking, audit tooling, procurement, data residency, and staff expertise often outweigh small compute-price differences. Test cross-region and cross-cloud data paths; model calls can create latency and egress costs that a simple instance comparison misses.

Cost controls

Create budgets and alerts before production, tag resources by application and environment, restrict expensive models and GPU shapes, and capture model usage separately from cloud infrastructure. Published calculators are estimates; validate with a representative load test.

Avoid accidental lock-in

Lock-in is not automatically bad when a managed service creates real value. Make it deliberate: own the application schema, export data, isolate cloud adapters, document identity dependencies, and rehearse recovery. Do not build a lowest-common-denominator platform that discards every useful managed capability.

Final choice

Pick AWS for breadth and an AWS-skilled team, Google Cloud for a strong fit with its AI/data services, or Azure when Microsoft identity and enterprise integration dominate. If you only need to host a frontend and API that call external models, a smaller PaaS may be simpler. Start with the AI hosting guide and AI Deployment & Hosting.