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There is no universal winner. Choose AWS when you want Amazon Bedrock’s managed, model-independent API and a broad set of foundation models alongside AWS services. Choose Azure when your organization already uses Microsoft identity, security, and governance and wants Microsoft Foundry’s integrated model, agent, retrieval, evaluation, and monitoring tools. Choose Google Cloud when Vertex AI and Model Garden fit your model-customization and machine-learning workflow, especially when your data platform already runs on Google Cloud.
The best decision depends on the models available in your required region, where your data lives, latency and residency constraints, the controls your auditors require, your team’s skills, and the complete cost of serving your traffic. The comparison below gives you a way to make that decision without assuming that similarly named services are interchangeable.
What each cloud actually offers
All three providers offer managed access to multiple generative-AI models, but they package the work differently. Your choice affects model switching, deployment options, retrieval, agent tools, governance, operations, and how much infrastructure your team must run.
AWS: Amazon Bedrock as the managed model layer
AWS describes Amazon Bedrock as a fully managed service for building generative-AI applications. Its model-independent API lets an application call supported foundation models through a common interface, reducing the code changes required when you change model versions. The catalog includes Amazon Nova and Titan and third-party models from Anthropic, DeepSeek, Cohere, AI21 Labs, Meta, Mistral, and Stable Diffusion XL, subject to regional and deployment availability.
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Bedrock is the natural starting point when you want serverless model access and AWS-native integration for storage, identity, networking, logging, and event-driven workloads. If you need hands-on training, custom deployment, or direct accelerator control, AWS also offers SageMaker AI and specialized Trainium and Inferentia infrastructure. Those are different operating models: Bedrock reduces infrastructure work, while SageMaker AI and accelerator services give you more control and more responsibilities.
Azure: Microsoft Foundry as an application and agent platform
Microsoft Foundry is documented as an AI application and agent platform. Microsoft says it provides access to more than 10,000 models from Microsoft, OpenAI, Anthropic, Meta, and other providers. The platform combines model access with agents, tools, memory, retrieval, tracing, monitoring, evaluations, role-based access control, and policy controls.
Foundry distinguishes between models sold by Azure and partner or community models. Azure-sold models are hosted and operated by Azure, billed through the Azure subscription, supported by Microsoft, and covered by Azure service-level agreements. Partner and community models can have different support boundaries and marketplace billing arrangements, so the model card and deployment terms matter as much as the model name.
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Foundry documentation describes serverless API deployments and managed-compute deployments, with instant-access preview availability for some models. Treat preview status, quotas, regions, and support commitments as deployment-specific. Microsoft directs customers to the Azure pricing calculator for rate estimates rather than promising a single platform-wide price.
Google Cloud: Vertex AI and Model Garden across the model lifecycle
Google’s generative-AI application guidance organizes work into four stages: select a model, customize its output, evaluate the customization, and deploy the application. Vertex AI and Model Garden are the central places to discover Google, open-source, and third-party foundation models and to move them through that workflow.
This approach fits teams that want model experimentation, tuning, evaluation, and production deployment connected to a broader machine-learning platform. Google’s service map places products such as Document AI, Vision AI, and conversational services beside approximate counterparts on AWS and Azure. That map is useful for finding candidate services, but it does not establish that the products have identical behavior, quality, quotas, or prices.
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Decision at a glance
| Choose this cloud when… | Best fit | Check before committing |
|---|---|---|
| AWS | You want Bedrock’s managed, multi-model API and tight integration with existing AWS systems. | Required model and modality in each target region; Bedrock quotas; whether you need SageMaker AI or accelerator-level control. |
| Azure | Your company is standardized on Microsoft identity, networking, security, and compliance, and you want one platform for models, agents, retrieval, evaluation, and monitoring. | Whether a model is Azure-sold or partner/community; deployment mode, marketplace billing, preview status, quota, and regional availability. |
| Google Cloud | Vertex AI and Model Garden match your model-selection, customization, evaluation, and deployment process, or your data and ML stack already run on Google Cloud. | Exact model, tuning method, endpoint type, region, quota, and integration requirements for your existing data services. |
This table is a starting point, not a ranking. A model catalog that looks larger on a product page may not include the model, context length, modality, license, or throughput you need in your selected region.
How to compare model choice and portability
Start with a workload-specific model list
Write down the models your application actually needs instead of counting catalog entries. Record the model family, version, input and output modalities, context window, maximum output, tool-calling behavior, fine-tuning availability, safety controls, license, and data-processing terms. Then verify that each item is offered in the region where the application will run.
Bedrock, Foundry, and Model Garden all support multi-model access, but their catalogs and terms change. A model may be available through a serverless API in one region, require a managed endpoint in another, or be offered only as a preview. Confirm quotas and provisioned-throughput options before designing a capacity plan.
Separate portability at the API from portability in behavior
A common API can make a provider switch easier, but it cannot make models behaviorally identical. Prompt formats, system-message rules, tool schemas, tokenization, safety filtering, embeddings, structured-output guarantees, and citation behavior can differ. Build an adapter layer around your application’s own request and response types, then maintain provider-specific connectors behind it.
Keep a regression set of representative prompts, sensitive cases, long-context requests, tool calls, and failure conditions. Run it whenever you change model, region, provider, or safety configuration. Portability is a testing and operations discipline, not just a shared SDK.
Data location, latency, and ecosystem fit
The provider that already hosts your application data usually has an operational advantage. Keeping object storage, databases, identity, private networking, observability, and model inference in one cloud can reduce cross-cloud latency, egress charges, and governance work. This is not an automatic reason to stay put: a required model or compliance boundary can outweigh ecosystem convenience.
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Map every data movement
- List source systems, document stores, vector indexes, feature stores, model endpoints, logging destinations, and human-review tools.
- For each connection, record region, network path, encryption, retention, and whether prompts or outputs leave the approved boundary.
- Measure the latency budget separately for retrieval, model generation, tool calls, and post-processing.
- Decide which telemetry is safe to retain and which fields must be redacted before logging.
Cross-cloud retrieval can work, but it adds another failure domain and often creates a difficult question about where embeddings, prompts, and retrieved passages are processed. If the application has strict residency requirements, confirm the exact data path for the selected model and deployment mode rather than relying on a provider’s global service description.
Application control: serverless, managed endpoints, or your own infrastructure
Serverless inference
Serverless APIs are the fastest route from prototype to production when traffic is variable and you do not want to manage accelerators. You pay according to the provider’s request or token model and accept the provider’s supported model versions, quotas, and scaling behavior. This is usually the simplest option for chat, summarization, extraction, and moderate-volume agent workloads.
Managed compute
Managed endpoints give you more control over deployment configuration, model packaging, scaling, and sometimes fine-tuning. They require capacity planning, health checks, rollout procedures, and cost management even when the provider operates the underlying machines. Foundry’s managed-compute option and Google’s endpoint workflows belong in this category; AWS teams may use SageMaker AI when Bedrock’s managed access is insufficient.
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Training or serving on dedicated accelerators is justified when you need a model configuration unavailable through a managed API, predictable high utilization, custom kernels, or strict control over the serving stack. AWS documents Trainium and Inferentia for specialized compute. Comparable choices on other clouds should be evaluated by actual throughput, memory, software support, and operational burden, not by accelerator names alone.
Governance, security, and enterprise operations
Evaluate governance as a system rather than as a checklist of product names. Your architecture should answer who can deploy a model, who can access prompts and outputs, how content is filtered, how incidents are investigated, and how an auditor can reconstruct a decision.
- Identity and access: Use least-privilege roles for developers, deployment pipelines, runtime services, evaluators, and support staff.
- Private connectivity: Confirm private endpoints, firewall behavior, DNS, and egress controls for every model and retrieval service.
- Data protection: Document encryption, key ownership, retention, deletion, and whether provider services use your inputs for training under the selected terms.
- Safety: Test prompt-injection resistance, sensitive-data handling, harassment and self-harm policies, and refusal behavior with your own content.
- Evaluation: Measure factuality, groundedness, task success, bias, latency, and cost on a versioned test set.
- Observability: Capture request IDs, model versions, token counts, tool calls, latency, errors, and redacted traces.
- Support: Identify which party supports the model when it is supplied by a partner or community publisher.
Foundry explicitly brings role-based access, policy controls, tracing, monitoring, and evaluations into its documented platform experience. On AWS and Google Cloud, verify the equivalent controls in the specific services you select; do not assume that a similarly named governance feature has the same scope.
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Regional availability and quotas can change the answer
Model availability is regional and deployment-specific. A model shown in a catalog may be unavailable in your country, restricted to a particular region, limited to a preview, or subject to a quota too low for production. Throughput can also vary by account, model version, and whether you use on-demand or provisioned capacity.
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- Check model availability, context limits, rate quotas, and maximum concurrency in both regions.
- Request quota increases early; approval is not guaranteed on your launch schedule.
- Test failover with a model that has compatible behavior and an approved data path.
- Record the date of every availability check because catalogs and previews change.
Cost: build a common workload model
Official documentation does not establish a universal cheapest provider. A fair comparison must include more than model token rates.
| Cost component | What to measure |
|---|---|
| Inference | Input tokens, output tokens, cached tokens where applicable, requests, retries, and selected model version. |
| Embeddings and retrieval | Embedding volume, vector storage, index operations, reranking, and retrieval calls. |
| Endpoints or accelerators | Replica count, minimum capacity, uptime, scaling floor, GPU or custom-accelerator time. |
| Data and networking | Storage, inter-region transfer, cross-cloud egress, private connectivity, and backup. |
| Operations | Logs, traces, evaluations, monitoring, security scanning, and human-review tooling. |
| Commercial terms | Committed-use discounts, enterprise agreements, support plans, marketplace fees, and one-time migration work. |
Model three traffic patterns: steady baseline, normal variability, and a short-lived peak. A serverless API can win for low or irregular utilization, while managed capacity may be cheaper at sustained volume. Include retries caused by throttling and the cost of quality failures, such as re-running a weak extraction or sending a response to human review.
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Compare the complete developer loop: local testing, SDK quality, secrets handling, infrastructure as code, CI/CD, prompt and model versioning, evaluation gates, incident response, and rollback. A platform that looks powerful but does not fit your team’s deployment process can increase delivery time and operational risk.
AWS-oriented workflow
Use Bedrock for managed model calls, connect it to your existing AWS identity and data services, and add guardrails, retrieval, and observability as explicit architecture components. Move to SageMaker AI when you need custom training or endpoint control. Keep model selection behind an application interface so a Bedrock model change does not alter business logic.
Azure-oriented workflow
Use Foundry to manage model selection and application components when your organization already operates Azure subscriptions, resource groups, role assignments, and policy. Confirm the commercial category of each model before deployment: Azure-sold models and partner/community models differ in support, billing, and service-level commitments. Use Foundry’s tracing and evaluation capabilities as release gates rather than relying only on prompt review.
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Google-oriented workflow
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Three practical selection scenarios
Your data and operations are already on AWS
Start with Bedrock if the required model and region are available. Reuse existing identity, networking, storage, monitoring, and deployment practices. Escalate to SageMaker AI or specialized accelerators only when managed model access cannot meet customization, throughput, or control requirements.
Your enterprise is Microsoft-standardized
Start with Foundry when Azure identity, policy, and support contracts are central requirements. Decide separately whether each model should be an Azure-sold deployment or a partner/community deployment. Validate regional availability and billing before exposing the model to production traffic.
Your team’s core workflow is Google ML
Start with Model Garden and Vertex AI when model experimentation, customization, evaluation, and deployment need to live in one workflow. Verify that the selected model’s tuning method, endpoint type, and region meet production requirements; a catalog listing alone is not a deployment guarantee.
You are starting greenfield and have no cloud commitment
Run the same small pilot on each finalist using identical prompts, documents, safety tests, traffic assumptions, and acceptance thresholds. Compare task quality, p95 latency, failure modes, operational effort, and total cost. Do not choose from a product tour or a single benchmark.
A repeatable pilot plan
- Define success: Set measurable thresholds for answer quality, extraction accuracy, groundedness, latency, availability, safety, and monthly cost.
- Freeze the test set: Include normal requests, edge cases, long context, multilingual inputs if relevant, adversarial prompts, and tool failures.
- Choose equivalent deployments: Match model class, context length, temperature, retrieval corpus, region, and concurrency as closely as possible.
- Instrument everything: Record token usage, model version, retries, throttles, latency percentiles, tool calls, and redacted traces.
- Load-test traffic shapes: Test baseline, burst, and sustained utilization, including cold starts and quota behavior.
- Review governance: Confirm access roles, private networking, retention, audit logs, safety filters, and incident procedures.
- Calculate total cost: Include inference, retrieval, storage, egress, endpoint time, observability, support, and engineering work.
- Document exit criteria: State which results would disqualify a provider and what fallback model or region would be used.
Common mistakes and how to avoid them
- Choosing by catalog size: A large catalog does not guarantee the model, license, region, or quota you need. Validate the exact deployment.
- Comparing token prices alone: Retrieval, networking, endpoint minimums, retries, and monitoring can dominate the bill.
- Treating service maps as benchmarks: A product listed as an equivalent does not imply equal accuracy, latency, APIs, or price.
- Ignoring partner support boundaries: Confirm who operates, patches, bills, and supports a partner or community model.
- Building provider-specific business logic: Keep prompts, tool definitions, and response handling behind adapters and test contracts.
- Skipping regional checks: Availability, quotas, previews, and residency rules can invalidate an otherwise good design.
- Logging sensitive prompts: Redact personal, confidential, and regulated data before sending traces to observability systems.
- Failing to plan degradation: Define behavior for throttling, model outages, safety refusals, retrieval failure, and malformed tool output.
Final decision checklist
- Is the required model and modality available in every approved region?
- Are context limits, quotas, concurrency, and throughput sufficient for peak traffic?
- Does the deployment mode match your need for serverless simplicity or infrastructure control?
- Can identity, private networking, retention, audit, safety, and evaluation requirements be demonstrated?
- Have you priced tokens, retrieval, storage, networking, endpoints, observability, support, and migration?
- Can your team deploy, monitor, evaluate, and roll back the application with its existing tools?
- Is there a tested fallback model, region, or provider for a material outage or quota failure?
Use AWS Bedrock for a managed, model-independent AWS approach; use Microsoft Foundry for an Azure-governed application and agent platform; use Vertex AI and Model Garden for a Google-centered model lifecycle. Make the final choice only after checking the exact workload, region, model terms, controls, and traffic economics.
Quick Recap
Product prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on Amazon at the time of purchase will apply.
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