Short answer: Start with the cloud estate and governance controls you already operate. Azure AI Foundry is usually the best first choice for Microsoft identity, Azure data, Microsoft 365 integrations, and hybrid environments. Amazon Bedrock is the strongest starting point for AWS-native teams that want a managed, multi-provider model API. Google Vertex AI is the best fit when Gemini multimodality, very long context, Google data products, or a complete machine-learning lifecycle are central. Those are starting hypotheses, not universal rankings: validate the exact models, region, traffic pattern, latency target, safety policy, and data controls with a controlled bake-off.
At a glance: which platform fits which company?
All three services can expose foundation models, connect them to enterprise data, apply safety controls, and operate production applications. The practical difference is where each platform is deepest: Azure AI Foundry in Microsoft-oriented identity and enterprise application governance, Amazon Bedrock in AWS-native model access and managed generative-AI components, and Google Vertex AI in Gemini capabilities plus the end-to-end data-science and machine-learning lifecycle.
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| Decision factor | Azure AI Foundry | Amazon Bedrock | Google Vertex AI |
|---|---|---|---|
| Best starting context | Microsoft Entra ID, Azure data, Microsoft 365, private networking, or hybrid controls | AWS-native applications, IAM, VPC architecture, and a desire to change model providers behind one API | Google Cloud, Gemini, BigQuery, multimodal applications, and ML engineering teams |
| Model strategy | Azure and OpenAI models alongside Microsoft, partner, and open models | Amazon Nova and Titan plus partner models through a model-independent API | Gemini plus partner and open models in Model Garden |
| Published catalog figure | No stable total published in the reviewed Microsoft material; availability varies by date, region, and subscription | Over 100 models in Bedrock Marketplace, according to Amazon Web Services in 2025 | Over 200 enterprise-ready models in Model Garden, according to Google Cloud in 2025 |
| Application and agent layer | Agents, tools, tracing, evaluations, monitoring, and governance | Knowledge Bases, Guardrails, Agents and AgentCore, Flows, Data Automation, and prompt management | Grounding, tuning, deployment, monitoring, and agent/application tooling |
| Governance examples | Microsoft Entra identity, role-based access control, Azure Policy, network isolation, and content filters | AWS security integration, Guardrails, governed tool permissions, isolated sessions, and observability | Data residency, customer-managed encryption keys, VPC Service Controls, and Access Transparency where supported |
| Lifecycle emphasis | Application and agent control plane with evaluation and enterprise setup | Managed application and agent operations with component-based services | Broad ML lifecycle covering data, training, tuning, registry, pipelines, serving, and monitoring |
Do not compare the catalog figures as if they were the same measurement. Bedrock’s number refers to Marketplace models, while Vertex’s refers to enterprise-ready Model Garden models. Azure’s reviewed pages do not publish a comparable stable total.
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What Azure AI Foundry provides
A unified control plane for enterprise AI applications
Microsoft describes Azure AI Foundry as an environment that brings models, agents, tools, evaluations, monitoring, and enterprise governance into one workspace. Its documented controls include Microsoft Entra identity, role-based access control, content filters, network isolation, and Azure Policy. That combination matters when the same team already manages Azure subscriptions, private endpoints, policy assignments, and Microsoft identities.
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Foundry Models is a multi-provider catalog. It includes Microsoft and OpenAI offerings as well as Anthropic, Meta, and other partner or open models. Model availability is not a permanent property of the service: it can differ by region, subscription, quota, and date. Check the model picker and deployment documentation for the target region before designing an architecture around a specific entry.
Where Foundry is strongest
- Identity and access: Entra ID and Azure role-based access control can align model, agent, data, and deployment permissions with existing corporate identities.
- Hybrid and private networking: Network isolation and Azure Policy are useful when traffic must stay within approved network boundaries or comply with centrally managed policy.
- Evaluation and operations: The platform emphasizes tracing, monitoring, evaluations, and configurable enterprise setup rather than treating a model endpoint as the whole product.
- Microsoft data gravity: Teams already using Azure data services or Microsoft 365 generally avoid creating a second identity, networking, and logging pattern.
Microsoft summarizes the design as unifying agents, models, and tools under one management grouping with tracing, monitoring, evaluations, and customizable enterprise setup configurations.
What Amazon Bedrock provides
A model-independent API for AWS applications
AWS positions Bedrock as a fully managed service for building generative-AI applications. Its central proposition is a model-independent API: an application can call Amazon Nova or Titan and partner models such as Anthropic Claude, Meta Llama, Mistral, Cohere, AI21, and others without rewriting the entire integration for every provider. AWS describes this as single-API access that lets teams change foundation models with minimal code changes.
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AWS’s 2025 decision material says Bedrock Marketplace had over 100 popular, emerging, and specialized foundation models. That is a published Marketplace figure, not a promise that every model is enabled in every account or region. Access, throughput, modality, quota, and commercial terms still need to be checked for the deployment location.
Application services around the model call
- Knowledge Bases: Managed retrieval-augmented generation components for connecting applications to indexed enterprise content.
- Guardrails: Policies that evaluate inputs and outputs and can enforce content, denied-topic, sensitive-information, word, and image rules.
- Agents and AgentCore: Runtime and operational capabilities for tool use, MCP servers, permissions, governance, isolated sessions, memory, and observability across frameworks and models.
- Flows, Data Automation, and prompt management: Building blocks for orchestrating multi-step application behavior and handling documents or prompts.
Bedrock is consequently more than a routing endpoint, but each added component can introduce its own quota, meter, configuration, and operational dependency.
What Google Vertex AI provides
Gemini plus a full machine-learning lifecycle
Google defines Vertex AI as a machine-learning platform that combines data engineering, data science, and ML engineering workflows for training and deploying models and AI applications. Model Garden centralizes first-party, partner, and open models for discovery, testing, customization, deployment, and monitoring.
Google Cloud stated in 2025 that Model Garden offered over 200 enterprise-ready models. The count is a catalog snapshot, not a guarantee of universal regional availability. Google also states that Gemini supports a context window of up to 2 million tokens in the documented generative-AI overview. The applicable limit depends on the specific Gemini model, account, region, and feature.
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- Multimodality and long context: Gemini is a natural candidate for workloads that combine text, images, audio, video, or very large documents, subject to the selected model’s limits.
- Data and ML integration: Teams using Google data products and BigQuery can keep experimentation, feature preparation, model training, deployment, and monitoring in one cloud environment.
- Model choice: Model Garden provides first-party, partner, and open models in one discovery and deployment surface.
- Enterprise controls: Vertex documentation lists data residency, customer-managed encryption keys, VPC Service Controls, and Access Transparency for supported generative-AI features.
Vertex is therefore a particularly strong fit when generative AI is part of a broader ML program rather than an isolated application feature.
Model choice: catalog breadth is only the first filter
A large catalog helps only if the models you need are available in the regions, modalities, quotas, and commercial terms your application requires. Bedrock’s “over 100” and Vertex’s “over 200” figures use different counting scopes, so a larger published number does not prove better quality, lower cost, or easier deployment. Foundry does not publish a stable comparable total in the reviewed material.
Shortlist models by capability before counting them. Record the required modality, context length, structured-output support, tool calling, fine-tuning option, safety behavior, throughput, and regional availability. Then test at least one first-party model and one alternative provider where portability matters. A catalog entry that cannot meet your latency or data-residency requirement is not a usable option.
Questions to ask for every candidate model
- Is the model offered in the production region, and is access subject to approval or quota?
- What are the input and output token limits for the exact model version?
- Does it support the required images, audio, video, JSON schema, function calling, or tool protocol?
- Can prompts and outputs be excluded from provider training under the selected service and configuration?
- Are provisioned throughput, batch processing, caching, fine-tuning, or dedicated endpoints priced separately?
- What happens when the provider retires or replaces the model version?
Governance, security, and data boundaries
Governance is not one checkbox. Map each platform’s controls to your identity, network, data, safety, audit, and operational requirements.
Identity and access
Foundry’s Entra ID and Azure role-based access control are advantageous when users, service principals, and policy assignments already live in Microsoft’s identity plane. Bedrock fits AWS teams that rely on IAM, VPC controls, and existing AWS audit patterns. Vertex supports Google Cloud’s access model and adds documented controls such as VPC Service Controls for supported features. The right choice is usually the one that lets security staff reuse established groups, logging, separation of duties, and incident procedures.
Network and residency controls
Foundry documents network isolation and Azure Policy. Vertex documents data residency, customer-managed encryption keys, VPC Service Controls, and Access Transparency for supported generative-AI features. Bedrock inherits AWS security integration and adds service-level controls such as Guardrails and governed agent permissions. Confirm the exact feature, model, and region: a control listed for the platform may not apply identically to every model or workflow.
Input and output safety
Bedrock Guardrails can inspect both incoming prompts and generated responses, with policies for content, denied topics, sensitive information, words, and images. Foundry provides content filters. Vertex security controls address data and access boundaries, while model- and application-level safety settings still need to be tested against your policy. Run adversarial prompts, sensitive-data cases, and image or document attacks through the same pipeline you plan to operate in production.
Agents, retrieval, and operational complexity
Agent and retrieval features reduce the amount of infrastructure your team must build, but they can also increase platform coupling and the number of billable components.
Azure approach
Foundry groups agents, tools, evaluations, tracing, monitoring, and governance in one application-oriented control plane. This is useful when the team wants a centrally managed agent lifecycle with policy and evaluation close to deployment. Document how tools authenticate, where traces are stored, and how human approval is enforced for consequential actions.
Bedrock approach
Bedrock offers Knowledge Bases for retrieval, Guardrails for policy enforcement, and Agents and AgentCore for deployment and operations. AgentCore’s documented scope includes tool use, MCP servers, permissions, governance, isolated sessions, memory, and observability across frameworks and models. AWS explicitly describes component-based charges for agents and related services, so estimate those meters separately from model inference.
Vertex approach
Vertex combines grounding, tuning, deployment, monitoring, and agent/application tooling with Google’s model and data services. It is a strong fit when retrieval and agents must coexist with an existing ML pipeline, registry, evaluation process, or BigQuery-centered data workflow. Verify which agent features support your chosen model and region before promising portability.
Pricing and procurement: build a workload model, not a leaderboard
There is no defensible universal price ranking among these platforms. Model inference is generally metered, while tuning, endpoints, vector stores, agent components, guardrails, retrieval, networking, and observability can add separate costs. Prices and quotas change by model, region, version, and commitment.
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For Vertex open-model tuning, Google states that custom-training compute rates apply; deployment uses prediction or endpoint compute rates. Organization policies can also restrict model access. Bedrock’s agent and related services have component-based charges. Azure and the other providers publish model-specific rates that change over time, so procurement should verify current regional prices rather than rely on a static comparison article.
Procurement checklist
- Obtain current regional rates for every model version in the shortlist.
- Confirm minimum commitments, provisioned throughput, batch options, and cancellation terms.
- Ask whether prompt, response, embedding, retrieval, and evaluation traffic are metered separately.
- Price observability retention and security scanning, not only successful model calls.
- Model quota increases and regional failover capacity before signing a production SLO.
Lock-in and portability
Using multiple model providers behind one API can reduce migration work, but it does not eliminate lock-in. Application code may still depend on provider-specific tool schemas, safety filters, streaming formats, embeddings, vector indexes, agent memory, tracing, quotas, and identity policies.
Keep prompts, evaluation datasets, retrieval documents, tool contracts, and conversation records in exportable formats. Put a thin adapter around each model API, and record model version and region with every evaluation result. Separate business rules from provider-specific orchestration so that changing a model does not require rewriting authorization or data-access logic.
Managed retrieval and agent services can be worth the coupling when speed and operational simplicity matter. If portability is a strategic requirement, maintain an independent document store, embedding strategy, and test harness even when the first production deployment uses a native service.
Decision rules by starting situation
Choose Azure AI Foundry first when
- Entra ID, Azure Policy, Microsoft 365 or Azure data, private networking, or hybrid controls dominate the design.
- Security teams want one enterprise setup for agents, models, tools, evaluations, and monitoring.
- The application must fit established Azure identity, logging, and network operations.
Choose Amazon Bedrock first when
- The application already runs on AWS and must integrate with AWS security and networking patterns.
- The team wants to switch among Amazon and partner models through a managed, model-independent API.
- Managed Knowledge Bases, Guardrails, and agent operations are more valuable than owning every orchestration layer.
Choose Google Vertex AI first when
- Gemini multimodality or a documented 2-million-token context window is central to the use case.
- BigQuery, Google Cloud data products, or an existing Google ML engineering workflow are strategic.
- The team needs training, tuning, model registry, pipelines, serving, and monitoring in the same lifecycle.
Run a bake-off instead of guessing when
- Model quality, latency, regional availability, or total cost is uncertain.
- The workload has strict safety, residency, or audit requirements.
- Several clouds are already approved and migration risk matters more than initial setup speed.
How to run a fair platform bake-off
- Freeze the workload definition. Use the same user journeys, system instructions, retrieval corpus, tool definitions, response schema, region, and concurrency target on every platform.
- Build a representative test set. Include routine requests, long documents, ambiguous questions, multilingual examples, sensitive-data cases, tool failures, and adversarial prompts.
- Measure quality and safety. Score factual accuracy, citation or grounding behavior, refusal correctness, data leakage, structured-output validity, and tool-call precision.
- Measure operations. Record p50, p95, and timeout latency under the intended concurrency; test retries, rate limits, regional failover, streaming, and partial outages.
- Measure full cost. Include input and output tokens, caching, embeddings, retrieval, agent steps, guardrails, storage, logs, evaluations, and endpoint or training charges.
- Inspect governance evidence. Verify identity boundaries, private network paths, encryption keys, residency, audit records, retention, and administrator access for the exact services used.
- Test a model change. Swap to a second model on each platform and estimate code, prompt, evaluation, and operational changes. This exposes practical lock-in better than a feature checklist.
- Choose with weighted criteria. Set weights for quality, security, integration effort, latency, cost, portability, and team expertise. Keep the raw measurements so the decision can be revisited when prices or model versions change.
Implementation pitfalls and recovery plans
Choosing from the catalog before checking the region
A model may appear in a catalog but lack capacity, quota, or a required modality in the production region. Resolve this by validating access and throughput during the proof of concept, not after application code is complete.
Comparing headline token prices only
Low inference cost can be outweighed by retrieval, agent steps, safety checks, logging, or data transfer. Recalculate the complete workload model whenever the architecture adds a managed component.
Assuming a governance feature applies everywhere
Data residency, customer-managed keys, VPC controls, content filters, and audit features can vary by model, region, and service path. Record the exact supported combination and obtain security approval for that configuration.
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Native memory, tool schemas, and tracing are convenient but difficult to export. Keep canonical conversation state, tool contracts, and evaluation records under your control so a provider change remains feasible.
Ignoring failure behavior
Production incidents often involve throttling, malformed structured output, tool timeouts, stale retrieval indexes, or model retirement rather than a total service outage. Add bounded retries, validation, fallbacks, human escalation, and a tested model-version rollback path.
A practical selection sequence
- Map the existing estate: identity provider, data stores, network boundaries, security tooling, and ML skills.
- Write non-negotiable controls: residency, encryption keys, retention, audit, private connectivity, safety, and approval requirements.
- Shortlist models: select by modality, context, tool use, quality, quota, and region rather than catalog count.
- Estimate complete cost: model, retrieval, agent, tuning, endpoint, observability, and transfer charges for three traffic scenarios.
- Run the bake-off: use identical prompts, corpus, safety tests, concurrency, and failure cases.
- Design the exit path: export data and evaluations, isolate provider adapters, and document model-version dependencies.
- Recheck before procurement: confirm current prices, quotas, model availability, and contractual data controls for the selected region.
The resulting choice should be explainable in terms of integration effort, control coverage, measured application behavior, and complete operating cost—not a generic claim that one cloud is the best.
Frequently Asked Questions
Are Bedrock’s 100-plus models and Vertex AI’s 200-plus models directly comparable?
No. The figures use different scopes: AWS’s 2025 figure covers Bedrock Marketplace models, while Google Cloud’s 2025 figure covers enterprise-ready models in Vertex AI Model Garden. Availability also varies by region, account, and date.
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Does using a model-independent API remove cloud lock-in?
It reduces the code change needed to call another foundation model, but provider-specific tool schemas, guardrails, retrieval indexes, agent memory, quotas, identity policies, and observability can still create coupling.
What should a small team do if it cannot run a full multi-cloud test?
Start with the cloud where identity, data, networking, and operational expertise already exist. Test at least two models there, document the controls and costs, and preserve exportable prompts, evaluation data, documents, and tool contracts for a later second-cloud test.
When is a long context window preferable to retrieval?
Long context can simplify one-off analysis of very large, coherent documents, but retrieval remains useful for selective access, freshness, permissions, and controlling token cost. Test both approaches with your documents and access rules.
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