Vertex AI vs. Amazon SageMaker vs. Azure AI Foundry: Which Enterprise AI Platform Should You Choose?

Note on names: Vertex AI is a machine learning and generative AI platform from Google Cloud. Vertex Cyber Tech Solutions is an independent software, AI, cloud, and cybersecurity services company. We are not affiliated with or endorsed by Google. We build on Vertex AI alongside other platforms.

Google Vertex AI, Amazon SageMaker AI (with Amazon Bedrock), and Microsoft Azure AI Foundry are the three leading cloud platforms for building enterprise AI. They all provide access to foundation models, tools for retrieval and agents, model training, and managed deployment. The practical differences lie in model catalogs, data platform integration, and how well each fits your existing cloud. Product names change often, so verify details in each vendor's current documentation.

Side-by-side comparison

How to choose

1. Start with your data gravity. Moving large datasets between clouds adds cost, latency, and compliance work. Build AI where the data already lives.
2. Test the models on your own task. Benchmarks rarely predict performance on your documents and edge cases. Run the same evaluation set across candidate models.
3. Check governance requirements: regions, private networking, encryption keys, audit logs, and contractual data use terms.
4. Consider your team's skills and existing tooling, such as Terraform modules, CI/CD, and monitoring.
5. Model total cost: tokens, deployed endpoints, vector storage, data egress, and engineering time. See our cloud cost guide.

Multi-cloud and portability

You do not have to commit everything to one vendor. Common patterns include:

• A thin internal abstraction layer over model APIs, so you can switch providers for specific tasks.
• Open-source vector databases and orchestration frameworks that run anywhere. See our vector database comparison.
• Open models that can be self-hosted if requirements change.

Balance portability against the speed of using each platform's managed services. Our broader AWS vs. Azure vs. Google Cloud comparison covers non-AI factors.

Generative AI capabilities compared

All three platforms now offer access to large language models, tools for grounding answers in your data, and frameworks for building agents, but they approach it differently.

• Google Vertex AI offers first-party Gemini models with long context windows and strong multimodal capabilities, plus Model Garden for open and third-party models. Grounding with Google Search or your own data through Vertex AI Search is built in, and agent tooling integrates with Google Cloud services.
• AWS splits the experience: Amazon Bedrock provides managed access to foundation models from several providers, including Anthropic, Meta, Mistral, Amazon, and others, with knowledge bases for retrieval and agents for tool use. SageMaker covers custom training, fine-tuning, and hosting of your own models.
• Microsoft Azure AI Foundry provides access to OpenAI models through Azure OpenAI alongside a catalogue of other models, with Azure AI Search for retrieval and close integration with Microsoft 365, Teams, and Copilot extensibility.

In practice, the deciding factor is often which model family performs best on your tasks and which data sources you need to ground on. Test candidate models on your own evaluation set rather than relying on general benchmarks.

Classical ML and MLOps capabilities

For predictive models built on structured data, all three platforms offer managed notebooks, training jobs, hyperparameter tuning, model registries, managed endpoints, batch prediction, pipelines, and monitoring. The differences are in depth and ergonomics:

• SageMaker has the broadest set of individual ML features and is mature for teams that want fine control over training infrastructure, distributed training, and deployment options.
• Vertex AI integrates particularly well with BigQuery, which suits teams whose data already lives there, and offers a unified interface for AutoML, custom training, pipelines, and monitoring.
• Azure Machine Learning fits organizations using Azure data services and Microsoft tooling, with strong support for responsible AI dashboards and enterprise governance features.

Security, identity, and compliance

Each platform inherits its cloud's identity and security model: IAM and VPC Service Controls on Google Cloud, IAM and VPC endpoints on AWS, and Microsoft Entra ID with private endpoints on Azure. All three offer encryption with customer-managed keys, private networking options, audit logging, and data residency controls, and all publish compliance certifications for major standards. The practical question is which identity system your organization already manages well. Running AI workloads in the cloud where your identity, logging, and security tooling are mature usually reduces risk more than any single feature comparison. Our cloud security guide covers these controls across providers.

Comparing costs fairly

Headline prices per token or per instance hour are hard to compare directly because models, machine types, and discount programs differ. A fair cost comparison follows these steps:

1. Define two or three representative workloads, such as a chat assistant handling a set number of conversations a month, a nightly batch extraction job, and a custom model endpoint.
2. For each platform, select the model or machine that meets your quality and latency targets in testing, not the cheapest option on paper.
3. Estimate monthly volume, including tokens in and out, storage, vector search, data transfer, and logging.
4. Include committed-use or reserved capacity discounts and any existing enterprise agreements, which can change the picture significantly.
5. Add the engineering cost of integration and operations; a platform that saves a few percent on compute but needs much more engineering time is not cheaper.

Our cloud cost optimization guide explains how to keep spending under control once you have chosen.

Running a platform proof of concept

When the choice is not obvious, a time-boxed proof of concept on two platforms is the most reliable way to decide. Keep it small and comparable:

• Use the same use case, data sample, and evaluation set on both platforms.
• Limit the scope to two to four weeks, with a written list of criteria and weights agreed in advance.
• Measure answer quality or model accuracy, latency, estimated monthly cost, development effort, and how well each platform fits your security and deployment practices.
• Involve the team who will operate the system, not only the people who build the prototype.

Score each criterion, document the trade-offs, and make the decision with both the results and your longer-term cloud strategy in mind.

Example decisions

A retailer on Google Cloud with sales and product data in BigQuery chooses Vertex AI, because Gemini can work directly with its data through BigQuery integration and its team already manages Google Cloud IAM.

A fintech company standardized on AWS chooses Bedrock for generative features and SageMaker for its fraud models, keeping all workloads inside its existing AWS security controls and accounts.

A professional services firm using Microsoft 365 chooses Azure AI Foundry so its assistant can integrate with Teams and SharePoint and use the firm's existing Entra ID permissions.

In each case, the platform followed the data, identity, and team skills, not a feature checklist.

Avoiding lock-in without losing managed benefits

Managed AI services save time, but every platform-specific API you adopt makes switching harder. You can keep options open without giving up the benefits:

• Wrap model calls in your own service layer, so the rest of the application calls a stable internal interface rather than a vendor SDK directly.
• Keep prompts, evaluation sets, and retrieval logic in your own code and repositories, not only in platform consoles.
• Store source data and embeddings in portable formats, and record which embedding model created each index, so you can rebuild indexes elsewhere.
• Use containers and open frameworks such as PyTorch, scikit-learn, and standard model formats for custom models.
• Use infrastructure as code so environments are documented and reproducible.

This approach also makes it easier to use more than one platform where it genuinely helps, for example a model available on only one cloud for a specific task.

Questions to ask before deciding

1. Which cloud already hosts most of our data and applications?
2. Which identity and security tooling does our team manage confidently?
3. Which model family performs best on our evaluation set?
4. Do we need predictive ML, generative AI, or both?
5. Which platform best fits our compliance and data residency requirements?
6. What existing commitments or discounts do we have with each provider?
7. Which platform can our team operate day to day without heavy outside support?

Related reading

For a deeper look at each option, see our Google Vertex AI platform guide and Vertex AI deployment guide. For cloud strategy beyond AI, read our AWS vs. Azure vs. Google Cloud comparison, and for help choosing a delivery partner, see how to choose an AI development company.

Developer experience and team skills

The platform your engineers can use productively is often more important than small differences in features. Consider how each option fits your team:

• Languages and SDKs: all three provide Python SDKs and REST APIs, with varying support for other languages. Check that the SDKs your team prefers are well documented and maintained.
• Local development: how easily can developers test prompts, pipelines, and models locally or in a sandbox project before deploying?
• Infrastructure as code: Terraform support for AI resources varies by service and changes frequently; confirm the resources you need can be managed in code.
• Observability: logs, traces, and cost data should flow into the monitoring tools your operations team already uses.
• Training and community: documentation quality, examples, and the availability of experienced engineers in your hiring market all affect delivery speed.

If your team has deep experience with one cloud, the productivity gained from staying there usually outweighs a modest advantage elsewhere, unless a specific model or capability is decisive for your use case.

Reviewing the decision over time

AI platforms change quickly. New models, pricing changes, and new managed services appear every few months. Review your platform choice at least once a year against the same criteria you used to make it: model quality on your evaluation set, cost per workload, security fit, and team productivity. Because a well-designed architecture keeps model calls behind your own service layer, adding a second provider for a specific task or switching for a new workload becomes a manageable project rather than a rewrite.

Summary: which platform fits which organization?

Choose Vertex AI if your data lives in Google Cloud and BigQuery, you want first-party Gemini models, or you need strong multimodal and long-document capabilities. Choose AWS Bedrock and SageMaker if your applications and security controls are standardized on AWS, or you want a choice of foundation models from several providers alongside mature custom ML tooling. Choose Azure AI Foundry if your organization runs on Microsoft 365 and Entra ID, or you want OpenAI models within Azure's enterprise controls. When none of these factors is decisive, run a short proof of concept on your own data and let the results decide.

Not every AI project needs a large language model. For forecasting, scoring, and fraud detection on structured data, read our guide to machine learning solutions and business use cases.

Get an independent platform assessment

Vertex Cyber Tech builds AI on all three major clouds, so we can run a structured, side-by-side evaluation on your data. Explore our AI/ML services and cloud services, or book an assessment call.

Sources and further reading

• Google Cloud: Introduction to Vertex AI
• AWS: Amazon Bedrock
• AWS: Amazon SageMaker
• Microsoft Learn: Azure AI Foundry documentation