Deploying AI in the Cloud: AWS, Azure, and GCP Compared

You’ve trained a model that works. Maybe it’s a fine-tuned LLM for internal document search, or a computer vision model that classifies manufacturing defects. Now you need it running in production serving predictions reliably, scaling with demand, and not burning through your budget while idle. That’s where the choice of “AI in the cloud” platform actually matters.

AI, especially generative AI and AI agents built on large language models (LLMs), rely on cloud services to build, scale, deploy, and power efficiently. As a result, most organizations now rely on cloud platforms to implement AI, often using pre-made AI as a service (AIaaS) to reduce operational complexity.

When deploying AI, the choice typically comes down to three cloud services businesses: Amazon Web Services (AWS), Azure, and Google Cloud Platform (GCP). Each has its own strengths and weaknesses, but all of them can provide scalable, cost-effective infrastructure solutions needed to build and deploy AI. 

Understanding AI in the Cloud

The vast majority of AI relies on cloud infrastructure, due partly to the immense amounts of computing power involved in many machine learning applications.

What It Means and Why It Matters

The term “AI in the cloud” refers to the integration of AI technologies with cloud computing, in which data is stored and programs are run through offsite data centers and servers. Cloud services make powerful, highly scalable AI accessible and affordable by eliminating the need for costly on-site infrastructure for data processing. 

In practice, most cloud and hybrid cloud AI systems are typically powered by Azure, AWS, or GCP. These platforms provide the infrastructure and managed services needed to train models, deploy them into production, and scale AI workloads as demand grows. 

Major enterprise companies are already seeing the benefits from this approach. Netflix, for example, has been able to use machine learning for a number of key features. One being, use in their personalization recommendations, employing deep learning models to analyze individual users’ behavior and preferences, and delivering content they’re most likely to want to watch. Another being, the use of cloud AI for analyzing their vast amounts of data on trends, engagement, metrics, and social media sentiment.¹

Core Business Benefits

Cloud AI offers several advantages over on-site infrastructure. Key benefits include:

• Cost savings: Cloud platforms leverage massive economies of scale and eliminate the need for up front hardware expenditure.² Pay-as-you-go pricing allows you to align costs more closely with usage, reducing financial risk. 
• Agility: Cloud AI enables rapid experimentation and iteration by giving you  on-demand access to resources you need.
• Scalability: As AI workloads grow, cloud platforms let you scale training and inference resources up or down based on demand, without re-architecting your infrastructure. 
• Collaboration: Cloud computing provides a centralized platform, streamlining communication and collaboration between developers, data scientists, and analysts.
• Innovation: Cloud AI reduces time to production and time to market, and can significantly speed up the rate at which your team is able to innovate and improve.

If you’re new to Cloud AI, Udemy offers a comprehensive online course, Introduction to Cloud Computing with AWS, Azure, and GCP, designed to help you get up to speed with hands-on labs and informative lectures from top AI and cloud computing experts. 

Why Cloud AI Is Transforming Innovation

Incorporating AI tools into processes and workflows can be a powerful accelerant for innovation.

Cloud AI solutions allow for:

• Massive computing power on demand. AI is resource-intensive, but cloud services give you access to the power necessary for training complex machine learning models.
• Faster prototyping. Cloud AI tools are equipped to analyze huge datasets, informing a data-driven approach to generating concepts, design ideas, and mockups quickly.
• Access to pre-built AI tools and their APIs. AI as a Service solutions from platforms like Azure, GCP, and AWS can provide you with robust, pre-trained AI models for you to integrate.
• Faster QA and testing. Cutting edge AI tools can run thousands of simulations and carry out testing tasks at lightning speed, incorporating real-time analytics.
• Huge amounts of data available to inform decisionmaking. Cloud AI can process huge volumes of real-time data, producing valuable insights into market trends, customer decisionmaking, and online brand sentiment.

Comparing AWS, Azure, and GCP for AI Deployment

When selecting an AI cloud provider, the options usually come down to AWS vs Azure vs GCP. They differ in the respective strengths of their ecosystems, how easy they are to use, and what their ideal user profiles generally look like. The best options for different organizations can depend on factors such as business size, industry, and how AI is being utilized.

As of Q4 2025, AWS holds roughly 30% of the global cloud infrastructure market, Azure sits at about 20%, and GCP at around 13%. Together, they control over 60% of a market that now exceeds $400 billion in annual revenue.³

Amazon Web Services (AWS): Control at Scale

Amazon Web Services is a widely used cloud platform, with over 4 million companies worldwide relying on their services.⁴ 

AWS’s philosophy is to give you granular control over every part of the stack. SageMaker, its flagship ML platform, supports the full lifecycle from data labeling through training to deployment.

You can choose from built-in algorithms, bring your own containers, and deploy models to real-time endpoints, batch transform jobs, or asynchronous inference queues.

In practice, SageMaker offers more deployment flexibility than its competitors. It supports multi-model endpoints serving several models from a single instance to optimize resource usage and asynchronous inference via SQS, which lets you process variable workloads and scale down to zero instances when there’s no traffic. 

Google’s Vertex AI, by comparison, currently lacks a managed async option and cannot scale endpoints to zero.

On the hardware side, AWS has invested heavily in custom silicon. Trainium chips are purpose-built for model training, while Inferentia handles inference at significantly lower cost than equivalent GPU instances AWS claims 30–60% savings on total cost of ownership compared to NVIDIA-based configurations.⁵

For teams that need standard GPUs, AWS also offers the latest NVIDIA hardware (P5 instances with H100s), though you’ll need to request quota separately from EC2, a small but annoying operational detail.

Some of their most important and widely used AI services include:

• Amazon Bedrock, a fully managed service that currently powers generative AI for over 100,000 companies around the globe. It encompasses pre-trained models and infrastructure, customization, safety guardrails, and more. 
• Amazon Q, a virtual assistant powered by cutting edge generative AI that helps employees access and use internal data.
• Sagemaker, a centralized product that integrates analytics and AI so you can build, train, and deploy machine learning models.
• A selection of pre-trained AI services for different use cases, designed for users without ML expertise. These include Rekognition for analyzing videos and images, Amazon Forecast for forecasting, and Amazon Lex for developing chatbots. 

AWS’s range of cloud-based offerings are positioned to support the full end-to-end machine learning lifecycle from data preparation and model development, to LLM deployment and ongoing maintenance. 

These tools are tightly integrated with the broader AWS ecosystem and are designed to scale efficiently, with support for specialized hardware, distributed training via Sagemaker, and scalable data pipelines.

While AWS is outstanding for cloud AI, it does have a couple of potential drawbacks:

• Its pricing structure is relatively complex, and can be confusing or unclear at times. Additionally, setting up AWS AI tools also has a relatively steep learning curve, which could make it less ideal for newer users and some use cases.
• AWS gives you the most knobs to turn, but that means more knobs to get wrong. IAM permissions, VPC networking, instance selection, and endpoint configuration all demand AWS fluency. If your team already lives in the AWS ecosystem and has the engineering depth, it’s exceptionally powerful. If you’re a small team looking to ship fast, the learning curve is real.

Microsoft Azure: Enterprise-Ready and Ecosystem-Deep

Microsoft Azure is a fast-growing cloud service platform, currently supporting over 53,000 AI customers and including the popular generative AI tools GPT-4 and DALL-E.⁶ 

Azure offers a tiered AI and Machine Learning platform. They have options for “out-of-the-box” models that are easy to implement, as well as tools designed for data scientists who are building their own custom models. 

Some of the foremost Azure AI services include:

• Azure Machine Learning, a full-lifecycle MLOps tool for engineers and data scientists to build proprietary custom machine learning models. Automated model training, deployment to managed endpoints or Kubernetes clusters, built-in model monitoring, and responsible AI dashboards. It’s solid, well-documented, and designed for teams that care as much about governance as they do about model accuracy.
• Azure AI Services, for integrating pre-built AI capabilities into apps, for developers and IT professionals without extensive ML expertise. It incorporates models designed for common tasks, including speech recognition, computer vision, translation, and speech-to-text.
• Azure AI Studio, now unified with Azure AI Foundry; designed around low-code and no-code AI creation solutions for developers and prompt engineers. 
• Azure AI Cognitive Services, formerly separate but now integrated into Azure AI Services. This product is for embedding existing pre-built AI tools into apps via API calls, without the need for machine learning expertise. 

Azure supports a wide range of use cases and skill levels, offering both robust tools for  ML-specialized engineers and low-code or no-code solutions for faster implementation.  

Also with its built-in compliance support for HIPAA, FedRAMP, and financial regulations, combined with hybrid cloud deployment via Azure Arc, makes it the default for healthcare organizations, government agencies, and large financial institutions that need to keep some workloads on-premises.

Azure also integrates seamlessly with the broader Microsoft ecosystem, and supports hybrid cloud deployment, making it a strong fit for organizations that already operate their own infrastructure. If your organization runs on Active Directory, Office 365, and Windows Server, Azure ML slots in with remarkably little friction. 

Azure can have its drawbacks, however, particularly for certain contexts and use cases. 

• Like AWS, it has a complex and often opaque pricing system. 
• On hardware, Azure primarily offers NVIDIA GPUs (H100, A100) and is developing its own Maia chip, though Maia is still emerging and not yet widely available. This means Azure doesn’t currently match the cost-per-inference advantages that AWS gets from Inferentia or GCP gets from TPUs.

Google Cloud Platform (GCP): Clean, Research-Oriented, Cost-Competitive

The third big player in the cloud AI space is Google Cloud Platform. GCP offers an integrated AI ecosystem that’s versatile, centralized, and known for its overall ease of use. 

GCP’s key cloud AI offerings include:

• Vertex AI, a centralized, fully-managed AI development platform. It encompasses Vertex AI Studio, which offers access to the latest Gemini models, foundation models, and APIs for rapid prototyping and testing of AI models; as well as Agent Builder, an enterprise-oriented platform for developing AI agents using internal data. 
• AutoML, an automation-focused tool suite under the Vertex AI umbrella for building AI models with minimal ML expertise.
• Generative AI Studio, also a part of the Vertex AI platform. It features an extensive catalog of over 200 foundation models, including Gemini, Imagen, and others.

Google’s Vertex AI provides a centralized platform that supports streamlined workflows across the AI and ML development lifecycle, with a noticeably cleaner UX than SageMaker or Azure ML that includes  no-code and low-code options for nonspecialists. 

GCP is also known for its strong native support for popular open-source ML frameworks such as TensorFlow, Jax, PyTorch,  offering a high degree of flexibility and control over your code. For teams that want to go from a Jupyter notebook to a production endpoint with minimal configuration, Vertex AI offers the shortest path.

As a result, GCP is an excellent cloud platform option for data-driven teams,researchers, and startups. They also offer generous Sustained Use Discounts and Committed Use Discounts, helping to mitigate costs. 

GCP’s biggest hardware differentiator is TPUs (Tensor Processing Units). The latest generation, Ironwood, offers 192 GB of HBM per chip and can be clustered into pods of over 9,000 chips for massive training runs.⁷

For inference, TPU v5e delivers roughly 3x better throughput per dollar compared to the previous generation, making high-volume serving of large models significantly more affordable. TPU v5e can serve LLaMA 2-70B at approximately $0.30 per million output tokens—a fraction of what equivalent GPU configurations would cost.⁸

GCP also has the strongest native support for open-source ML frameworks. TensorFlow and JAX are first-class citizens on TPUs, and PyTorch support has improved substantially through PyTorch/XLA. If your team standardizes on these frameworks, the integration is tight.

Though it has many upsides, GCP remains the smallest of the big three cloud providers. Its overall ecosystem may not be as mature as its competitors, which are more widely adopted in enterprise environments.

Also price wise, Vertex AI endpoints currently cannot scale to zero replicas, meaning at least one instance must keep running, which means you’re paying for baseline capacity even during zero-traffic periods. TPUs are also exclusive to GCP, so if portability across clouds matters, any TPU-optimized code creates lock-in.

That concludes the comparison of the features of AWS, Azure and GCP. If you’re considering a cloud certification, and you’re not sure which one may be right for you, check out our Which Cloud Certification Should You Get in 2026? (AWS vs GCP vs Azure) article to learn more and find out which certification may be right for your growth. 

How To Choose the Right Cloud for Your AI Strategy

AWS, Azure, and GCP all have their own advantages, as well as their drawbacks. Here’s how to determine which provider makes the most sense for your organization’s AI strategy.

Cloud Provider	Key Strengths (Pros)	Potential Drawbacks (Cons)
AWS	• Industry leader in scale and maturity for AI and ML services • Excellent for large-scale, production-grade workloads • Strong scalability and global infrastructure  • Access to specialized and high-performance hardware  • Deep integration with the broader Amazon ecosystem	• Can feel complex and overwhelming for beginners  • Steeper learning curve for teams without strong ML expertise  • Costs can rise quickly without careful optimization
Azure	• Strong choice for enterprise and regulated industries  • Excellent compliance, governance, and security features  • Seamless integration with Microsoft tools like Windows, Active Directory, and Office 365  • Well suited for hybrid cloud and on-premises environments • Broad AI services without requiring niche hardware	• Less specialized high-end hardware compared to AWS and GCP  • Some AI services may feel less cutting-edge for experimental use cases  • Best value often depends on existing Microsoft licensing
Google Cloud Platform (GCP)	• Strong focus on AI research, experimentation, and innovation  • Clean, intuitive user interface  • Excellent support for open-source frameworks  • Competitive pricing and discounts for startups and SMBs  • Good no-code and low-code tools for nonspecialist teams	• Smaller enterprise footprint than AWS and Azure  • Fewer enterprise-focused governance features  • Less suitable for heavily regulated or compliance-driven industries

Evaluate Your Technical and Business Goals

Both technical considerations, and your organization’s broader business goals, are important factors to consider. The sections below provide additional guidance on how to evaluate and choose the platform that best aligns with both.

Some of the key elements to take into account when choosing a cloud AI platform include:

• Overall key priorities. Is scalability a central need? Are you in an industry or context in which compliance is paramount? Is cost efficiency a factor? Azure leads in compliance and hybrid deployment. GCP is the weakest here. AWS is somewhere in between.
• Existing infrastructure. Does your organization already have on-premises infrastructure? 
• Team skill levels. Do you have ML experts or require no-code or low-code options for nonspecialists? SageMaker requires AWS fluency. Azure ML benefits from Microsoft ecosystem familiarity. Vertex AI is probably the quickest to pick up for someone without deep experience in any of the three.
• Which software ecosystems are you already embedded in? Each company’s cloud platform is well integrated with their broader software ecosystem. Migrating training data between clouds adds latency and egress costs. The platform where your data already lives has a strong default advantage.
• Do you need to scale to zero? Only SageMaker’s async endpoints support true scale-to-zero. If you have bursty, unpredictable traffic, this matters for your bill.
• How important is custom silicon? If inference cost at scale is a primary concern, AWS (Inferentia) and GCP (TPUs) offer meaningful advantages over standard GPU pricing. Azure is betting on Maia but it’s not there yet.

Use-Case Examples

The answer to which cloud provider is best for AI will largely come down to your particular use case. Here are some examples of use cases where each of the big three cloud providers is the strongest.

AWS may be the right solution if you:

• Require large-scale production workloads
• Are already embedded in Amazon’s software ecosystem
• Scalability is a key concern
• You may benefit from access to specialized hardware
• Your team has a high level of expertise and proficiency with AI and ML

Azure may be the most sensible if you:

• Are an enterprise-level company
• Are already embedded in Microsoft’s software ecosystem
• Are in a sensitive industry like healthcare or finance, where compliance and security are crucial
• Have on-premises infrastructure, and intend to employ a hybrid cloud approach
• Don’t have a need for certain kinds of very high-end specialized niche hardware

Google Cloud Platform is an excellent option if you

• Are in research and development, and/or are involved heavily in ongoing experimentation
• Are a small to midsize business
• Are an AI-focused startup
• Are looking for a clean, intuitive UI
• Have a nonspecialist team looking for no-code or low-code tools
• Are on a budget and may qualify for one of GCP’s discounts
• Benefit from integrated access to a variety of open-source frameworks

Building for the Future with Hybrid and Multi-Cloud AI

Interest in hybrid cloud and multi-cloud solutions has grown as organizations look to balance flexibility, cost, and control when deploying AI workloads. As of 2025, Gartner reports that 76% of enterprises use more than one public cloud provider.⁹ 

• A hybrid cloud approach combines cloud infrastructure with a company’s existing on-premises infrastructure, allowing organizations, particularly large enterprises, to improve cost efficiency by balancing capital expenditure on private resources with operational expenditure on cloud services. 

• Multi-cloud AI strategies, meanwhile, distribute AI workloads to multiple cloud service providers to leverage the advantages of different platforms and reduce the risk of vendor lock-in.  To support portability across environments, teams often rely on containerization tools like Docker (lightweight software packages that bundle an application’s code with its dependencies) alongside orchestration tools such as Kubernetes. 

Together, these approaches help balance performance, redundancy, and data sovereignty when deploying AI at scale.

A hypothetical example of a multi-cloud workflow, in order to combine the best features of each platform, could include:

• Using AWS’s Amazon S3 to store raw, historical, and proprietary data, taking advantage of free data ingress while avoiding the expense of data egress.
• Using GCP for data processing–for example, implementing Google BigQuery Omni as an analytics engine that’s amenable to scaling. GCP would host an execution environment running queries and processing on the S3 data.
• Utilizing a combination of AWS and GCP for developing and deploying AI models, favoring whichever has better efficiency for a given specific model. 

This kind of multi-platform approach can help optimize costs, while taking advantage of each platform’s best features.

Best Practices for Managing AI Workloads

There are some best practices that can help keep AI workloads in check, optimizing costs and benefitting performance.

Streamline Data Pipelines and Governance

AI models are only as good as the data they’re trained on, so maintaining data pipelines that are both robust and compliant is important for managing AI workloads in the cloud. For sensitive data, governance factors like version control and compliance are critical. The overall goal is to construct a unified data governance framework that spans your entire organization. 

Each major platform offers their own managed tools, ETL, and data integration –AWS Glue, Azure Synapse, and Google’s BigQuery.

Implement MLOps for Long-Term Scalability

Machine learning operations (MLOps) produces a set of best practices for automating and standardizing the entire ML product lifecycle.

• Standardized deployment: AI models move through a consistent set of stages through their development cycle. This helps minimize manual errors and speeds up release cycles.
• Automated Monitoring and Maintenance: MLOps workflows manage model retraining automatically, ensuring the model remains accurate throughout production.
• Reproducibility and Auditability: Every model version, its associated data, code, and training parameters, is tracked and logged. 

Two of the most widely recommended open-source tools for MLOps are MLFlow and Kubeflow.

Optimize Costs and Performance

Managing AI workloads effectively depends on balancing computational requirements with budgetary constraints. 

Best practices for balancing cost with performance can include:

• Strategic Instance Usage: Use auto-scaling features, so you only pay for the capacity needed during peak traffic, and scale down during lulls. 
• Budgeting and Monitoring: Tools like AWS Cost Explorer or Azure Cost Management can be used to forecast and analyze spending.
• Resource Tagging: Implement a rigorous resource tagging policy, to easily attribute complex AI spending to specific business outcomes.

Refining performance to increase the efficiency of processes will help to mitigate costs.

• Infrastructure Right-Sizing: Reducing the size of the instance, without impacting performance quality.
• Load Testing and Stress Testing: Identifying bottlenecks prior to moving a model into production. 
• Specialized Hardware: Leveraging cost-efficient specialized hardware offered by the cloud service providers for specific tasks.

Future of AI in the Cloud

The rapid ongoing rise and expansion of generative AI technology is reshaping cloud strategies, as organizations determine their best options for cloud platforms and services to power agile, scalable, resilient AI systems. 

As multi-cloud and hybrid approaches grow in prominence, we’re seeing the rise of interoperable AI ecosystems that aren’t fully locked into any one vendor.

Cloud AI is a fast-growing area, with a large amount of career growth potential for forward-looking professionals who cultivate expertise in AI, machine learning, and their interface with cloud computing technologies.

If you’re ready to deepen your knowledge and start building practical cloud AI skills, Udemy offers a wide range of courses taught by industry experts, covering everything from cloud fundamentals and certifications to hands-on machine learning, MLOps, and generative AI. 

Start learning today and take the next step toward building and deploying AI confidently in the cloud.