How to choose the best cloud for handling AI workloads: A comprehensive guide

As enterprises increasingly adopt Artificial Intelligence to tackle complex tasks, manage growing data, and navigate AI-driven innovation, selecting the best cloud for handling AI workloads has become a critical decision. According to Grand View Research, the Global Cloud AI market is constantly growing, estimated at $87.27B in 2024 and projected to grow at a CAGR of 39.7% between 2025 and 2030 to $648.7B. However, with different cloud providers each offering cutting-edge AI services and powerful ML tools, organizations must consider: Is it the best cloud for handling AI workloads? This guide will help you compare strengths and choose the right cloud services tailored to your unique needs.

Types of AI workloads

Choosing the best cloud provider for AI also depends on the types of workloads you need to process. Let's discover the key AI/ML workloads:

• Data processing workloads involve preparing and transforming raw data for AI use, including ETL (extract, transform, load) tasks and feature extraction.
• Deep Learning workloads involve training multi-layered neural networks to handle complex tasks such as image analysis and speech processing.
• Natural Language Processing (NLP) workloads enable machines to process and generate human language for tasks like translation and sentiment analysis.
• Generative AI workloads help create new content using models like Large Language Models (LLMs) for text and diffusion models for images and videos.
• Computer vision workloads process visual data for object detection and facial recognition, relying on deep learning models like Convolutional Neural Networks and transformers.

Training these models requires large datasets, substantial computational resources, specialized hardware accelerators like GPUs (graphics processing units) and TPUs (tensor processing units), etc. Cloud computing can cover these needs, enabling companies to process various AI workloads tailored to their requirements.

AWS vs Azure vs GCP: Comparing the big three for AI workloads

Which cloud provider is best for AI? It depends on your needs, budget, and current infrastructure. AWS, Azure, or GCP can be your best cloud for handling AI workloads, as they offer plenty of features and customized platforms. We compared them to highlight key strengths and tools to help you select the right one.

AI workloads on AWS

Amazon Web Services is often hailed as a pioneer in cloud computing, known for its scalability, innovation, and extensive global network. For AI workloads, AWS excels with a wide range of services tailored to meet the needs of machine learning, deep learning, and data analytics. The platform offers high-performance infrastructure, including EC2 instances optimized for GPUs, which are essential for training complex AI models.

For example, N-iX AI and cloud experts helped our client company build a deep learning solution using AWS virtual machines, such as EC2, and the S3 storage service. Our engineering team partnered with an enterprise developing intelligent transport solutions for police, government, and traffic departments. They aimed to launch a new product with an in-built AI solution to detect driver violations and enhance road safety.

Utilizing AWS deep learning tools, our cloud engineering team developed high-accuracy detection models for seatbelt usage violations and distracted driving behaviors. These models achieved detection rates of 88% and 91%, respectively. Additionally, the real-time processing capabilities supported by AWS helped streamline data handling, enabling rapid decision-making, improving operational efficiency, and ensuring road safety.

Read a full case here: Increasing market reach with traffic management and computer vision

The AWS ecosystem also includes SageMaker, a cornerstone of its AI offerings, enabling developers to build, train, and deploy models efficiently. Moreover, the AWS ecosystem offers seamless connectivity for AI models with various services. For example, businesses can link AI models to serverless functions like Lambda or analytics tools like Redshift, which makes it a preferred choice for organizations aiming to unify their AI and data strategies.

Amazon offers a variety of ready-to-use AI services, tools, and platforms, including:

• Amazon Bedrock: A managed service that simplifies deploying foundation models (FMs) from top providers such as Anthropic, Meta, and Stability AI.
• Amazon Rekognition: A robust solution for analyzing images and videos to detect objects, faces, and activities.
• Amazon Polly and Amazon Lex: Tools designed for text-to-speech conversion and building conversational AI applications, respectively.

Choosing the best cloud for handling AI workloads depends on your requirements. Select AWS if you want to leverage some of the following advantages:

• Broadest service catalog with 200+ AI/ML services
• Comprehensive MLOps platform (SageMaker ecosystem)
• Extensive global infrastructure (25+ regions)
• Mature cost optimization tools and pricing models
• Custom Inferentia/Trainium chips for cost-effective inference
• Largest partner ecosystem and community support
• Comprehensive compliance certifications

AI workloads on Azure

Microsoft Azure offers a balanced ecosystem for AI workloads, combining cutting-edge tools with seamless integration into enterprise IT environments. Though AWS and Azure are continuously striving to establish themselves as the best cloud for handling AI workloads, Azure excels in solutions requiring hybrid or on-premise solutions due to Azure Arc and other tools that enable hybrid cloud management with AI.

Azure's model catalog includes hundreds of models from Microsoft, OpenAI, Mistral, Meta, and Cohere. Its prompt flow streamlines the development cycle of ML- and LLM-powered applications. By the way, our AI and cloud experts integrated Azure Machine learning tools, such as MLflow, for experiment tracking and Snowflake for dataset handling to help our client estimate future sales. N-iX partnered with a global fashion retail company, which aimed to enhance their ability to accurately predict the sales performance of new products, particularly during the initial weeks after launch. By leveraging the Azure AI toolkit, we improved forecast accuracy by over 50%, reduced product deficits to 5%, and optimized inventory allocation. This led to cost savings, better product management, and more precise predictions aligned with market demands.

Read a full case here: Improving sales forecasting accuracy in retail for effective product allocation

Microsoft Azure has plenty of tools and platforms for developing AI and ML products, including those widely used among developers:

• Azure Machine Learning: A versatile platform offering comprehensive ML tools and MLOps capabilities, enabling efficient model development, deployment, and lifecycle management.
• Azure Cognitive Services: A collection of pre-built APIs for integrating AI-driven speech, vision, language, and decision-making features into applications with ease.
• Azure Databricks: A highly scalable and collaborative platform designed for big data analytics and ML, offering seamless integration with Azure's suite of tools and services.

Choose Azure if you need these provided strengths:

• Seamless Microsoft ecosystem integration
• OpenAI partnership for cutting-edge generative AI
• Strong enterprise focus on hybrid cloud capabilities
• Advanced security and compliance features
• Native Office 365 and GitHub integration
• Enterprise identity management integration

AI workloads on GCP

Google Cloud Platform is renowned for its AI-first approach, leveraging Google's expertise in artificial intelligence and machine learning. Its standout service, Vertex AI, provides a unified platform for managing ML workflows, from data preparation to model deployment.

Our cloud engineers have successfully leveraged Vertex AI's unified platform to develop a custom generative AI solution. Our client, a growing brokerage firm, needed to streamline routine tasks and enhance employee productivity. We developed a GenAI-powered solution that enables the automation of repetitive tasks, such as email drafting and JIRA ticket creation, while providing accurate, real-time access to company policies and information through a chatbot interface. Additionally, MLOps practices and GCP's infrastructure supported lifecycle management and scalability. The result was a highly efficient system that reduced operational bottlenecks and optimized resource utilization.

Read a full case here: Streamlining operations and boosting efficiency in finance with generative AI

GCP also introduced their Model Garden, the library on Vertex AI that offers over 200 foundation models from Google, which you can use out of the box. It enables rapid customization with minimal training data and facilitates instant deployment on enterprise-grade infrastructure. GCP also excels in big data processing, with services like BigQuery ML, enabling businesses to directly integrate machine learning into their data warehouses. Google's strong focus on user-friendly, automated solutions like AutoML allows even non-experts to build effective AI models.

Besides the described solutions, GCP also offers other essential services for handling AI workloads:

• Deep Learning Containers: Pre-configured Docker containers optimized for TensorFlow, PyTorch, and other deep learning frameworks that simplify deployment across GCP services and on-premise environments.
• Data Studio: A visualization platform that integrates seamlessly with BigQuery and other GCP data sources to create dashboards that visualize AI outputs, making insights more accessible to stakeholders.
• Agent Builder: A suite of services from Vertex AI that delivers enterprise-ready conversational AI agents through no-code interfaces, enabling quick development and seamless integration with company data sources.

You can go with GCP to gain the following benefits:

• Custom Tensor Processing Units (TPUs) for ML acceleration
• Strong data analytics capabilities and BigQuery integration
• Research-backed AI services and algorithms
• Native TensorFlow support and optimization
• Per-second billing for cost optimization

Conclusion

Choosing the best cloud for handling AI workloads should be driven by your needs, existing technology ecosystem, cost constraints, and team expertise. While AWS offers the broadest platform, Azure excels in enterprise integration, GCP leads in AI innovation, and each provider continues to evolve their offerings to meet the growing demands of enterprise AI implementation. Our cloud engineers advise conducting proof-of-concept (POC) testing with actual workloads to validate performance and cost assumptions before making long-term commitments. This cloud assessment requires careful consideration and a deep understanding of cloud providers' capabilities. You can partner with experienced consultants to leverage necessary expertise on evaluating, choosing, or migrating to the cloud according to your AI initiatives.

N-iX has partnerships with three leading cloud providers, having the following statuses: AWS Premier Tier Services Partner, Microsoft Solutions Partner, and Google Cloud Platform Partner. With deep knowledge of cloud resources and AI expertise, we help our clients transform AI initiatives from concept to enterprise-scale implementations. Our AI team includes over 200 data experts with experience delivering more than 60 data projects across logistics, e-commerce, and finance industries. Partner with N-iX and let us help you unlock the full potential of the cloud for your business. Let's turn your AI vision into a reality.