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Machine Learning & AI

Best AI cloud providers for LLMs, apps, and compute

Read Time 11 mins | Written by: Cole

Best AI cloud providers for LLMs, apps, and compute

[Last updated: Nov. 2024]

The best cloud providers for AI apps and infra are AWS, Azure, and Google Cloud. For working with LLMs, building AI apps, and securing computers, they’re the easiest to integrate into your business. Which to choose depends on your use cases and tech stack.

Besides all the pre-trained models and specialized AI cloud services, the biggest difference between AI cloud providers is which LLMs they make available.

Azure features OpenAI models like o1 and GPT-4o. AWS Bedrock serves up Anthropic’s Claude models (which are slowly taking the lead in enterprise AI). Google Cloud gives you access to advanced Gemini models.All have open-source options like Mixtral and Llama LLMs and ways to integrate external models.

You can use OpenAI LLMs on AWS or Anthropic LLMs on Azure, they just don’t make it easy. Google Cloud doesn't make it easy to deploy OpenAI models. 

Here’s an overview of AI services, pricing, and use cases from the best AI cloud providers.

Amazon Web Services (AWS)

Strengths for AWS AI cloud services– AWS offers a complete ecosystem for AI development – with powerful compute, storage, and networking resources. Both enterprises and startups can scale AI apps and infra fast with AWS. 

Here’s a few of the most critical AWS services for AI, their pricing, and some business use cases. 

Key AWS AI services

  • AWS Bedrock – Fully managed service that offers a choice of high-performing foundation models (FMs) through a single API. Comes with a broad set of capabilities you need to build generative AI applications with security, privacy, and responsible AI.
  • Amazon SageMaker – This fully managed service simplifies the process of building, training, and deploying ML models. It supports a wide range of ML frameworks – including TensorFlow, PyTorch, and Scikit-learn.
  • AWS Deep Learning AMIs – Pre-configured with deep learning frameworks, these virtual machines provide quick access to GPU-powered compute resources.
  • Amazon Elastic Compute Cloud (Amazon EC2) – Here’s where you can secure and scale compute for virtually any AI workload with AWS.
  • AWS S3 – Object storage built to retrieve any amount of data from anywhere. 
  • AI Pre-Trained Services – AWS offers a suite of pre-trained AI models like Rekognition (image analysis), Polly (text-to-speech), and Lex (used for chatbots).

LLMs on AWS

AWS Bedrock is the best cloud platform for Anthropic models and makes it easy to use others. Using OpenAI models on AWS takes a workaround to deploy the API. 

AWS AI pricing

AWS offers a pay-as-you-go pricing model, which can be complex but is highly flexible. AI services like SageMaker have a range of pricing options based on usage.

Some examples of AWS pricing below:

  • Amazon Bedrock pricing charges for model inference and customization. You can choose from On-demand and batch or provisioned throughput.
  • SageMaker pricing includes separate costs for training, inference, and instance types used. Prices start at around $0.10 per hour for basic instances.
  • Rekognition charges per image analyzed or per second of video analyzed, with prices starting from $0.001 per image.
  • Deep Learning AMIs pricing depends on the instance type. GPU instances, like p4d.24xlarge, can cost approximately $32.77 per hour.

AWS Free Tier gives 250 hours of free t2.medium notebook usage (per month) in SageMaker for the first two months, which can help new AI users explore the platform without incurring costs.

AWS AI use cases

  1. Ecommerce personalization – Global e-commerce platforms use AWS to deploy recommendation engines and automate customer service with AI-powered chatbots.
  2. Medical imaging – Healthcare providers use AWS Rekognition to analyze medical images for early detection of diseases like cancer.
  3. Customer support with text-to-speech – AWS Lex and Polly can power voice recognition and conversational AI systems for any customer support org.

Microsoft Azure

Strengths for Azure AI cloud services– Azure offers a comprehensive suite of AI services and tools – it’s ideal for businesses already embedded in the Microsoft ecosystem. Azure’s deep integration with Microsoft products and its robust support for enterprise applications make it a popular choice for AI-driven workloads. Azure is the best cloud platform for using OpenAI LLMs. 

Key Azure AI services

  • Azure AI Studio – Develop and deploy custom AI apps and APIs responsibly with a comprehensive platform.
  • Azure OpenAI service – Use cutting-edge AI models like GPT-4o, DALL·E, and the o1 series to create custom AI-powered experiences.
  • Azure Machine Learning – A robust, end-to-end platform that helps data scientists and developers build, train, and deploy ML models faster.
  • Azure Cognitive Search– Deliver better AI experiences with a knowledge retrieval system built for advanced retrieval augmented generation (RAG) and modern search.
  • Azure Bot Services – A fully managed service to build conversational AI chatbots integrated with Microsoft Teams and other platforms.

LLMs on Azure

Azure is the best cloud platform for deploying OpenAI models directly. Anthropic models are usable with a technical workaround. 

Azure AI pricing

Azure uses a pay-as-you-go model with flexible options based on services and regions.

Some examples of Azure pricing below:

  • Cognitive Services like text analysis and speech-to-text are charged per API call. For example, Text Analytics starts at $1.50 per 1,000 transactions, while Speech Services cost about $1 per hour of speech analyzed.
  • Azure Machine Learning pricing is based on compute instances used, starting at around $0.10 per hour for basic VMs. Additional charges apply for data storage, training, and model deployments.
  • Azure Bot Services offers a free tier, but charges $0.50 per 1,000 messages for paid plans.

Azure Free Tier provides a free account with access to over 25 services, including 12 months of free limited-use, AI tools like Azure Cognitive Services.

Azure AI use cases

  1. Fraud detection – Ecommerce and fintech companies Azure Machine Learning to build fraud detection models – using historical data to predict and prevent fraudulent transactions in real-time.
  2. Healthcare text analytics – Hospital networks use Azure Cognitive Services to improve patient care by using AI-powered text analytics to process patient records and surface critical health insights.
  3. Customer support automation - Telecommunications companies build customer service chatbots with Azure Bot Services (integrated with Microsoft Teams) to handle routine inquiries – reducing support staff workload.

Google Cloud 

Strengths for Google Cloud AI services – Google Cloud has cutting-edge AI and ML tools, with deep integration into open-source AI and ML frameworks, including TensorFlow. Google infrastructure is optimized for data analytics and AI – making it the top choice for deep learning projects.

Key AI services

  • Vertex AI – Google Cloud’s flagship AI platform provides a unified interface for building, deploying, and scaling generative AI.It gives direct access to Gemini and Gemma LLMs.
  • AI Infrastructure – Scalable, high performance, and cost effective infrastructure for every AI workload.
  • Vertex AI Agent Builder –  End-to-end conversational AI, with multimodal and omnichannel functionality to deliver exceptional customer experiences.
  • MLOps tools – For data scientists and ML engineers to automate, standardize, and manage ML projects.
  • Speech-to-text – Convert audio into text transcriptions and integrate speech recognition into applications with easy-to-use APIs.
  • Gemini Code Assist – Increase software development and delivery velocity using generative AI assistance, with enterprise security and privacy protection.

LLMs on Google Cloud

Google Cloud makes it easy to use Google LLMs, a wide variety of open models, and Anthropic models. OpenAI LLMs are not easily deployed.

Google Cloud AI pricing

Google Cloud’s pricing is also pay-as-you-go, with transparent pricing models:

  • Vertex AI pricing offers a wide variety of options, including clear breakdowns of LLM pricing for different models and a custom option. 
  • Vertex AI Agent Builder pricing has different structures for voice and chat. It also comes with $1000 in free trial credits.
  • AutoML model pricing is based on training the model, deploying the model to an endpoint, and using the model to make predictions. 

Google Cloud Free Tier – Google cloud offers $300 in free credits for the first 90 days and provides access to various AI services, including AutoML and Vertex AI – allowing users to experiment with AI models.

Google Cloud AI use cases

  1. Video recommendation engines – Video streaming platforms use GCP’s Vertex AI and TPUs to build real-time content recommendation engines to improve user engagement and retention.
  2. Retail demand forecasting – A retail chain utilizes Google Cloud’s BigQuery ML to analyze historical sales data and predict future demand, optimizing inventory and supply chain operations.
  3. Healthcare image diagnostics – Healthcare providers use Google Cloud for AI-enabled imaging diagnostics to address eye diseases, detect lung cancer, and improve breast cancer identification. 

How to choose the best AI cloud for your business

Instead of wasting time trying to decide on your own, get a technical audit to make the right choice fast. You can request a free technical audit (normally starts at $5000) with our CTO and senior AI engineers.

You'll get:

  • Complete technical audit of architecture, cloud services, backend, frontend, code, security, and QA
  • Extensibility and maintainability review 
  • AI development team and process review 
  • AI technology recommendations based on your roadmap and backlog
  • Actionable next steps with development resource recommendations

Request your technical audit here>

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Cole

Cole is Codingscape's Content Marketing Strategist & Copywriter.