Top 20 AI Cloud Companies to Know in 2024

In the fast-growing tech-driven economy, businesses and organizations constantly consider artificial intelligence (AI) a vital technology. The global cloud AI market size was valued at USD 44.97 billion in 2022 and is estimated to grow at a compound annual growth rate (CAGR) of 39.6% from 2023 to 2030, as predicted by Grandviewresearch. Organizations face difficulty synchronizing multiple technological components to boost the AI lifecycle. It will help simplify and resolve issues with AI. AI Cloud resolved these challenges by streamlining the use of AI for every business unit and developing it in a fabric for day-to-day operations.

Also Read: Top 10 Test Data Management Tools for Clean and Secure Data

According to Statista’s report, cloud-based AI has proven to be a valuable tool for medical imaging analysis, drug discovery, and patient care, thanks to its scalability and computational power, with exponential growth in the market predicted to reach almost 188 billion U.S. dollars by 2030.

What is AI Cloud?

AI Cloud is the integration of cloud computing and artificial intelligence—it brings both technologies together to innovate and change the way operations are carried out. It helps companies easily apply the tools and algorithms of AI in their processes and benefits from machine learning, natural language processing, and computer vision for competitive advantage.

In cloud computing, AI facilitates dynamic resource allocation, automatically adjusting to demand for efficient resource usage. It monitors infrastructure, predicts issues, and enables proactive maintenance to prevent downtime.

What does AI Cloud offer?

AI Clouds encompass technologies that span the entire AI lifecycle, from creating features, models, and applications to operating, monitoring, and sharing them across organizations. These comprehensive platforms, known as AI platforms, thrive in high-scale and elastic environments, earning the moniker “AI Clouds.”

These AI Clouds can be managed by customers or AI vendors. In customer-managed environments, often termed “Hybrid Cloud” setups, customers have control over the AI Cloud, operating it on their preferred infrastructure—on-premises, private cloud, hybrid cloud, or public cloud. Customers manage various aspects, including infrastructure, scalability, elasticity, reliability, and security.

Application of AI in Cloud Computing

AI and cloud computing convergence drive diverse applications across industries. Here are key use cases:

1. Internet of Things (IoT): Cloud services process data from IoT devices, enabling real-time analytics and predictive maintenance.
2. Chatbots: AI-powered chatbots, hosted on cloud platforms, enhance customer service with natural language processing.
3. Business Intelligence (BI): Cloud-based AI gathers market data, running predictive analytics for informed decision-making.
4. AI as a Service (AIaaS): Public cloud providers offer cost-effective AI solutions, enabling experimentation without infrastructure risks.
5. Cognitive Cloud Computing: AI-driven cognitive insights-as-a-service, offered by platforms like IBM and Google, revolutionize industries like finance, retail, and healthcare.

AI Revives Growth in Cloud Computing

• As cloud computing becomes pervasive, cloud market revenues may slow down.
• Increasing AI capabilities in the cloud for the major tech players lead to the revival of growth in cloud computing.
• Amazon is working on Bedrock, a cloud service focusing on generative AI.
• This aspect of cloud computing shows how developers may use AI-generated text efficiently to enhance software.
• A synergy between AI and cloud computing propels businesses into an intelligent, interconnected future.

What are AI Cloud Services?

AI cloud services, known as AI as a Service, offer AI capabilities and resources through cloud-based platforms.

• Cloud providers scale AI resources to meet the demands of the projects and make it easy to manage tasks and growth.
• Access to AI cloud services from anywhere with internet connectivity for remote access and collaboration work.
• Organizations leverage AI without investment in infrastructure and skills on a pay-as-you-go or subscription basis.
• Featuring powerful GPUs and TPUs for the training of deep learning models, specialist hardware is not needed.
• Developers and data scientists use pre-trained AI models, APIs, and tools to easily integrate AI features into applications.
• The cloud system hosts and manages datasets of AI projects.
• AI cloud services are integrated with data analytics, databases, and IoT services, providing seamless end-to-end solutions.

Must-know Twenty AI Cloud Companies 

#1 Altair

Altair, headquartered in Troy, MI, is a globally known computational intelligence expert. The company was founded and run by the visionary James Scapa as its Chairman and CEO. It provides software and cloud solutions. It is built on the mission of empowering its customers to make smarter, sustainable decisions by seamlessly infusing data science and advanced engineering principles through AI-led software and cloud solutions.
The comprehensive product portfolio includes:

• Altair HPC Works: High-Performance Computing
• Altair HyperWorks: Simulation and Optimization
• Altair RapidMiner: Advanced Data Analytics and Machine Learning Platform

Product Groups across various domains at Altair include:

• Data Analytics & AI
• Electromagnetics
• Electronic System Design
• Fluids & Thermal
• HPC & Cloud
• Industrial Design Applications
• Internet of Things
• Manufacturability
• Materials
• Multiphysics
• One Total Digital Twin
• Structural Analysis
• Structural Engineering – AEC
• Systems Modeling

#2 Amazon Web Services

Amazon Web Services is the largest cloud computing firm in the world, led by CEO Adam Selipsky. As a pioneer in the industry, it invested billions of dollars in creating new AI and Generative AI offerings. It made strategic partnerships with one of the top AI players, Anthropic.

AWS has introduced new AI-powered products: AI chatbot assistants, large language models, AI-powered cloud cybersecurity solutions, and processors to make things easier for the training and deployment of artificial intelligence and machine learning use cases.

AWS’ comprehensive AI offerings:

• Extensive AI and ML services: AWS offers a host of prebuilt AI services that integrate seamlessly into existing applications and help cover common use cases, such as recommendation systems, chatbots, and image recognition.
• SageMaker for building, training, and deploying custom ML models.
• AI Workloads Infrastructure: AWS provides high-performance VMs and cloud resources optimized for AI workloads, allowing customers to scale their AI projects up or down flexibly.
• Accessibility to All Levels: Whether customers are seasoned data scientists or just starting with AI, AWS.

Also Read: Top 10 AWS Services Every Cloud Security Professional Needs

#3 Cirrascale Cloud Services

Cirrascale Cloud Services, headquarters in San Diego, California, integrates cloud computing and artificial intelligence to help businesses achieve unprecedented performance in the future. Under the leadership of its visionary CEO, PJ Go, the company leverages the power of AI Cloud. The convergence of cloud and AI technologies is to deliver cutting-edge solutions that will provide a competitive edge.

Cirrascale Cloud Services has dedicated offerings in public and private GPU (Graphic Processing Unit) and IPU (Intelligence Processing Unit) cloud infrastructure designed for large-scale deep learning use cases. Therefore, strategic partnerships with industry leaders such as AMD, Qualcomm, and NVIDIA have shaped the AI offering to meet customers’ unique needs of cloud service offerings in GPU as a Service (GPUaaS) solutions.

The company seamlessly integrates the best AI tools and algorithms with cloud computing, enabling businesses to innovate and transform operations through the latest technologies like machine learning, natural language processing, and computer vision. This makes it possible for companies to optimize procedures, improve decision-making, and ultimately build a lead against market rivals.

#4 Dataminr

Dataminr was founded and led by Ted Bailey, the Founder and CEO. The company puts real-time AI and public data to work for clients. It generates relevant, actionable alerts for global corporations, public sector agencies and NGOs. Dataminr’s AI platform helps detect the early signals of emerging risks from public data sources. It offers real-time alerts that allow public and private sector organizations to prepare and take mitigation actions before the event breaks.

The company’s AI platform offers 150 languages, images, video, sound and machine-generated sensor data. It is powered by over 50 proprietary LLMs and multi-modal foundation models, trained on Dataminr’s 12+ year proprietary event archive.

#5 Dynatrace

Dynatrace combines deep observability, AIOps and application security in an open unified platform. Rick McConnell is the company’s CEO. The company also leverages hypermodel AI to anticipate future behaviors and deliver recommendations-based solutions.

What Dynatrace offers: 

• It focuses on IT operations with AI and has application performance monitoring as its core offering.
• It provides actionable insights and automation capabilities beyond just monitoring.
• Designed to work across different cloud providers, offering flexibility in cloud strategy.

#6 Google Cloud

Google Cloud is a reputed global provider of AI tools and GenAI offerings. It is led by Thomas Kurian, CEO. Its offerings include purpose-made infrastructure, AI accelerants, GPUs, APIs,large language models and chatbot collaborators. Google’s Vertex AI offers machine learning tools to automate and manage AI environments, while Gemini helps generate code, text, images and videos.

Key Features of Google Cloud:

• Scalable Service: it can build models of any data type and size using managed distributed infrastructure.
• Notebook developer experience: it can analyze and create models using the Jupyter Notebook development experience.
• Portable models: Open-source TensorFlow SDK models can be trained locally.
• HyperTune: used to create better-performing models automatically and faster.
• Integrated: it is integrated with cloud data flow for feature processing.
• Managed service: without worrying about the infrastructure, it focuses on model development and prediction.

#7 H2O.ai

H2O.ai is an open-source leader in Generative Artificial Intelligence and machine learning, founded by the visionary Sri Ambati. H2O.ai’s innovative h2oGPTe platform, when combined with their Document AI and autoML Driverless AI solutions, has transformed over 20,000 organizations and more than half of the Fortune 500 companies.

The key benefits for clients of H2O.ai offerings include:

• Cost Control: For GenAI products, H2O.ai offers a highly cost-effective solution that provides unlimited text generation at a flat cost, which is up to 10 times more economical than the pay-by-usage models.
• Custom AI Apps: With the low-code Python-based stack developed by H2O.ai, customers can quickly and easily create custom AI apps. Auto-generated code accelerates development and can be tailored to meet specific needs.
• Predictive meets GenAI: H2O.ai offers a unique blend of GenAI and automatic machine learning solutions, making it easy for customers to seamlessly build and deploy use cases that leverage the best of both capabilities through simple API calls.
• Kaggle Grandmasters: H2O.ai has more than 30 Kaggle Grandmasters, each having worked more than 10 years serving hundreds of Fortune 2000 companies.

#8 HashiCorp

HashiCorp is a company that helps companies automate multi-cloud and hybrid cloud environments. Its infrastructure Lifecycle Management and Security Lifecycle Management make automation possible.

HashiCorps Suite of Products includes:

• Built-on projects with source code freely available at their core.
• Underpin the most important applications for the largest enterprises in the world.

#9 IBM

IBM’s Watsonx.ai, a data platform, is a base for foundation models and machine learning. It also includes a set of AI assistants. With this, its other innovations include Watsonx.data to support data science and lifecycle management and Watsonx.governance for monitoring customers’ AI projects.

Benefits of IBM’s AI Cloud platform:

• Seamless scaling to accommodate the evolving needs of AI projects.
• Easy integration and deployment of AI across business functions.
• Ethical and secure development throughout the process.
• Customers can build and deploy their custom ML models.

Also Read: IBM Expands its Software Portfolio Availability Globally in AWS Marketplace

#10 Lambda Labs

Lamda Labs is a deep learning company that obtained a leading position in the AI Cloud domain. The company plans to build an AI computing platform with a portfolio spanning on-premises GPU hardware to hosted GPUs in the cloud. It offers fast access to the latest GPUs and architectures for fine-tuning, training and inferencing of generative AI and LLMs.

What Lamda Labs offers:

• GPU Cloud for Deep Learning: This cloud service is specifically designed for Deep Learning workloads, which are a demanding subset of AI that require significant processing power provided by Graphics
• Processing Units (GPUs): It offers high-performance options with the latest NVIDIA GPUs like the H100 and A100, allowing users to train complex AI models efficiently.
• Pre-configured Cloud Instances for Deep Learning: Lambda Labs pre-configures their cloud instances with popular Deep Learning frameworks like TensorFlow and PyTorch pre-installed.
• Hardware Options Beyond Cloud: offers a range of Deep Learning hardware options, including workstations, servers, and even laptops.

#11 Microsoft

Microsoft offers a comprehensive suite of cloud computing services under the Azure umbrella. With a strong focus on AI and Machine Learning (ML), the company’s Azure AI Platform provides a robust cloud platform designed for building, deploying, and managing AI solutions.

This platform includes a variety of features, such as:

• Pre-built AI services: Azure offers a wide range of pre-trained AI services that can be easily integrated into existing applications. These services cover common functionalities like object detection, speech recognition, and sentiment analysis.
• Tools for custom development: For those who need to build custom models, Azure provides tools like Azure Machine Learning and Azure Databricks for data management, model training, and deployment.
• Integration with Existing Microsoft Products: A major advantage of Microsoft’s AI Cloud offering is its seamless integration with other popular Microsoft products, such as Office 365 and Power BI. This allows businesses to leverage AI capabilities within their familiar workflows.
• Focus on Responsible AI: Microsoft recognizes the importance of responsible AI development. They offer tools and resources to help users build trustworthy AI solutions that are fair, unbiased, and secure.
• Global Reach and Security: Microsoft Azure is a global cloud platform focusing on security and compliance. This makes it a suitable option for businesses of all sizes and across various industries, including those with strict regulatory requirements.

#12 MongoDB

Led by President and CEO Dev Ittycheria, MongoDB is innovating its AI to stay ahead of the game and is incorporated into the popular database platform. Its mission is empowering developers to quickly and easily build AI applications with a complete data platform built for modern applications.

MongoDB’s AI domain includes:

• Focus on Developer Data Platform: MongoDB Atlas, a modern app developer data platform, includes a cloud-based database service that is flexible in its document model and can handle multiple data types. This is key to working with diverse data used in AI projects.
• AI Lifecycle Support: While it does not directly build or deploy AI models, MongoDB says it’s there for the AI lifecycle. Its Atlas platform integrates a broad base of AI tools and services from cloud providers like AWS and Google Cloud AI to support real-time data streaming and vector search. That enables a developer to leverage the existing AI services while maintaining streamlined data management within MongoDB.
• Enabling Generative AI: MongoDB’s recent AI Applications Program seeks to empower businesses in building applications that utilize the strong branch of AI known for creating totally new content: generative AI. By providing a data infrastructure along with integration capabilities, MongoDB tries to make the development of such next-generation AI applications both easy and fast.

#13 Nerdio

Nerdio is a leading Microsoft cloud management software specialist, led by Co-Founder and CEO Vadim Vladimirskiy, pioneering in changing the management and optimization of the cost of virtual desktops, applications, and Azure infrastructure by empowering its products with AI.

Nerdio approaches AI in the following way:

This is particularly focused on cloud management with AI. Nerdio provides cloud management platforms specifically designed to manage Microsoft’s Azure Virtual Desktop and Windows 365 deployments. These platforms, Nerdio Manager for Enterprise and Nerdio Manager for MSP, include the most innovative capabilities within Generative AI.

The Generative AI at Nerdio will automate tasks by managing virtual desktops and cloud environments. It could automate such things as:

– User provisioning and permission management are automatically done
– Optimization of resources used by virtual desktops based on usage
– Predictive maintenance of potential problems within the cloud environment

#14 Oracle

Oracle, has developed ways of integrating AI functionality across its cloud infrastructure, data platform, and applications to deliver a fully integrated experience. Oracle recently rolled out an OCI Generative AI service, embedding large language models with flexible fine-tuning options. Further, the company has combined large language models and retrieval-augmented generation with enterprise data in its OCI AI Agents.

Oracle’s general AI offers a line of products across infrastructure, language, speech, vision, document understanding, and digital assistants:

•  OCI Generative AI: This service enables the seamless integration of large language models with flexible fine-tuning options
• Generative AI Agents: Combining large language models and retrieval-augmented generation with enterprise data
• OCI Digital Assistant: A leading digital assistant solution
• OCI Language: State-of-the-art language processing
• OCI Speech: The recognition and synthesis of speech
• Vision: Computer vision and the processing of images
• Document Understanding: Intelligent document processing and understanding.

Also Read: CIO Interview with Joey Fitts, Vice President, Analytics Product Strategy at Oracle

#15 PagerDuty

PagerDuty is in the leading position of operational management. The company’s Operations Cloud combines AIOps, automation, incident management, and customer service operations into a scalable platform designed to fuel innovation, reduce costs, and reduce the risk of operational failure.

Key elements of the company’s approach to AI and operations include:

• Core Functionality: Incident Response Management: PagerDuty’s core offering is an incident response management platform that helps organizations streamline identifying, escalating, and resolving critical incidents impacting their digital services.
• Generative AI for Improved Operations: PagerDuty is leveraging generative AI within their PagerDuty Copilot product. This AI assistant aims at the automation and simplification of tasks associated with incident response, potentially by:

#16 Red Hat

Red Hat is one of the leading open-source software companies, allowing customers to leverage its software platforms to build, deploy, and monitor AI models and applications. Red Hat’s OpenShift AI solution allows organizations to build, train, test, and serve models for their AI-enabled applications with transparency and control.

Red Hat’s featured solutions for building and using AI include:

• Red Hat Enterprise Linux AI: A robust platform for AI workloads.
• Red Hat OpenShiftAI: A comprehensive solution for building, training, testing, and serving AI models, built on top of Red Hat’s container orchestration platform, OpenShift.
• Red Hat Lightspeed: An innovative solution for accelerating AI development and deployment.

Key features of Red Hat’s offerings relevant to AI and Machine Learning (ML) include:

• Red Hat OpenShift is a container orchestration platform based on Kubernetes. It provides a foundation for building, deploying, and managing containerized applications, ideal for scalable and portable AI workloads.
• Built on OpenShift, this offering specifically addresses the needs of AI and ML development. It features model training and deployment, hardware acceleration, MLOps practices, and compatibility with a wide range of popular AI frameworks and tools.
• Solutions are designed to be flexible and agnostic to specific cloud providers, allowing businesses to deploy their AI applications on-premises, in a public cloud, or in a hybrid environment based on their needs.
• Focuses on providing a secure platform for developing and deploying AI applications, with features like role-based access control and data encryption.
• A strong proponent of open-source software, leveraging open-source technologies in their AI platform, making it more accessible and customizable for developers.

#17 Salesforce

Salesforce develops cloud-based software designed to help businesses connect to their customers. The software helps Salesforce customers find more prospects, close more deals, and offer customer satisfaction with its services.

Salesforce offers:

• AI built on trust: AI Cloud is built on the Einstein GPT Trust Layer, which safeguards your company’s sensitive customer data.
• Various possibilities: Its AI cloud is built on an Open platform, and its users can use LLMs without any risks or threats.
• Customer 360: Its purpose is to build AI that drives efficiency and business outcomes for the companies.

#18 Snowflake

Snowflake is a data cloud provider where organizations can mobilize data with concurrency, performance and nearly unlimited scale. Sridhar Ramaswamy is the CEO of Snowflake and global AI leader. The company is integrating Generative AI technologies throughout its data platform. This GenAI will help with insights and data-driven decisions. Snowflake Cortex a platform that is used to detect patterns and analyze data uses machine learning to build AI applications.

Snowflake’s Platform with simplified architecture offers:

1. Optimized Storage
2. Cloud Services
3. Elastic Multi-cluster computing and more.

#19 Spectro Cloud

Spectro Cloud is a Kubernetes specialist company led by Tenry Fu, Co-founder and CEO. The company’s latest Palette EdgeAI platform lets customers deploy, build and manage Kubernetes-based AI software stacks.

Palette provides features including:

• Simplified deployment: Streamlined tools for deploying pre-trained AI models or custom containerized AI applications onto edge devices.
• Integration with AI marketplaces: Access to pre-built AI models from platforms like Hugging Face, simplifying the process of finding and deploying relevant AI models for edge use cases.
• Distributed training and inference: This technology enables collaborative training of AI models across multiple edge devices, maximizing resource utilization and improving model performance.

#20 VMware by Broadcom

VMware by Broadcom is a company that offers a wide range of AI offerings. The offerings include AI-optimized infrastructure on VMware and Broadcom’s networking chips that are critical for AI computing. Its latest Private AI drives GenAI initiatives and business gains with the help of AI. In addition, VMware AI Labs creates platforms for AI services that ensure privacy and control.

What does VMware Private AI offer with Generative AI?

• AI foundation with NVIDIA
• AI Partner Ecosystem
• AI with Intel
• AI with IBM

Finally

In conclusion, the benefit of AI Cloud lies in efficiently processing extensive datasets. It is more beneficial in data-centric industries such as e-commerce, banking, and healthcare. AI Cloud enables machine learning algorithms to exhibit patterns and insights within large data repositories. AI Cloud platforms allow AI applications centralized management and control. Choosing the right AI Cloud company depends on AI project’s needs and the company’s infrastructure. In addition, companies must help organizations operate and innovate with AI to solve business problems.

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