* [Topics](/en/topics "Topics") * [Artificial intelligence](/en/topics/ai "Artificial intelligence") * What is an AI platform? What is an AI platform? ======================= Updated April 14, 2026•*6*-minute read Copy URL Jump to section --------------- OverviewTypes of AI platformsAI platform capabilitiesHow to scale AI platform use casesRed Hat can help Overview -------- An [artificial intelligence](/en/topics/ai) (AI) platform is an integrated collection of technologies to develop, train, and run [machine learning](/en/topics/ai/what-is-machine-learning) models. This typically includes automation capabilities, [machine learning operations (MLOps)](/en/topics/ai/what-is-mlops), predictive data analytics, and more. Think of it like a workbench–it lays out all of the tools you have to work with and provides a stable foundation on which to build and refine. There is a growing number of options when it comes to choosing an AI platform and getting started. Here’s what to look for and the top considerations to keep in mind. [Learn the top 5 ways to successfully implement MLOps](/en/resources/mlsops-top-5-checklist "resource | Top 5 ways to implement MLOps successfully in your organization") Types of AI platforms --------------------- The first AI platform decision facing any organization is whether to buy one that’s pre-configured or build a custom platform in-house. ### Buy an AI platform If you’re interested in rapidly deploying AI applications, models, and algorithms, buying a comprehensive pre-configured AI platform is the best option. These platforms come with tools, language repositories, and APIs that are tested ahead of time for security and performance. Some vendors offer pre-trained foundation and generative AI models. Support and onboarding resources help them fit smoothly into your existing environments and workflows. Popular cloud providers are expanding their portfolios with AI platforms, including Amazon Web Services (AWS) Sagemaker, Google Cloud AI Platform, Microsoft Azure AI Platform, and IBM’s watsonx.ai™ AI studio. In many cases, AI platform providers also offer standalone AI tools that can be partnered and integrated with other AI solutions. ### Build an AI platform To meet specific use cases or advanced privacy needs, some organizations need to fully customize and manage their own AI platform. Uber, for example, developed a custom AI platform that uses technologies like natural language processing (NLP) and computer vision to improve their GPS and crash detection capabilities. Syapse, a data-focused healthcare company, created Syapse Raydar®, an AI-powered data platform that translates oncology data into actionable insights. Building an AI platform offers full control over the environment and allows you to iterate in line with your business’s specific needs. However, this approach requires more upfront work to get a platform up and running. Maintenance, support, and management cannot be outsourced. This video can't play due to privacy settings To change your settings, select the "Cookie Preferences" link in the footer and opt in to "Advertising Cookies or try disabling adblockers." ### Go open source Open source communities are driving advancements in artificial intelligence and machine learning. Choosing an open source software solution as the foundation for your AI initiatives means you can rely on a community of peers and practitioners who are constantly improving the frameworks and tools you use the most. Many organizations start with open source tooling and build out from there. Tensorflow and PyTorch are open source platforms that provide libraries and frameworks for developing AI applications. [What does AI look like at the enterprise?](/en/topics/ai/what-is-enterprise-ai) State of platform engineering in the age of AI ---------------------------------------------- [Get the resource](/en/resources/state-of-platform-engineering-age-of-ai "Get the resource") Capabilities to look for in an AI platform ------------------------------------------ ### MLOps [Machine learning operations (MLOps)](/en/topics/ai/what-is-mlops) is a set of workflow practices aiming to streamline the process of deploying and maintaining ML models. An AI platform should support MLOps phases like model training, serving, and monitoring. [Large language model operations (LLMOps)](/en/topics/ai/llmops) is a subset of MLOps that focuses on the practices, techniques and tools used for the operational management of large language models in production environments. LLMs can perform tasks such as generating text, summarizing content, and categorizing information, but they draw significant computational resources from GPUs, meaning that your AI platform needs to be powerful enough to accommodate and support LLM inputs and outputs. ### Generative AI [Generative AI](/en/topics/ai/what-is-generative-ai) relies on neural networks and [deep learning](/en/topics/ai/what-is-deep-learning) models trained on large data sets to create new content. Given sufficient training, the model is able to apply learning from training and apply it to real-world situations, which is called [AI inference](/en/topics/ai/what-is-ai-inference). Generative AI encompasses many of the functions that end-users associate with artificial intelligence such as text and image generation, data augmentation, conversational AI such as chatbots, and more. It is important that your AI platform supports generative AI capabilities with speed and accuracy. [Compare generative AI vs. predictive AI](/en/topics/ai/predictive-ai-vs-generative-ai) ### Scalability Models can only be successful if they scale. In order to scale, data science teams need a centralized solution from which to build and deploy AI models, experiment and fine tune, and work with other teams. All of this demands huge amounts of data and computing power, and most importantly, a platform that can handle it all. Once your models are successful, you’ll want to reproduce them in different environments–on premise, in public cloud platforms, and at the edge. A scalable solution will be able to support deployment across all of these footprints. [Scaling AI with Red Hat AI](/en/products/ai/scaling-ai) ### Automation As your organization goes from having a handful of models you want to roll into production to a dozen or more, you'll need to look into automation. Automating your data science pipelines allows you to turn your most successful processes into repeatable operations. This not only speeds up your workflows but results in better, more predictable experiences for users and improved scalability. This also eliminates repetitive tasks and frees up time for data scientists and engineers to innovate, iterate, and refine. [What is agentic AI and what does it have to do with automation?](/en/topics/ai/what-is-agentic-ai) ### Tools and integrations Developers and data scientists rely on tools and integrations to build applications and models and deploy them efficiently. Your AI platform needs to support the tools, languages, and repositories your teams already use while integrating with your entire tech stack and partner solutions. ### Security and regulation Mitigate risk and protect your data by establishing strong security practices alongside your AI platform. Throughout the day-to-day operations of training, developing, it’s critical to scan for [common vulnerabilities and exposures](/en/topics/security/what-is-cve) (CVEs) and establish operational protection for applications and data through access management, network segmentation, and encryption. [What does AI security look like?](/en/topics/ai/what-is-ai-security) ### Responsibility and governance Your AI platform must also allow you to use and monitor data in a way that [upholds ethical standards and avoids compliance breaches](/en/blog/explaining-artificial-intelligence-governance-and-why-financial-institutions-need-it). In order to protect both your organization’s data and user data, it’s important to choose a platform that supports visibility, tracking, and risk management strategies throughout the ML lifecycle. The platform must also meet your organization’s existing data compliance and security standards. ### Support One of the most important benefits of a pre-configured, end-to-end AI platform is the support that comes with it. Your models will perform better with the help of continuous bug tracking and remediation that scales across deployments. Some AI platform providers offer onboarding and training resources to help your teams get started quickly. Those opting to build their own platform with open source tooling may want to consider choosing vendors who provide support for machine learning feature sets and infrastructure. [Top considerations for building a production-ready AI/ML environment](/en/resources/building-production-ready-ai-environment-ebook "resource | Top considerations for building a production-ready AI/ML environment") How to scale on an AI platform ------------------------------ There are many different factors that can impact the success of AI at scale. Mainly, it depends on how efficiently and effectively your moving pieces are working together, in order to successfully inference. Specifically, [inference servers that can support larger AI models (like LLMs) and their more complex inference capabilities](/en/products/ai/fast-efficient-inference) are essential to scaling AI workloads for the enterprise. [Why you should care about AI inference](/en/artificial-intelligence/inference) These AI tools help engineers use resources more efficiently to help them inference at scale: * [**llm-d**](/en/topics/ai/what-is-llm-d): LLM prompts can be complex and nonuniform. They typically require extensive computational resources and storage to process large amounts of data. llm-d, an open source AI framework, uses well-lit paths to help developers use techniques like distributed inference to support the increasing demands of sophisticated and larger resoning models like LLMs. * [**Distributed inference**](/en/topics/ai/what-is-distributed-inference): Distributed inference lets AI models process workloads more efficiently by dividing the labor of inference across a group of interconnected devices. Think of it as the software equivalent of the saying, “many hands make light work.” * [**vLLM**](/en/topics/ai/what-is-vllm): vLLM, which stands for virtual large language model, is a library of open source code maintained by the vLLM community. It helps large language models (LLMs) perform calculations more efficiently and at scale. Find out how companies like LinkedIn, Roblox, and Amazon used vLLM to scale. [3 real world use cases](/en/topics/ai/how-vllm-accelerates-ai-inference-3-enterprise-use-cases) AI platform use cases --------------------- ### Telecommunications Comprehensive AI services can streamline different parts of the telecommunications industry, such as network performance optimization and quality enhancement for telecommunications products and services. Applications include improved quality of service, audio/visual enhancements, and churn prevention. ### Healthcare A robust AI platform can usher in transformative benefits in healthcare environments like faster diagnosis, advancements in clinical research, and expanded access to patient services. All of this leads to improved patient outcomes by helping doctors and other medical practitioners deliver more accurate diagnoses and treatment plans. [Read about AI in healthcare](/en/topics/ai/what-is-ai-in-healthcare) ### Manufacturing Intelligent automation powered by machine learning is transforming manufacturing throughout the supply chain. Industrial robotics and predictive analytics are reducing the burden of repetitive tasks and implementing more effective workflows in real-time. [Learn how Guise AI automated quality control at the edge](/en/resources/guise-ai-powered-visual-inspection-edge) How Red Hat can help -------------------- [Red Hat® AI](/en/products/ai) is built for fast, flexible, and efficient inference through its [vLLM-powered](/en/topics/ai/what-is-vllm) server. It reliably connects models to your data to unify the customization and development of specialized agents on a single platform. Built on an open source foundation, our products give you full control of AI workflows from end-to-end at any scale. The Red Hat AI portfolio includes [Red Hat AI Enterprise](/en/products/ai/enterprise), a platform for deploying, managing, and scaling AI inference, agentic AI workflows, and AI-powered applications on any infrastructure. [Explore Red Hat AI](/en/products/ai) The official Red Hat blog ------------------------- Get the latest information about our ecosystem of customers, partners, and communities. [Keep reading](/en/blog "The official Red Hat blog") All Red Hat product trials -------------------------- Our no-cost product trials help you gain hands-on experience, prepare for a certification, or assess if a product is right for your organization. [Keep reading](/en/products/trials "All Red Hat product trials") Keep reading ------------ ### What is agentic AI? Agentic AI is a software system designed to interact with data and tools in a way that requires minimal human intervention. [Read the article](/en/topics/ai/what-is-agentic-ai "article | What is agentic AI?") ### What is generative AI? Generative AI is a kind of artificial intelligence technology that relies on deep learning models trained on large data sets to create new content. [Read the article](/en/topics/ai/what-is-generative-ai "article | what is generative ai?") ### What are large language models? A large language model (LLM) is a type of artificial intelligence that uses machine learning techniques to understand and generate human language. [Read the article](/en/topics/ai/what-are-large-language-models "article | What are large language models?") 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