Gartner defines AI application development platforms as those that offer the required technology and workflows to design, build, test, and deploy AI applications. These platforms provide access to foundation models and the capability to ground and place guardrails around them. Software engineering teams use these platforms to build AI applications, such as assistants, agents, and multimodal applications. Software engineering leaders face increasing pressure to incorporate AI into their products. AI application development platforms host the necessary tooling for enterprise developers to build AI assistants, agents, and multimodal apps without extensive knowledge of machine learning. AI application development platforms focus on providing the features developers need to ground models with organizational knowledge. They also reduce risk by implementing responsible AI processes and guardrails within their AI applications. These platforms help scale the development of AI-embedded applications by offering governance, evaluation metrics, and support throughout the application life cycle. Not every platform will offer access to first-party models or application-testing capabilities.
Gartner defines AI governance platforms as tools designed to ensure organizations adhere to organization policy, regulations and industry standards across common responsible AI principles. These platforms allow leaders responsible for AI and other technical or business leaders to streamline governance processes organization wide and serve as a central repository for trust, risk and security controls. They also automate workflow approvals for new AI use cases, applications and to streamline governance processes organization wide. AI governance platforms support a wide range of AI techniques across built, blended, embedded and bring-your-own-AI applications.
AI Security and Anomaly Detection is a market focused on providing runtime protection and monitoring for AI applications, particularly those using generative models like large language models (LLMs). These solutions detect and mitigate risks such as prompt injection, hallucinations, toxicity, biased outputs, data leakage, and performance drift. Delivered as cloud-native modules via APIs or embedded within applications, they offer real-time visibility into content and security anomalies. The market supports compliance with emerging regulations, enables centralized oversight across multiple AI deployments, and helps organizations safeguard their brand and decision-making processes from faulty or malicious AI behavior.