Today's cloud administrators are responsible for the complete lifecycle of infrastructure components, including virtual servers, networks, applications, and data management from deployment to decommissioning. Automation can offload many of these tasks from administrators, allowing you to focus on other important aspects of infrastructure management.
Infrastructure management is more complicated in modern cloud environments where resources often require rapid scaling to meet demands based on different variables. Multi-cloud and hybrid cloud environments increase the challenges associated with managing cloud-based infrastructure. The challenges cloud administrators encounter include:
- safety.
- compliance.
- Cost management.
- Performance and optimization.
- automation.
Adding these challenges to the concerns of the cloud skills gap, there are disaster recipes.
Today, AI presents users with convenient resolutions for almost any IT challenge. Cloud infrastructure management is no exception. According to Flexera's 2025 State of the Cloud Report, 79% of organizations are already using or experimenting with AI and machine learning PAAS services.
Find out how cloud administrators can integrate AI into existing workflows to enhance infrastructure management capabilities, particularly with regard to dynamic scaling, AI-generated infrastructure configuration, self-monitoring and self-healing systems.
How AI enables dynamic scaling in cloud infrastructure
AI-based services allow administrators to use data analytics for more responsive and efficient workflows. By providing support for dynamic and automated scaling, AI can deal with traffic spikes and avoid network destruction, or to save costs and power.
Consider the benefits of AI-based dynamic scaling, including:
- Predictive scaling. Historical and real-time data help AI predict changes in network traffic and usage, further optimizing resource scalability.
- Continuous monitoring. Resources become available and enable AI to adjust to fluctuating demand.
- Anomaly detection. This allows AI to predict failures in proactive responses, whether automated or manual.
- Cost management. AI that accesses traffic and uses data can scale up and down to meet demand, allowing you to manage costs without wasting unnecessary resources.
How AI can improve infrastructure configuration
It is common to use AI to generate application-level code using languages such as Python and JavaScript. However, AI can also improve infrastructure as a code (IAC) scenario. Some administrators may use AI to generate IAC resources, while others may rely on AI to validate and analyze files.
Some ways AI can improve IAC management include:
- A natural language that codes generation. Generate code using natural language queries to enable less experienced administrators to manipulate complex configurations.
- IAC optimization. Validate and analyze existing code resources to ensure the best performance.
- Security and compliance. Use AI to scan for misconceptions and verify your configuration according to carefully regulated environments such as finance and healthcare.
- Knowledge transfer and documentation. AI services such as Komment can use natural language to summarize and document complex code repositories.
How AI optimizes self-monitoring and self-healing systems
AI offers more effective self-monitoring and self-healing capabilities than cloud administrators could have predicted in the past. In addition to features like IAC optimization and continuous monitoring, AI can quickly dig into troubleshooting to identify and fix issues.
Some of the benefits of AI self-monitoring and self-healing systems include:
- Route cause analysis. AI can provide and monitor resources baselines and streamline anomaly detection and incident reporting. This prevents infrastructure failures and future downtime.
- Automatic repair. Use AI to automate and speed recovery times. This will improve reliability and help keep obstacles transparent to consumers.
- Predictive maintenance. With the proliferation of IoT devices, AI can use hardware and software data to determine when to perform maintenance or repairs.
This information enhances the knowledge base that AI can draw for optimization, compliance and verification, and perpetuates machine learning (ML) and AI capabilities in the infrastructure lifecycle.
AI Tools for Cloud Infrastructure Management
Managing the operational aspects of cloud infrastructure requires two different but closely related concepts. First IT Operations Cloud Artificial Intelligence (AIOPS) uses operational intelligence to maintain availability and automation. The second Generation AI (Genai) efficiently generates configuration code that supports automated operations.
Let's take a closer look at these two concepts.
- Cloud aiops. Artificial intelligence in IT operations uses ML and available data to optimize cloud infrastructure and surveillance to enhance decision-making. Consider tools like Fabrix or Dynatrace. Some common use cases include capacity planning, cost optimization, and anomaly detection.
- Generated AI. Create code, configuration, documentation and reports for cloud operations that help administrators manage their infrastructure effectively. Tools like Google Cloud Vertex AI, AWS Bedrock, and Openai GPT-4 can help support generative coding initiatives.
Other AI utilities provide supplemental data or capabilities to meet special aspects of infrastructure management. Consider the following:
- Komment. Distill your code and other projects into useful WIKIs to streamline onboarding and knowledge transfer. This tool is particularly useful for managing cloud-based IAC scenarios.
- Github Copilot. Provides coding assistance and explanations to allow developers to focus on problem solving rather than repetitive coding tasks.
Note that the lines between these tools may be slightly blurry. Consider using native tools for your primary cloud infrastructure. AWS, Microsoft Azure and Google Cloud have their own portfolios of AI services. According to Google Cloud's 2025 State of AI Infrastructure report, 48% of organizations acquire and implement Genai solutions directly from cloud providers, 36% use independent software vendors, and 26% develop solutions in-house.
Damon Garn owns Cogspinner's collection and offers freelance IT writing and editing services. He has created several Comptia study guides, including Linux+, Cloud Essentials+, and Server+ guides, and has contributed widely to the new stack and Comptia blog, Informa TechTarget.
