What is infrastructure automation?

Modern IT environments are increasingly complex, spanning multiple data centers, cloud providers and hybrid architectures. Workloads that once ran on a few servers now involve thousands of containers and microservices distributed across regions.

Managing this infrastructure manually—configuring servers individually, tracking dependencies in spreadsheets and deploying updates one system at a time—can result in weeks of work, high error rates and missed deployment windows.

Infrastructure automation streamlines this process by replacing manual configuration with workflows that are code-driven, repeatable and automated.  Rather than waiting weeks for manual deployments, organizations can provision entire environments in minutes. This approach improves reliability, reduces configuration errors and enables on-demand scalability.

For instance, to manually deploy a microservices application across cloud regions for Black Friday, a company would need weeks to provision servers, configure load balancers and establish network connections across each region. With IT infrastructure automation tools, the same deployment can take minutes.

Infrastructure automation also supports DevOps practices, including continuous integration and continuous delivery (CI/CD) pipelines, container orchestration and infrastructure as code (IaC).

Infrastructure automation operates through three core functions: infrastructure provisioning, configuration management and workflow orchestration. 

True automation requires centralized visibility, standardized controls and management guardrails across all of these functions to help ensure consistent, secure operations at scale.

While infrastructure automation tools can perform these functions, no single tool handles all of them. Instead, organizations build end-to-end automation toolchains where different tools handle different stages of the infrastructure lifecycle. 

For example, a typical infrastructure automation pipeline might use:

• Terraform to provision, centralize, standardize and manage infrastructure, such as servers.

• Ansible to configure infrastructure, such as by installing software and setting security policies for servers. 

• Kubernetes to orchestrate containers running on those servers. 

• Jenkins to automate and coordinate the overall workflow.

Provisioning automatically creates and deploys new IT infrastructure components based on predefined templates. When an application needs new servers, automation tools can instantly provision virtual machines, configure networking, set up storage and establish security groups. These tasks can take hours when done manually, and they introduce the risk of human error.

For instance, a software development team can provision an entire test environment—typically 10–20 servers, load balancers, databases, application servers and networking components—with a single command, replicating production configurations exactly.

Automated provisioning processes are often idempotent, meaning they can be safely run multiple times without altering the system beyond the wanted state. 

Common provisioning tools include:

• Terraform, HashiCorp’s infrastructure-as-code platform that works across hybrid and multicloud environments such as Amazon Web Services (AWS), Google Cloud, Microsoft Azure and on-premises environments. 

• AWS CloudFormation for native AWS provisioning. 

• Docker for building container images, which can then be deployed by orchestration tools like Kubernetes. 

• Pulumi, which lets developers define infrastructure in general-purpose programming languages like Python, JavaScript or Go.

Configuration management helps ensure that resources maintain consistent settings throughout their lifecycle.

Infrastructure automation tools can apply updates, patches and configuration changes uniformly across all systems, preventing the configuration drift that can happen when administrators make manual changes to individual servers. These configurations are typically defined in YAML, JSON or other declarative formats.

For instance, when a critical security patch is released, configuration management tools can automatically apply it to thousands of servers within hours, helping to ensure that no system is left vulnerable. This consistency is especially important in regulated industries where configuration drift can cause compliance violations.

Common configuration tools include: 

• Ansible, which uses YAML playbooks and reusable modules for agentless automation. 

• Puppet, an open-source declarative configuration tool. 

• Chef, which uses recipe-based automation.

Orchestration coordinates automated processes at scale, managing dependencies and workflows across complex multicloud environments. When deploying a microservices application, orchestration helps ensure that databases are provisioned before application servers, load balancers are configured after instances are ready and monitoring is established after services are running.

For example, orchestration tools can optimize and manage rolling updates in real time across a Kubernetes cluster. The tools can help to ensure zero downtime by gradually replacing old containers with new versions while maintaining service availability. Downtime minimization is particularly important for organizations running 24 x 7 services where downtime can lead to revenue loss.

Though most container workloads still run on Linux, common orchestration tools include:

• Nomad, a workload orchestrator from HashiCorp that manages containers, binaries and virtual machines across all environments.

• Kubernetes, the leading open-source container orchestration platform that manages containerized applications at scale. 

• Google Kubernetes Engine (GKE), Google Cloud’s managed Kubernetes service. 

• Red Hat Ansible Automation Platform, which extends configuration management into enterprise-wide orchestration. 

• IBM Concert, an AI-powered platform for application orchestration and management.

By combining provisioning, configuration and orchestration tools, organizations can achieve complete application lifecycle automation. Systems are automatically provisioned when needed, maintained in the wanted state and decommissioned based on usage patterns, business requirements or end of need. 

This approach represents a broader model of infrastructure lifecycle management, which extends automation beyond initial deployment to include continuous monitoring, optimization and controlled decommissioning. It helps ensure that infrastructure remains secure, cost-efficient and aligned with organizational policies throughout its entire lifespan.

Effective automation depends on control and visibility. To scale automation safely, organizations implement guardrails that define what resources can be created, how they’re configured and under what conditions they can change. Using policy as code, these rules are encoded directly into automation workflows, allowing compliance and governance checks to run automatically before infrastructure changes are applied.

With these guardrails in place, developer self-service becomes possible. Teams have the autonomy to deploy approved environments and resources on demand without waiting for manual reviews, while still operating within secure and compliant boundaries. This balance of speed and control can help organizations to scale automation while maintaining governance and trust.

Infrastructure automation can help organizations innovate and deliver applications faster, strengthen security and governance and optimize cloud operations. Taken together, these benefits can help ensure businesses realize maximum return on their IT investments.

By replacing manual effort with consistent, code-driven workflows, infrastructure automation helps improve reliability, reduce risk and maintain compliance across increasingly complex hybrid environments.

Manual processes can introduce human error—particularly in IT infrastructure with its many complex and interconnected processes, from server configuration and network setup to load balancer configuration, access control and backup management. According to Gartner, 67% of enterprise networking activities remain manual.¹

Automation can reduce mistakes by eliminating manual typing errors and enforcing standardized consistent configurations through templates.

For instance, a single typo in a manual firewall configuration can expose an entire network to security threats. Automated infrastructure tools apply pretested, version-controlled configurations validated across development and staging environments.

Infrastructure costs represent one of the largest IT budget line items for many organizations. Infrastructure automation helps optimize costs by enabling environments to scale quickly and efficiently as demand fluctuates by using built-in cloud scaling capabilities through automated provisioning workflows.

Rather than simply relying on automatic scaling, effective automation introduces guardrails and visibility—helping prevent teams from creating unnecessary or overly expensive infrastructure. With centralized management and policy enforcement, organizations can identify and remove idle resources, right-size environments and keep spending aligned with actual needs.

For instance, automation tools can monitor cloud server usage and deprovision servers when demand is low, then quickly provision new servers when demand increases, such as during a sale or special promotion.

Organizations now manage distributed ecosystems, spanning on-premises, cloud and hybrid cloud environments.

Infrastructure automation helps unify management across these diverse, often multicloud environments by deploying and configuring resources almost instantaneously. Organizations can maintain flexibility whether they’re using data centers, cloud servers or any combination.

For instance, an organization can deploy the same application stack across AWS, Azure and on-premises servers by using identical configuration files. This capability helps ensure consistency for developers and platform teams while maintaining flexibility to shift workloads based on cost, performance or compliance requirements.

By standardizing automation workflows and creating cross-cloud consistency, organizations can deliver faster without compromising security or control.

Automation also strengthens security posture by enforcing policies and reducing the potential for configuration drift or human error. Security controls, access policies and compliance requirements can be codified into automation workflows, helping ensure they’re applied consistently across all systems. This approach minimizes exposure to threats and helps maintain compliance with internal standards and external regulations.

Effective infrastructure automation supports developer self-service—allowing teams to deploy and manage approved environments without needing deep infrastructure expertise. By establishing preconfigured templates and governed workflows, platform teams act as producers, defining safe patterns that consumers (such as application developers) can use with confidence. This model accelerates delivery while maintaining consistency, compliance and alignment with organizational best practices.

Without automation, infrastructure configurations are often scattered across manual scripts, GitHub documentation and individual server settings, creating maintenance challenges and vulnerabilities.

Infrastructure automation tools create a central repository for all infrastructure configurations, establishing a single source of truth. IT teams can revert after failures and reduce mean time to recover (MTTR).

For example, when a security vulnerability requires patching across hundreds of servers, teams can update one configuration file and automatically propagate the change across all environments, rather than manually updating each server. 

Infrastructure automation is one component of IT automation—a broader discipline that involves automating all technology processes across an organization.

Infrastructure automation focuses specifically on the foundational technology layer. It automatically manages servers, networks, storage and operating systems, handling tasks such as provisioning virtual machines, applying patches, monitoring performance and maintaining consistency. For instance, a script that automatically spins up new servers when demand increases is an example of infrastructure automation.

IT automation encompasses all automated technology processes across the entire organization: infrastructure automation plus business processes, data workflows and application-level tasks. For example, automating employee onboarding workflows, invoice processing or customer data synchronization represents the broader scope of IT automation.

Think of it this way: Infrastructure automation maintains the technology foundation, while IT automation extends automation to every digital process within the organization. Automatically managing servers would be an example of infrastructure automation, while automating HR processes and financial reporting would be examples of IT automation. 

Infrastructure as code (IaC) manages IT infrastructure through code, while infrastructure automation is the broader practice of automating IT infrastructure management through code, scripts or other tools. Infrastructure automation coordinates multiple processes across entire environments, unlike single-task scripts.

IaC uses code to provision IT infrastructure. Code is typically stored in source code repositories such as GitHub, GitLab or Bitbucket. 

IaC works well for specific use cases, such as provisioning servers and managing configurations. More complex workflows often require CI/CD platforms like Jenkins to coordinate infrastructure provisioning with IaC tools like Terraform or Ansible.

Infrastructure automation enables rapid, reliable infrastructure deployments, a key principle of DevOps. 

Automated functions can be tested and moved between environments with minimal risk. Infrastructure automation tools and processes can also make it easier to maintain complete audit trails of changes made across the network, enabling teams to scale, rollback or revert changes as needed. 

Automated performance testing can also help DevOps teams improve code quality and catch issues early. As organizations adopt DevSecOps practices, infrastructure automation can help enforce security policies, manage access controls, apply patches and help ensure code passes security checks within the CI/CD pipeline. 

Artificial intelligence (AI) is transforming infrastructure automation through self-healing systems, intelligent resource optimization and predictive failure prevention. 

Among business leaders surveyed by IDC, 45% plan to increase infrastructure automation efforts because of agentic AI—that is, artificial intelligence systems that can carry out tasks with some autonomy.²

AI systems are beginning to assist with tasks that previously required human judgment, such as optimizing resource allocation during traffic spikes or identifying root causes through log analysis. This assistance is only as good as the data the AI system has to help with decision-making. IaC can help facilitate the creation of a centralized data layer across an organization’s entire hybrid estate to help drive this AI engine.

While human oversight remains essential, AI can enhance infrastructure automation in three key ways:

• Predictive analytics can identify potential failures before they occur by analyzing patterns across metrics, logs and system behavior. 

• Intelligent autoscaling goes beyond threshold-based rules to consider factors like cost optimization, performance requirements and historical usage patterns.  

• Automated root cause analysis can trace issues through complex microservice dependencies in minutes rather than hours.

As AI becomes more integrated into infrastructure operations, it can amplify the value of IaC by making it more adaptive and context-aware. Instead of relying on predefined rules, AI can interpret real-time signals from connected infrastructure models and datasets to guide automation decisions—adjusting scaling thresholds, optimizing placement, remediating issues or enforcing policies dynamically.

The convergence of AI and codified infrastructure lays the foundation for intelligent, autonomous systems that continuously evaluate and improve reliability, efficiency and performance across hybrid environments.