What Is an Auto Scaling Group?

Understanding Auto Scaling Groups

Modern applications face unpredictable traffic patterns that can shift dramatically within minutes. A sudden surge in users can overwhelm your infrastructure, leading to slow response times, crashes, and ultimately, lost revenue and frustrated customers. On the flip side, maintaining excess capacity during quiet periods drains your budget unnecessarily, paying for resources that sit idle while your finance team questions your cloud spending decisions.

An Auto Scaling Group represents a fundamental cloud infrastructure component that automatically adjusts computing resources based on actual demand. This intelligent system monitors your application's performance and scales server capacity up or down, ensuring optimal performance while controlling costs. Rather than presenting a single perspective, understanding Auto Scaling Groups requires examining technical architecture, business implications, operational considerations, and strategic planning dimensions.

Throughout this exploration, you'll gain comprehensive knowledge about how Auto Scaling Groups function within cloud environments, the specific mechanisms that drive scaling decisions, configuration strategies that align with different workload patterns, and practical implementation approaches that balance performance requirements with cost efficiency. You'll discover real-world scenarios, troubleshooting techniques, and architectural patterns that transform theoretical concepts into actionable infrastructure strategies.

Foundational Concepts and Core Architecture

At its essence, an Auto Scaling Group functions as a dynamic collection of compute instances that expand and contract based on predefined rules and real-time metrics. The architecture operates through a continuous monitoring loop that evaluates application health, resource utilization, and performance indicators against established thresholds. When metrics exceed or fall below these boundaries, the system triggers scaling actions that launch new instances or terminate existing ones.

The underlying mechanism relies on several interconnected components working in concert. A launch template or launch configuration defines the blueprint for new instances, specifying the Amazon Machine Image (AMI), instance type, security groups, storage configurations, and user data scripts. The Auto Scaling Group itself maintains the desired capacity, minimum capacity, and maximum capacity parameters that constrain scaling operations within acceptable boundaries. Health checks continuously verify instance functionality, automatically replacing failed instances to maintain the target capacity.

"The true power of auto scaling isn't just adding more servers when needed—it's the intelligent orchestration of resources that anticipates demand patterns and responds faster than any human operator could manage."

Scaling policies determine when and how the group adjusts capacity. Target tracking policies maintain a specific metric at a designated value, such as keeping average CPU utilization at 50%. Step scaling policies define incremental adjustments based on the magnitude of metric breaches, allowing proportional responses to different severity levels. Scheduled scaling accommodates predictable traffic patterns by adjusting capacity at specific times, perfect for applications with known usage cycles.

The integration with load balancers creates a seamless traffic distribution system. As new instances launch, they automatically register with the associated load balancer, which begins routing requests once health checks pass. When instances terminate, the load balancer gracefully removes them from rotation, ensuring in-flight requests complete before shutdown. This coordination prevents service disruptions during scaling events, maintaining consistent user experiences regardless of backend infrastructure changes.

Instance Lifecycle Management

Understanding the instance lifecycle within an Auto Scaling Group reveals the sophisticated orchestration happening behind the scenes. When a scaling action triggers instance launch, the group creates the instance based on the launch template, applies the specified configuration, and begins executing any user data scripts. The instance enters a "Pending" state while it initializes, during which the Auto Scaling Group doesn't consider it toward the desired capacity count.

Once initialization completes and health checks pass, the instance transitions to "InService" status, actively handling application traffic and counting toward capacity targets. Throughout its operational life, continuous health monitoring evaluates both EC2 status checks and application-level health indicators. If an instance fails health checks repeatedly, the Auto Scaling Group marks it as unhealthy and initiates replacement, launching a new instance before terminating the failed one to maintain capacity.

Termination follows a sophisticated selection process that considers multiple factors. The default termination policy prioritizes instances in availability zones with the most instances, then selects the oldest launch template or configuration, and finally chooses the instance closest to the next billing hour. Custom termination policies can prioritize different criteria, such as protecting instances with specific tags or preferring older instances regardless of availability zone distribution.

Scaling Strategies and Policy Configuration

Effective Auto Scaling Group implementation demands careful consideration of scaling strategies aligned with application characteristics and business requirements. The choice between reactive and proactive scaling approaches fundamentally impacts performance, cost efficiency, and operational complexity. Reactive scaling responds to observed metric changes, while proactive scaling anticipates demand based on patterns and schedules.

Target tracking scaling simplifies configuration by automatically creating and managing CloudWatch alarms that maintain a specified metric value. For CPU utilization targets, the system calculates the aggregate average across all instances and adjusts capacity to maintain the target percentage. The scaling algorithm incorporates cooldown periods to prevent rapid oscillation, allowing recently launched instances time to stabilize before triggering additional scaling actions.

"Proper scaling policy configuration isn't about preventing every performance dip—it's about finding the optimal balance between responsiveness and stability that aligns with your application's tolerance for variability."

Advanced Metric Selection and Custom Metrics

While CPU utilization remains the most common scaling metric, sophisticated implementations leverage application-specific indicators that more accurately reflect user experience and business impact. Memory utilization, request count per target, active connection counts, and application response times often provide superior scaling signals compared to generic infrastructure metrics.

Custom CloudWatch metrics enable scaling based on business-specific indicators such as queue depth, database connection pool utilization, or even revenue-generating transaction rates. Applications publish these metrics to CloudWatch, where scaling policies reference them just like standard metrics. This approach aligns infrastructure scaling directly with business outcomes, ensuring resources expand when they genuinely impact customer experience or revenue generation.

Composite metrics combine multiple indicators into a single scaling signal, creating more nuanced scaling decisions. An e-commerce application might combine CPU utilization, active shopping cart count, and checkout process duration into a weighted metric that triggers scaling when any combination indicates capacity constraints. This multidimensional approach prevents scenarios where a single metric appears acceptable while users experience degraded performance due to bottlenecks in other areas.

High Availability and Multi-Zone Distribution

Auto Scaling Groups inherently support high availability architectures through intelligent distribution across multiple availability zones. By specifying multiple subnets in different zones, the group automatically balances instances to maintain roughly equal distribution, protecting applications from zone-level failures. If an entire availability zone becomes unavailable, the group launches replacement instances in healthy zones, maintaining the desired capacity despite infrastructure failures.

The rebalancing mechanism continuously monitors instance distribution and takes corrective action when imbalances occur. If one availability zone contains significantly more instances than others, the group launches instances in underrepresented zones and terminates instances in overrepresented zones. This process happens gradually to avoid service disruptions, respecting termination policies and cooldown periods throughout the rebalancing operation.

Health check configuration critically impacts availability characteristics. EC2 health checks verify basic instance functionality, confirming the instance is running and responsive at the infrastructure level. ELB health checks perform application-level verification, ensuring the instance not only runs but actually serves traffic correctly. Configuring both check types provides comprehensive health monitoring, though it increases the likelihood of instance replacement when application issues occur.

Capacity Reservation and Warm Pool Strategies

For applications requiring immediate scaling response, warm pools maintain pre-initialized instances in a stopped or hibernated state. When scaling events occur, the Auto Scaling Group draws from the warm pool instead of launching new instances from scratch, dramatically reducing the time to serve traffic. Stopped instances incur only EBS storage costs, while hibernated instances preserve memory state, enabling even faster activation for applications with lengthy initialization processes.

Capacity reservations guarantee instance availability in specific availability zones, ensuring scaling actions succeed even during periods of high AWS demand. On-Demand Capacity Reservations reserve compute capacity without requiring long-term commitments, while Savings Plans and Reserved Instances provide cost benefits for predictable baseline capacity. Combining reserved capacity for minimum instance counts with on-demand scaling for variable demand optimizes both cost and availability.

"The difference between a good auto scaling implementation and a great one often comes down to how well you've architected for the transition periods—those critical moments when instances launch, initialize, and begin serving traffic."

Cost Optimization and Instance Mix Strategies

Auto Scaling Groups support sophisticated cost optimization through mixed instance types and purchasing options. By specifying multiple instance types with similar performance characteristics, the group can leverage spot instances for significant cost savings while maintaining on-demand instances for baseline capacity. This diversification increases the likelihood of spot instance availability while protecting against spot interruptions through automatic replacement with alternative instance types.

The allocation strategy determines how the group distributes instances across types and purchasing options. The "capacity-optimized" strategy prioritizes spot instance pools with the highest available capacity, reducing interruption likelihood. The "lowest-price" strategy minimizes costs by selecting the cheapest available options, accepting higher interruption risk. The "capacity-optimized-prioritized" strategy balances cost and availability by considering both capacity and a prioritized instance type list.

Spot instance integration requires application architecture that gracefully handles interruptions. AWS provides a two-minute warning before terminating spot instances, allowing applications to complete in-flight work, save state, and deregister from load balancers. Auto Scaling Groups automatically launch replacement instances when spot capacity becomes unavailable, maintaining the desired capacity through alternative instance types or on-demand instances.

Right-Sizing and Performance Optimization

Continuous monitoring of instance utilization reveals opportunities for right-sizing that reduce costs without impacting performance. If instances consistently operate at low utilization levels, switching to smaller instance types maintains adequate capacity while reducing hourly costs. Conversely, instances frequently hitting resource limits benefit from larger types that improve performance and potentially reduce the total instance count needed.

Graviton-based instances offer compelling price-performance advantages for many workloads. These ARM-based processors provide up to 40% better price-performance compared to x86 alternatives, though they require application compatibility verification. Auto Scaling Groups can include both x86 and Graviton instance types, allowing gradual migration and testing while maintaining operational continuity.

"Cost optimization in auto scaling isn't about choosing the cheapest instances—it's about understanding your application's true requirements and matching them with the most economical resources that meet those needs."

Integration Patterns and Architectural Considerations

Auto Scaling Groups integrate seamlessly with Application Load Balancers (ALB) and Network Load Balancers (NLB), creating resilient traffic distribution architectures. The load balancer performs health checks and routes traffic only to healthy instances, while the Auto Scaling Group maintains the target instance count. This separation of concerns allows independent scaling decisions based on application performance while the load balancer handles traffic distribution logic.

Target groups enable sophisticated routing patterns where a single Auto Scaling Group serves multiple load balancers or a single load balancer distributes traffic across multiple Auto Scaling Groups. This flexibility supports blue-green deployments, canary releases, and A/B testing scenarios. During deployments, a new Auto Scaling Group launches with updated application versions while the existing group continues serving traffic, allowing validation before shifting production load.

Container Orchestration and Microservices

While Kubernetes and ECS provide their own scaling mechanisms, Auto Scaling Groups often form the underlying compute layer for container orchestration platforms. ECS cluster auto scaling adjusts the EC2 capacity based on container resource requirements, launching additional instances when pending tasks exceed available capacity. This two-tier scaling approach separates container-level scaling decisions from infrastructure-level capacity management.

For microservices architectures, separate Auto Scaling Groups for each service enable independent scaling based on service-specific demand patterns. A user authentication service might scale based on login request rates, while a media processing service scales based on queue depth. This granular approach optimizes resource allocation, ensuring each service receives appropriate capacity without overprovisioning the entire application stack.

Service mesh integration adds another dimension to auto scaling architectures. As instances launch and terminate, the service mesh control plane automatically updates service discovery information, routing tables, and security policies. This dynamic configuration ensures new instances immediately participate in the mesh without manual intervention, maintaining consistent security and observability regardless of scaling events.

Monitoring, Observability, and Troubleshooting

Comprehensive monitoring extends beyond basic CloudWatch metrics to include scaling activity history, instance lifecycle events, and scaling policy effectiveness. CloudWatch Logs capture detailed scaling decisions, including which policy triggered the action, the metric values that exceeded thresholds, and the resulting capacity changes. This audit trail proves invaluable when troubleshooting unexpected scaling behavior or optimizing policy configurations.

Custom dashboards consolidate key metrics into unified views that reveal scaling patterns and anomalies. Effective dashboards display current capacity against desired and maximum capacity, recent scaling activities, metric trends that drive scaling decisions, and instance health distributions across availability zones. These visualizations help operations teams quickly assess Auto Scaling Group status and identify potential issues before they impact users.

Common Issues and Resolution Strategies

🔧 Insufficient Capacity Errors: When AWS cannot fulfill instance launch requests due to capacity constraints in specific availability zones, Auto Scaling Groups may fail to reach desired capacity. Resolution involves expanding the availability zone configuration, diversifying instance types, or implementing capacity reservations for critical applications.

⚡ Scaling Oscillation: Rapid scaling up and down indicates improperly configured cooldown periods or threshold values. The system launches instances in response to high utilization, but those instances immediately reduce average utilization below the scale-down threshold, triggering termination. Adjusting cooldown periods, widening threshold ranges, or implementing more sophisticated target tracking policies resolves this instability.

🎯 Launch Configuration Errors: Instances that launch but immediately fail health checks often indicate problems in the AMI, user data scripts, or security group configurations. Launching instances manually with the same configuration helps identify the root cause, whether it's missing dependencies, incorrect application paths, or network connectivity issues.

💾 State Management Challenges: Applications that maintain local state struggle with auto scaling because instance termination loses that state. Solutions include externalizing state to databases or caching layers, implementing graceful shutdown handlers that persist state before termination, or using lifecycle hooks to coordinate state transfer during scaling events.

🔐 IAM Permission Issues: Auto Scaling Groups require specific IAM permissions to launch instances, attach volumes, register with load balancers, and publish metrics. Missing permissions cause silent failures where scaling actions appear to trigger but instances never launch. CloudTrail logs reveal permission denial events that indicate which policies need adjustment.

"The most difficult auto scaling problems aren't technical—they're about understanding the subtle interactions between application behavior, infrastructure constraints, and scaling policies that create unexpected emergent behaviors."

Advanced Features and Emerging Patterns

Lifecycle hooks enable custom actions during instance launch or termination, pausing the process while external systems perform configuration, registration, or cleanup tasks. A launch hook might wait for a configuration management system to apply application-specific settings before allowing the instance to enter service. A termination hook could trigger log aggregation, state backup, or graceful connection draining before the instance shuts down.

The integration with AWS Systems Manager enables automated patching, configuration management, and operational tasks across Auto Scaling Group instances. Parameter Store provides centralized configuration that instances retrieve during launch, eliminating hardcoded values in AMIs. Session Manager offers secure shell access without requiring SSH key management or bastion hosts, simplifying operational access to instances within Auto Scaling Groups.

Predictive Scaling and Machine Learning Integration

Predictive scaling analyzes historical load patterns and forecasts future capacity requirements, launching instances proactively before demand increases. The machine learning model considers daily and weekly patterns, identifying recurring traffic cycles that scheduled scaling might miss. This approach eliminates the reactive lag inherent in metric-based scaling, ensuring capacity exists before users experience performance degradation.

The forecasting model requires at least two weeks of historical data to identify patterns, though longer histories improve accuracy. The system generates forecasts daily, scheduling scaling actions to match predicted load. Manual review and adjustment capabilities allow operations teams to override predictions for known events like product launches or marketing campaigns that don't follow historical patterns.

Security Considerations and Compliance

Auto Scaling Groups introduce unique security considerations because instances launch and terminate dynamically, requiring automated security controls that don't depend on manual configuration. Security groups, network ACLs, and IAM roles embedded in launch templates ensure every instance receives consistent security policies regardless of when or why it launched.

Instance metadata service version 2 (IMDSv2) should be required in launch templates to prevent SSRF attacks that exploit the metadata endpoint. This configuration forces applications to use session-oriented requests that include authentication tokens, preventing unauthorized metadata access. Combined with IAM roles that grant only necessary permissions, this approach implements defense-in-depth security for Auto Scaling Group instances.

Compliance and Audit Requirements

Organizations subject to compliance requirements must ensure Auto Scaling Groups maintain necessary controls throughout the instance lifecycle. Automated tagging applies compliance-related metadata to every instance, enabling audit trails that demonstrate control effectiveness. CloudTrail logs capture all API calls related to Auto Scaling Groups, providing evidence of who made changes, when they occurred, and what configurations were modified.

Encryption requirements extend to Auto Scaling Group configurations. Launch templates should specify encrypted EBS volumes using customer-managed KMS keys, ensuring data at rest protection for all instances. For applications handling sensitive data, instance types with encryption of data in transit between instances and EBS volumes provide additional protection layers.

"Security in auto scaled environments isn't about protecting individual instances—it's about building security into the templates, policies, and automation that create and manage those instances throughout their lifecycle."

Implementation Best Practices and Operational Excellence

Successful Auto Scaling Group implementations begin with thorough application profiling that identifies resource consumption patterns, initialization times, and scaling triggers. Load testing under various scenarios reveals how the application behaves under stress, how quickly it can scale, and whether scaling policies trigger at appropriate thresholds. This empirical data informs configuration decisions far more effectively than theoretical estimates.

Infrastructure as Code (IaC) practices ensure Auto Scaling Group configurations remain consistent, version-controlled, and auditable. Terraform, CloudFormation, or CDK definitions capture the complete configuration, including launch templates, scaling policies, load balancer attachments, and monitoring alarms. This approach enables reliable replication across environments, simplifies disaster recovery, and provides clear documentation of infrastructure decisions.

Deployment Strategies and Change Management

🚀 Rolling Updates: Gradually replace instances with new configurations by adjusting the desired capacity upward, launching new instances with updated launch templates, then terminating old instances. This approach maintains service availability throughout the deployment while allowing validation at each step.

🔄 Blue-Green Deployments: Create a new Auto Scaling Group with the updated configuration, validate functionality, then switch load balancer target groups to route traffic to the new group. The old group remains available for immediate rollback if issues arise, providing a safety net during risky deployments.

🎯 Canary Releases: Route a small percentage of traffic to instances running new code while the majority continues using the stable version. Gradually increase the percentage while monitoring error rates, performance metrics, and business indicators. This incremental approach limits blast radius if the new version contains defects.

📊 Instance Refresh: The native Auto Scaling Group instance refresh feature automates rolling replacements, gradually terminating old instances while launching new ones based on updated launch templates. Configurable parameters control the replacement rate, minimum healthy percentage, and warmup time, balancing deployment speed against availability risk.

Performance Tuning and Optimization Techniques

Launch time optimization dramatically improves scaling responsiveness, reducing the delay between detecting demand and serving traffic with new instances. Custom AMIs with pre-installed dependencies, configured applications, and warmed caches launch faster than generic base images that install software during user data execution. Regular AMI updates ensure instances launch with current patches and configurations without sacrificing speed.

Connection draining and deregistration delay configurations allow graceful instance termination that completes in-flight requests before shutdown. The load balancer stops sending new requests to deregistering instances while allowing existing connections to complete within the configured timeout. This prevents abrupt connection termination that creates error responses and poor user experiences during scale-down events.

Metric Aggregation and Scaling Precision

The aggregation method for scaling metrics significantly impacts scaling behavior. Average aggregation smooths out temporary spikes, preventing overreaction to brief load increases. Maximum aggregation responds to peak values, ensuring capacity exists for worst-case scenarios. Sum aggregation works well for queue-based metrics where total queue depth across all instances determines scaling needs.

Evaluation periods and datapoints to alarm configurations control scaling sensitivity. Requiring multiple consecutive breaches before triggering scaling actions prevents false alarms from temporary anomalies. However, this added stability introduces delay in responding to genuine demand changes. The optimal balance depends on application tolerance for brief performance degradation versus preference for stable capacity.

Cost Analysis and Financial Optimization

Detailed cost analysis reveals the financial impact of Auto Scaling Group configurations, identifying optimization opportunities that reduce spending without compromising performance. CloudWatch metrics combined with Cost Explorer data show correlations between scaling activities and costs, highlighting periods of overprovisioning or underutilization that warrant policy adjustments.

Rightsizing recommendations based on actual utilization patterns suggest instance type changes that better match workload characteristics. An application consistently using 30% CPU on large instances might operate effectively on medium instances, reducing costs by 50% while maintaining adequate performance headroom. Conversely, instances frequently hitting resource limits benefit from larger types that improve user experience and might reduce total instance count.

Reserved Capacity and Savings Plans

For Auto Scaling Groups with predictable baseline capacity, Reserved Instances or Savings Plans provide substantial discounts compared to on-demand pricing. Analyzing historical minimum capacity over rolling 30-day periods identifies the stable baseline suitable for commitment-based pricing. The variable capacity above this baseline continues using on-demand or spot instances, optimizing costs while maintaining scaling flexibility.

Convertible Reserved Instances offer flexibility to change instance types, operating systems, or tenancy during the reservation term, accommodating evolving application requirements without sacrificing savings. This flexibility proves valuable for Auto Scaling Groups that might transition between instance families as application characteristics change or new instance types become available.

Disaster Recovery and Business Continuity

Auto Scaling Groups inherently support disaster recovery scenarios through their ability to launch instances across multiple availability zones and respond to failures automatically. However, comprehensive disaster recovery requires additional planning around data persistence, configuration backup, and cross-region failover capabilities.

Regular backup of launch templates, scaling policies, and Auto Scaling Group configurations enables rapid recovery if infrastructure components are accidentally deleted or become corrupted. Storing these configurations in version control systems provides both backup and change history, allowing rollback to known-good configurations when issues arise.

Cross-Region Replication and Failover

For applications requiring geographic redundancy, parallel Auto Scaling Groups in multiple regions provide protection against region-level failures. Route 53 health checks monitor application availability in each region, automatically routing traffic away from unhealthy regions. This active-active or active-passive architecture ensures business continuity even during catastrophic infrastructure failures.

AMI replication across regions enables consistent instance configuration regardless of which region serves traffic. Automated pipelines copy newly created AMIs to disaster recovery regions, ensuring the latest application versions and configurations are available for failover scenarios. Combined with replicated data stores and configuration management, this approach creates truly resilient multi-region architectures.

Future Trends and Evolving Capabilities

The evolution of Auto Scaling Groups continues toward greater intelligence, tighter integration with application architectures, and more sophisticated optimization algorithms. Machine learning models increasingly influence scaling decisions, moving beyond simple threshold-based rules toward predictive systems that anticipate demand based on complex pattern recognition.

Serverless computing patterns influence Auto Scaling Group design, with organizations seeking similar rapid scaling and pay-per-use economics for containerized applications. Technologies like AWS Fargate demonstrate this convergence, eliminating instance management while providing auto scaling capabilities. However, traditional Auto Scaling Groups remain relevant for applications requiring specific instance types, custom networking, or fine-grained control over the compute environment.

Sustainability considerations are becoming increasingly important in scaling decisions. Carbon-aware scaling policies that consider the carbon intensity of different regions or availability zones enable environmentally conscious infrastructure management. These policies might prefer regions with higher renewable energy percentages or schedule batch workloads during periods of lower carbon intensity.

How quickly can an Auto Scaling Group respond to traffic spikes?

Response time depends on several factors including the scaling policy type, metric evaluation periods, and instance launch time. Step scaling policies can trigger within 1-3 minutes of a metric breach, but instances typically require 2-5 minutes to launch, initialize, pass health checks, and begin serving traffic. Total time from spike detection to new capacity serving traffic usually ranges from 3-8 minutes. Warm pools dramatically reduce this time by maintaining pre-initialized instances in stopped or hibernated states that can activate in under a minute.

What happens to user sessions when instances terminate during scale-down?

When properly configured with connection draining, load balancers stop sending new requests to terminating instances while allowing existing connections to complete within the configured timeout period (typically 30-300 seconds). Applications should implement stateless architectures or externalize session state to databases or caching layers like ElastiCache or DynamoDB. For applications that must maintain local state, lifecycle hooks can pause termination while the application saves state or transfers sessions to remaining instances.

Can Auto Scaling Groups work with spot instances reliably?

Yes, when architected appropriately. Auto Scaling Groups support mixed instance policies that combine on-demand instances for baseline capacity with spot instances for variable demand. The capacity-optimized allocation strategy selects spot pools with the highest available capacity, reducing interruption likelihood. When spot instances are interrupted, the Auto Scaling Group automatically launches replacements using alternative instance types or on-demand instances to maintain desired capacity. Applications should be designed to handle interruptions gracefully, using the two-minute warning to complete work and deregister from load balancers.

How do I prevent Auto Scaling Groups from scaling too aggressively and increasing costs?

Several configuration options control scaling aggressiveness. Set appropriate maximum capacity limits to cap potential costs. Configure longer cooldown periods to prevent rapid successive scaling actions. Use target tracking policies with moderate target values rather than aggressive thresholds. Implement scale-in protection for instances that should remain running. Enable detailed monitoring to review scaling activities and adjust policies based on actual patterns. Consider scheduled scaling to proactively adjust capacity for known traffic patterns rather than reacting to metrics, which often overshoots optimal capacity.

What's the difference between launch templates and launch configurations?

Launch templates are the newer, more flexible option that supports versioning, allowing multiple template versions with the ability to specify which version the Auto Scaling Group uses. Templates support newer features like T2/T3 unlimited mode, dedicated hosts, and multiple instance types in a single Auto Scaling Group. Launch configurations are the legacy option that cannot be modified after creation, requiring a new configuration for any changes. AWS recommends using launch templates for all new implementations, as they provide more capabilities and will receive future feature updates that launch configurations won't.

How do Auto Scaling Groups handle updates to the application or operating system?

Updates typically involve creating a new launch template version with an updated AMI containing the new application or OS version. The instance refresh feature automates the replacement process, gradually terminating old instances while launching new ones with the updated configuration. You control the replacement rate and minimum healthy percentage to balance deployment speed against availability. Alternatively, blue-green deployments create a completely new Auto Scaling Group with the updated configuration, allowing validation before switching traffic and providing an easy rollback path if issues arise.