With cloud spending forecasted to hit $1 trillion by 2027, EC2 is often the most expensive item on your AWS bill. ECS is the most widely-used EC2 container orchestration service, but it presents some unique challenges for cost-optimization — particularly if you’re using advanced strategies like running on Spot.
This practical guide will focus on the key ECS cost optimization strategies, actionable tips, how-to guides, screenshots, and other critical info you need to start saving on your ECS costs.
Monitoring
The first step in controlling costs is understanding your cloud environment. Let’s discuss the essential tools and strategies for monitoring ECS environments and best practices for managing them efficiently.
The most common ECS monitoring tools include:
AWS CloudWatch: CloudWatch Provides monitoring and management for AWS cloud resources and the applications running on AWS. Includes logs, metrics, alarms, and events. Direct integration with ECS for monitoring CPU and memory utilization, network, and disk metrics.
AWS CloudTrail: AWS CloudTrail records AWS API calls for your account. Provides a history of AWS API calls for your account, including calls which are useful for auditing changes in ECS services and tasks.
Third-Party Monitoring Tools: Tools like Datadog, New Relic, and Prometheus can integrate with ECS through APIs for additional monitoring capabilities.
Monitoring tools shown in the ECS architecture
Aspect
Best Practice
Tools/Strategies Used
Logging and Analysis
Aggregate and analyze container logs
CloudWatch Logs, ELK stack
Alarms and Notifications
Set up alerts for critical metrics to monitor and control AWS costs; receive alerts when spending exceeds predefined thresholds.
CloudWatch Alarms, SNS
Cluster Health
Monitor cluster resources and task health
CloudWatch Metrics
Service and Task Management
Optimize service definitions and tasks, implement version control and change management processes
ECS Console, AWS CLI
Security Management
Regularly audit and enforce AWS IAM policies and compliance
IAM, AWS Config
Resource Optimization
Rightsize resources based on performance data, use cost-saving strategies such as leveraging Spot instances
CloudWatch, ECS Capacity Providers
It’s worth noting that proper tagging of your EC2 resources is a prerequisite for proper monitoring. The benefits of a robust tagging strategy are to:
Improve visibility into your workloads
Attribute shared costs to individual teams, projects, departments, environments, and business units
Programmatically manage infrastructure
Identify underutilized resources
Investigate spikes in usage and trace them back to specific causes
Tip: Use Container-Level Metrics for ECS cost optimization. Implement detailed container-level monitoring to gain insights into specific containers or services driving costs. Third-party tools can help you filter spending by resource type, compute type, tags, and other dimensions to investigate spikes and the biggest drivers of your cloud costs. For more information on allocating your AWS costs, you can download the complete guide.
Right-Sizing ECS Resources
By analyzing historical usage and performance, you can identify and rightsize instances that are not consuming all the resources currently available to them. Keep in mind:
Rightsize regularly. As your dynamic usage changes, regularly assess and adjust the ECS task definitions to ensure that the allocated CPU and memory are in line with the actual usage. Over-provisioning leads to unnecessary costs, whereas under-provisioning can affect performance.
Utilization Metrics: The key to safe rightsizing is data. Use AWS CloudWatch, Datadog, another third-party tool or a custom engineering solution to monitor resource utilization and identify optimization opportunities. AWS’s general rule for EC2 instances is that if your maximum CPU and memory usage is less than 40% over a four-week period, you can safely reduce capacity.
CloudWatch metrics used for rightsizing and ECS Cost Optimization
For extensive and specific information and best practices on how to rightsize with CloudWatch, Datadog or other metrics, check out this free ebook.
Right-Sizing ECS Resources
ECS auto scaling involves automatically adjusting the number of running ECS tasks in response to configured CloudWatch alarms. You can also scale the underlying EC2 instances based on demand, ensuring that there are always enough resources to run tasks without over-provisioning.
AWS ECS supports various scaling strategies to ensure that applications can handle varying loads efficiently: Horizontal, Vertical, Scheduled, Dynamic, and Predictive Scaling. Let’s talk about the differences and how to use them.
General Autoscaling Strategies:
Type
Definition
Best suited for
Best practices
Horizontal Scaling
Increasing or decreasing the number of container instances or tasks to handle the load.
Applications with variable workloads, where the load can increase or decrease unpredictably
Auto-scaling policies based on metrics
Vertical Scaling
Changing the compute (CPU, memory) capacity of existing instances.
Applications with predictable, stable workloads that occasionally require more resources
Resize instances based on steady trends
Scheduled Scaling
Scaling actions are automatically triggered based on a specified schedule.
Workloads that have predictable load changes, like batch processing jobs that run at specific times
Predefine scaling actions for known load patterns
Now let’s talk about some of the best practices for scaling effectively.
Implement ECS Service Auto Scaling
Use ECS service auto-scaling to adjust the number of tasks automatically based on demand. Leverage EC2 to manage the scaling of the underlying instances. Ensure instances have the necessary resources to support the maximum number of tasks you expect to run. Define scaling policies based on CloudWatch metrics such as CPU and memory utilization.
Related Content
ECS Cost Optimization: The Complete Guide
Actionable tips, how-to steps, screenshots, and more for optimizing ECS costs
Dynamic Scaling adjusts the number of active tasks in an ECS service automatically in response to real-time changes in demand.
Here are the basic steps:
Identify the metrics most critical for your application’s performance and stability. Common metrics include CPU utilization, network throughput, memory usage or custom application-specific metrics.
Establish appropriate minimum, maximum, and desired capacity settings for your ASGs. These should align with your anticipated workload demands, ensuring that the ASG can scale efficiently without over-provisioning. For example, scale out (add instances) when CPU utilization exceeds a certain threshold, and scale in (remove instances) when the utilization decreases. One strategy to consider is adjusting settings proactively in advance of predicted changes in demand, optimizing resource utilization and cost. We can also schedule the scaling based on the monitoring metrics where we can observe spikes and create the ASG to scale out during certain events or times of the week.Cost-optimize this workload by scaling in during off-usage.On the other hand, if your application has sporadic or unpredictable loads, consider using a mix of instances, possibly including burstable types (e.g. AWS T-series) that offer baseline performance with the ability to burst.
Create Dynamic Scaling Policies to properly utilize your resources and maintain cost-effective performance as your usage changes. In the AWS Management Console, go to the Auto Scaling service.
Define policies for scaling out and scaling in based on the CloudWatch alarms. Configure your ASG to actively adjust its capacity in anticipation of idle or loading windows, i.e. periods of low and high demand. You’ll need to regularly review historical data in CloudWatch to fine-tune your scaling policies continually.
Configure Scaling Actions: This may include adjusting the desired capacity, minimum and maximum instance counts, and cooldown periods that prevent rapid scaling events in response to fluctuations, ensuring stability.
Steps 3 & 4: Create dynamic scaling policies and configure scaling actions based on CloudWatch metrics
Predictive Scaling
Predictive scaling works by analyzing historical load data (e.g. via Amazon CloudWatch Integration) to detect daily or weekly patterns in traffic flows.
It uses this information to forecast future capacity needs, automatically scheduling scaling actions to proactively increase the capacity of your Auto Scaling group to match the anticipated load ahead of expected demand spikes.
Some best practices include:
Ensure sufficient historical data (typically at least 30 days) is available for accurate predictions.
Combine predictive scaling with dynamic scaling to handle unexpected surges in demand
Regularly review predictive scaling decisions and adjust as necessary based on actual demand patterns
Let’s summarize the differences between the above strategies:
Scaling Type
Trigger
Best For
Considerations
Dynamic
Real-time metrics (e.g., CPU load)
Unpredictable workloads
Requires fine-tuning of metrics and thresholds
Predictive
Historical data analysis
Predictable, significant fluctuations in load
Depends on quality and quantity of historical data
Scheduled
Predefined times
Known events or maintenance windows
Must be manually set and adjusted as patterns change
Additional quick tips for effective scaling
Container packing: Implement task placement strategies that optimize the packing of containers in each EC2 instance to maximize resource utilization. Strategies like ‘binpack’ and ‘spread’ can optimize resource utilization and availability. You can increase container density on each instance as long as it doesn’t compromise performance.
Binpack: Tasks are placed on container instances to leave the least amount of unused CPU or memory. When this strategy is used and a scale-in action is taken, Amazon ECS terminates tasks. It does this based on the amount of resources that are left on the container instance after the task is terminated. The container instance that has the most available resources left after task termination has that task terminated.
Spread: Tasks are placed evenly based on the specified value. Accepted values are instanceId (or host, which has the same effect), or any platform or custom attribute that’s applied to a container instance, such as attribute:ecs.availability-zone. Service tasks are spread based on the tasks from that service.
Spot Instances and Fargate Spot
If you’re looking to save on ECS, Spot is key to reducing cost. Spot Instances allow users to take advantage of unused EC2 capacity at a fraction of the standard On-Demand price.
Instance Type
Spot vs. On-Demand Savings
Expected Savings
General Purpose
Up to 90% cheaper than On-Demand
Very High
Compute Optimized
Up to 80% cheaper than On-Demand
High
Memory Optimized
Up to 75% cheaper than On-Demand
Moderate to High
However, they can be reclaimed by AWS with just a 2-minute warning if there’s higher demand for the capacity. This characteristic introduces a few risks and considerations that need to be managed carefully:
Service Interruption: The most direct risk is the potential for service interruption. If AWS reclaims Spot Instances, any containers running on those instances will be stopped, which disrupts running services or batch jobs if not properly managed.
Task Interruptions and Failures: For ECS tasks that are stateful or require longer processing times, interruptions can lead to incomplete transactions or data corruption if not properly handled.
Increased Management Overhead: To effectively use Spot Instances, you need to implement more sophisticated management strategies such as including handling instance interruptions, increasing the complexity of your infrastructure.
Variable Costs: While Spot Instances can offer significant cost savings, market prices continually fluctuate based on demand. If not monitored and controlled, costs can potentially spike.
Capacity Availability: Spot Instance market availability varies and might not always be able to match the scale or specific instance types/sizes your applications require, leading to potential scaling limitations.
However, with the right techniques, you can successfully utilize AWS Spot Instances in conjunction with ECS to significantly reduce costs while maintaining performance and reliability.
Best Practices for Using Spot Instances for ECS Cost Optimization
Analyze the suitability of your application: Stateless, fault-tolerant, or flexible applications are ideal candidates for Spot. Implement a robust failover strategy using ECS service auto-scaling to maintain application availability and regularly test the resilience of your ECS environment on Spot Instances to ensure that it can handle instance interruptions gracefully.
Use ECS Capacity Providers with Spot Fleet Integration: Capacity providers are used to manage the infrastructure on which ECS tasks are run. They can be set up to use a mix of On-Demand and Spot Instances. You’ll need to use a capacity provider strategy that prioritizes Spot Instances but falls back on On-Demand Instances when needed.
Spot Fleets manage a collection of Spot Instances and optionally On-Demand Instances to meet a certain capacity target. You can combine Spot Fleets with ECS to manage instances and scale capacity efficiently. Spot Fleet has the ability to mix instance types and sizes to optimize cost and maintain availability, and you can configure Spot Fleet to replace terminated instances automatically.
Implement Spot Instance Diversification: Rather than relying on a single instance type, diversify your Spot Instances across various instance types and availability zones. This strategy reduces the impact of sudden market fluctuations on your workload.
When it comes to critical applications, you want to always maintain a core base capacity of On-Demand instances to ensure they remain unaffected by interruptions in the event that no suitable Spot is available.
Implementing Spot Instance Draining: Use ECS’s managed instance draining feature to gracefully handle Spot Instance terminations by stopping tasks on the instance and rescheduling them on other instances. Monitor Spot Instance advisories and trigger the draining process automatically.
Use Spot Placement Scores: To better understand the market conditions for your selected instance types you can use Spot Placement Scores provided by AWS.
However, it’s worth noting understanding and interpreting placement scores accurately demands a deep understanding of how AWS calculates these scores and their implications on Spot instance availability.
Incorporating Spot placement scores into auto-scaling strategies adds complexity, because you need to balance cost, performance, and availability. Changing Spot market pricing and availability means users must continuously monitor and react quickly to score changes to optimize their Spot instance use.
Failing to do so properly can result in a volatile Spot instance environment, leading to application downtime, data loss or corruption, and increased overhead due to operational challenges. And if you don’t pick the right Spot instances, you might actually spend more for your trouble.
If these challenges sound familiar, nOps Compute Copilot can help. Just simply integrate it with EC2, ASG, EKS, Batch or other compute-based workload and let nOps handle the rest.
nOps offers proprietary AI-driven management of instances for the best price in real time. It continually analyzes market pricing and your existing commitments to ensure you are always on the best blend of Spot, Reserved, and On-Demand, gracefully replacing nodes before termination.
Here are the key benefits of delegating the hassle of ECS cost optimization to nOps.
Hands free. Copilot automatically selects the optimal instance types for your EC2 or other workloads, freeing up your time to focus on building and innovating.
Cost savings. Copilot ensures you are always on the most cost-effective and stable Spot options.
Enterprise-grade SLAs for the highest standards of reliability. Run production and mission-critical workloads on Spot with complete confidence.
No vendor-lock in. Just plug in your AWS-native ECS to start saving effortlessly, and change your mind at any time.
No upfront cost. You pay only a percentage of your realized savings, making adoption risk-free.
nOps manages over $1.5 billion in cloud spend and was recently ranked #1 in G2’s cloud cost management category. Join our customers using nOps to slash your cloud costs and leverage automation with complete confidence by booking a demo today.
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