In today’s cloud-native world, cost optimization is a critical factor in managing the long-term sustainability and profitability of a business. This is especially true when dealing with containerized environments (such as Kubernetes) and serverless computing (like AWS Lambda and Azure Functions). These technologies offer incredible scalability and flexibility, but without careful management, they can lead to unexpected costs.

Cost optimization in both these areas ensures that organizations can maintain cost efficiency while still delivering high performance. The goal is to balance resource utilization, manage dynamic scaling, and ensure that cloud resources are not being over-provisioned or under-utilized.

Before going through the some best practices for optimizing costs in Kubernetes and containerized environments will go through the key points which highlight that why cost matter.

Global Trend of Adoption of Kubernetes and Serverless

Kubernetes Adoption: As of 2023-2024, Kubernetes adoption has seen significant growth. According to the Cloud Native Computing Foundation (CNCF) Survey 2023, around 80% of organizations are using Kubernetes in production, up from 70% in 2021. Adoption is especially high in large enterprises (with over 1,000 employees), where over 90% are utilizing Kubernetes in some form. Smaller businesses, however, report lower adoption rates. Many organizations are opting for managed Kubernetes services offered by cloud providers like AWS (EKS), Azure (AKS), and Google Cloud (GKE), which simplify the deployment and scaling of Kubernetes without the need to manage the underlying infrastructure.

Serverless Adoption: Serverless computing, including services like AWS Lambda, Azure Functions, and Google Cloud Functions, is also on the rise. As of 2023, approximately 25%-30% of organizations have adopted serverless infrastructure in their production environments. Serverless is particularly favored for event-driven applications, microservices, and lightweight backend tasks. While it is more commonly adopted by small-to-medium-sized businesses, its use is also growing among larger enterprises. Looking ahead, serverless adoption is expected to reach 45%-50% by 2027 as more organizations leverage it for microservices and event-driven architectures.

Both Kubernetes and serverless are expected to see continued growth, with Kubernetes adoption likely surpassing 90% by 2027, while serverless is projected to grow to 45%-50% in the same timeframe. In the coming years, it’s likely that both technologies will coexist in many IT ecosystems, with Kubernetes managing containerized services and serverless handling event-driven workloads, creating a more hybrid and dynamic infrastructure landscape.

[Note: All the statistics mentioned in this blog are sourced from the EdgeDelta Blog on Kubernetes Adoption Statistics.]

Forecasts indicate that Kubernetes’ valuation will rise significantly, reaching an astounding USD 9.69 billion by 2031. This exponential increase reflects companies’ dependence on Kubernetes to orchestrate, manage, and deploy containerized applications.

In cloud-native environments, where both Kubernetes and serverless are used, the combined total for both could range from 20%-40% of the overall IT budget, depending on workload complexity, cloud provider pricing, and scale.

Why cost matters?

• Budget Overruns and Financial Constraints
  • High costs lead to budget overruns, causing financial instability and forcing compromises, such as cutting features or delaying milestones.
• Delayed Decision-Making
  • Fear of escalating costs leads to hesitation in decision-making, causing delays and a lack of clarity in the project direction.
• Reduced Flexibility and Innovation
  • Financial constraints limit the ability to experiment with new technologies, reducing flexibility and slowing innovation.
• Strained Resources and Overworked Teams
  • Tight budgets result in resource cutbacks, leading to overworked teams, burnout, and reduced productivity.
• Increased Technical Debt
  • Cost-saving decisions may delay necessary updates or fixes, leading to the accumulation of technical debt, which complicates future development.
• Missed Market Opportunities
  • Strict budgets prevent the project from capitalizing on new features, markets, or technologies, risking falling behind competitors.

Lets see how we can optimize the cost by following below best practices

Optimizing Costs in Containers and Kubernetes Clusters

When optimizing costs in Managed K8, the first step is to understand where the costs are being incurred. This involves identifying the various components of the Managed K8 infrastructure and services contributing to the overall expense, and analyzing their usage patterns to uncover opportunities for cost optimization. In Managed K8, the key factors that drive costs include:

1. Compute Resources
2. Data Transfer
3. Cluster Management(Additional Resources)
4. Networking

a. Resource Requests and Limits

• Set appropriate resource requests and limits: In Kubernetes, define CPU and memory requests and limits for containers. Requests ensure that pods are scheduled appropriately, while limits prevent them from consuming excess resources.
• Avoid over-provisioning: Ensure resource requests align with actual usage patterns, as over-provisioning leads to wasted resources.
• Use autoscaling effectively: Enable Horizontal Pod Autoscaling (HPA) to scale the number of replicas based on resource utilization, and use Vertical Pod Autoscaling (VPA) to adjust resource requests and limits based on usage.

b. Container Scheduling Optimization

• Node Affinity and Taints/Tolerations: Use these features to ensure workloads are placed on the most cost-effective nodes, such as spot instances or less-expensive machines.
• Resource overcommitment: Kubernetes allows overcommitting resources (where more containers are scheduled than the available resources), but be cautious with this to avoid performance degradation.

c. Monitoring and Cost Analytics

• Monitor resource consumption: Use tools like Prometheus, Grafana, and cloud-specific monitoring tools (e.g., AWS CloudWatch, Azure Monitor) to track resource consumption and identify inefficiencies.
• Cost monitoring tools: Platforms like Kubecost, CloudHealth, and Kubernetes Cost Allocation can help track and visualize costs related to Kubernetes infrastructure and containers.
• Use cost allocation tags: Tag your resources appropriately to easily track usage and spending across different teams, projects, or environments.

d. Use Managed Kubernetes Services (e.g., EKS, AKS, GKE)

• Managed services like AWS EKS, Azure AKS, and Google GKE can help optimize Kubernetes management, as they typically provide automated scaling, security patches, and other operational benefits that reduce overhead.
• Review pricing models: Managed Kubernetes services have pricing based on the number of nodes and the time spent running clusters. Compare these to self-managed clusters to ensure cost-effectiveness.

e. Node Sizing and Scaling

• Choose the right instance types for your nodes: Select appropriate node types based on workload requirements to avoid over-provisioning.
• Cluster Autoscaler: Use the Cluster Autoscaler to automatically adjust the number of nodes in your Kubernetes cluster based on demand, reducing the cost of idle resources.
• Enable the cluster that scale to zero: we can use it for dev/test clusters, or for those application which runs for certain duration of
• ·Spot Instances: Use spot or preemptible instances for non-critical workloads to take advantage of lower pricing, but consider the risk of interruptions.
• ·Saving plan : Azure savings plan for compute enables organizations to reduce eligible compute usage costs by up to 55% (off list pay-as-you-go rates) by making an hourly spend commitment for 1 or 3 years. 
  • Lower Cost for Committed Usage: By committing to an hourly spend (instead of on-demand pay-as-you-go pricing), you get significant discounts, sometimes up to 65% off, for the full duration of the plan.
  • Resource Flexibility: You can scale your services up or down, change your service types, or move between regions without losing your savings as long as they fall within the eligible services.
  • Predictable Budgeting: The ability to commit to a set usage level for 1 or 3 years helps businesses predict their Azure expenses more accurately and ensures they pay less than they would with pay-as-you-go pricing. Example Scenario: Let’s say your business plans to consistently use 1,000 per hour on Azure resources for the next 1 or 3 years. By purchasing an Azure Savings Plan, you could receive a discounted rate (e.g., a30% discount) off of this 1,000 per hour, depending on your commitment. For instance, if you were committing (around 1,000 per hour), a 30% discount would save you around 300 per hour, resulting in a Rs.216000 monthly Azure cost. Above the 1000rs spending per hour, whatever amount will be , we will be charged as per pay as you go.

• Reserved plan : the Azure Reserved VM Plan offers significant discounts for long-term commitments (up to 72%) and is ideal for businesses with stable and predictable workloads. However, any usage above the committed amount will be billed at pay-as-you-go prices. Its more feasible when workload is stable and predictable, and infra is mature. Compared to saving plan its more rigid :
  • Upfront Cost: Reserved plans usually require full or partial upfront payment for the committed period (1 or 3 years).
  • Scaling Limitations: If your workload grows rapidly or requires a different type of resource (e.g. Changing VM sizes, moving to different services), the Reserved Plan may not offer the flexibility to easily scale or switch services without incurring additional costs.
  • Region-Specific: Reserved plans are often tied to specific regions. If you need to move your workload to a different region, the reserved instance might not be transferable, and you could end up paying for unused capacity.
  • Limited Scope: Reserved instances typically apply to VMs, but not to other resources such as storage, networking, or specialized services like Azure Kubernetes Service (AKS), Azure Functions, etc. For these, you would still need to rely on pay-as-you-go or other discounting methods.
• Karpenter: it is an open-source Kubernetes cluster autoscaler . it helps in automates the provisioning and scaling of compute resources (nodes) in a Kubernetes cluster based on the resource requirements of the workloads.
  • Dynamic Node Provisioning:
    • Karpenter automatically provisions new nodes based on the workload’s resource requests and requirements (CPU, memory, etc.).
    • It helps in scaling up or down by choosing the most cost-efficient instance types from the cloud provider based on availability and pricing. This reduces over-provisioning, ensuring you only pay for what you need.
  • Right-sizing Nodes:
    • Karpenter intelligently selects the most appropriate size of instances based on the actual resource utilization, which prevents wastage of resources (and costs).
    • It can replace under-utilized nodes with smaller, cheaper instances or scale up to larger ones based on workload demands.
  • Automatic Spot Instance Usage:
    • Karpenter can optimize costs by using spot instances (if available), which are typically much cheaper than on-demand instances. It automatically provisions spot instances for non-critical workloads to reduce costs while still maintaining scalability and reliability.
    • When spot instances are interrupted or unavailable, Karpenter can dynamically switch to other types of instances to meet the demand.
  • Node Lifecycle Management:
    • Karpenter automatically terminates unused or underutilized nodes to prevent unnecessary costs, such as idle or low-usage nodes. This ensures resources are not running when they are not needed.
  • Integration with Cluster Autoscaler:
    • Karpenter works alongside the Kubernetes Cluster Autoscaler to provide more advanced and granular scaling capabilities, ensuring that nodes are provisioned efficiently while reducing costs.
  • Flexible Instance Types:
    • Karpenter supports a wide range of instance types, allowing it to pick the best fit for the workload, considering factors like cost, availability, and performance.
  • Cloud Provider Agnostic:
    • Although initially designed for AWS, Karpenter is also designed to be extensible to other cloud providers, enabling multi-cloud cost optimization.

f. Log storage:

Managed K8 generates logs for cluster activities, application events, and metrics, which can accumulate over time. These logs are typically stored in services like Azure Log Analytics, Azure Storage, or Azure Monitor.

• Optimized log query execution: The larger your log data, the slower and more resource-intensive the queries become when you need to search through logs for troubleshooting, audits, or performance monitoring
• Reducing the number of logs being generated or stored helps optimize resource consumption, allowing you to potentially downsize the underlying Storage resources.
• Compliance and Security In some cases, retaining unnecessary logs beyond the required retention period may introduce unnecessary security or compliance risks, particularly in regulated industries.
• Implementing effective log retention policies helps in automatically purging outdated logs after a certain period. This ensures that only relevant logs are retained, further reducing storage needs and associated costs.

g. Optimized Network Solution

• Cross-region traffic incurs higher data transfer costs compared to intra-region communication. Keep the traffic within a single region when possible, especially for inter-service communication.
• Use internal load balancers for internal communication between services. This avoids external network traffic and the associated costs.
• Private Link and VNet Integration: Use Azure Private Link and VNet Integration to keep network traffic between your AKS cluster and other Azure services, such as Azure databases or storage, private and within the same VNet.
• Azure Private Endpoints allow your AKS cluster to connect to Azure services privately, without traversing the public internet. This can significantly reduce egress costs, as data no longer has to flow through public IPs.

Optimizing Costs in Serverless Functions (AWS Lambda, Azure Functions)

When optimizing costs in serverless environments, the first step is to gain a clear understanding of where the costs are being incurred. This involves identifying the various components and services contributing to the overall expense, and then analyzing their usage patterns to uncover opportunities for cost optimization. In serverless infrastructure, the key factors that drive costs include:

1. Compute Time (Execution Time)
2. Memory Allocation
3. Number of Requests
4. Data Transfer Costs
5. Storage and State Management

a. Optimize Function Memory and Execution Time

• Choose appropriate memory allocation: Serverless functions like AWS Lambda and Azure Functions are priced based on the allocated memory and execution time. Ensure that memory is allocated efficiently—too little memory can cause functions to run slower (increasing execution time), and too much leads to unnecessary costs.
• Optimize function execution time: Reduce the execution time by optimizing your code, reducing unnecessary operations, and improving overall performance (e.g., avoiding synchronous calls to external services or optimizing database queries).
• Memory Rightsizing Tools:
  • AWS Lambda Power Tuning: With AWS Lambda Power Tuning, we can experiment with different memory settings to see how they affect both the cost and performance of your functions. The tool runs the function with multiple memory configurations and provides a detailed report on the results, helping us identify to find the sweet spot where we can minimize costs while maintaining acceptable performance.
  • AWS Cost Optimizer: is a set of tools, best practices, and strategies provided by AWS to help users their cloud spending. this is useful when function is running for a period of time.
  • Azure Advisor: It offer personalized recommendations across cost, performance, security, reliability, and operational excellence, it Identifies the underutilized, unused resources and recommend best practices and make informed decisions about managing their Azure environment. its goal is to maximize the efficiency, security, and cost-effectiveness of your resources in Azure.

b. Minimize Cold Starts

• Avoid frequent invocations: Serverless functions incur higher latency during cold starts (the time it takes to initialize a new instance of the function). To reduce this, avoid frequent invocations if possible.
• Warm-up strategies: Implement warm-up strategies by invoking functions periodically or using third-party services like AWS Lambda Warmer to keep instances alive.
• Pre-warmed containers : With container images we can package our functions as a container images. By using provisioned concurrency or frequently pinging , we can keep a certain number of instances pre-warmed.

c. Function Timeout and Concurrency Control

• Set appropriate timeouts: Configure function timeouts to ensure they are terminated after a reasonable period, preventing unnecessary cost due to longer running functions and unhandled exceptions.
• Limit concurrency: Use concurrency controls (like AWS Lambda’s reserved concurrency or Azure Functions’ maximum concurrent executions) to ensure that functions don’t scale beyond what’s necessary, especially when dealing with sudden spikes in traffic.

d. Optimize Dependencies and Package Size

• Minimize dependencies: Reduce the size of the function’s deployment package by removing unnecessary dependencies. This will decrease the initialization time and, in some cases, the memory consumption.
• Use Lambda Layers or Azure Functions extensions: Store reusable components like libraries in Lambda Layers or Azure Functions extensions to optimize the function’s code package and reduce the cold start time.
• AWS X-Ray: It is a service for debugging and analyzing distributed applications by providing end-to-end request tracing. It helps developers identify performance issues, bottlenecks, and errors in their applications. (In azure we can use application insight for end to end tracing)

e. Saving plan:

• Serverless are generally billed based on execution time (measured in gigabyte-seconds) and the number of executions. The Azure and AWS Savings Plan for Compute is available for services like Azure Functions and AWS lambda, By committing to a certain hourly spend on compute services, including Azure Functions AWS lambda, we can get discounts on our function execution costs.

f. Cost Allocation and Monitoring

• Monitor usage and track costs: Use tools like AWS Cost Explorer, Azure Cost Management, and Google Cloud’s Billing Reports to understand usage patterns and pinpoint areas where you can optimize.
• Log and analyze function performance: Use logs from CloudWatch or Application Insights to understand performance issues and optimize function execution.
• Budgeting and Alerts: Set up budgets and alerts to monitor costs and avoid surprises. Cloud providers offer built-in tools to set budgets and thresholds (e.g., AWS Budgets, Azure Budgets).
• Auto-scaling and Resource Scheduling: For both serverless and containerized environments, implement auto-scaling rules and resource scheduling (e.g., scaling down during low-traffic periods) to save costs.
• Use cost-efficient storage and networking: Optimize storage and minimize egress traffic between services to avoid additional costs.

Conclusion

Cost optimization in containerized environments and serverless computing is a dynamic process that requires a balance of performance and resource efficiency. By following the best practices for both Kubernetes and serverless functions, businesses can significantly reduce cloud spending, avoid unnecessary resource allocation, and ensure scalability and performance. Careful monitoring, adjusting resources, and leveraging the right pricing models are key to maintaining cost-effective and high-performing cloud architectures.

Key Takeaway: Cost optimization is a critical factor for the success of any project. By efficiently managing and optimizing costs across various resources, we free up valuable funds that can be reinvested into emerging technologies and managed services. These investments improve both the efficiency and security of the project, driving long-term success.

To highlight where we can save costs, here is a breakdown: