10 Open-Source Tools for Optimizing Cloud Expenses

15 min readJun 11, 2024


In today’s time, many organizations are using cloud technologies. Businesses rely heavily on cloud services for their day-to-day operations. And managing cloud expenses has become more critical than ever. But, with the growing adoption of cloud services, optimizing their costs has become a critical challenge.

According to Gartner’s report of November 2023, global spending on public cloud services is forecast to grow 20.4% to a total of $678.8 billion in 2024, up from $563.6 billion in 2023. As organizations scale up their operations, the cloud costs can go out of control if not managed effectively. Fortunately, many open-source tools are available in today’s time. These tools can help mitigate these costs while ensuring optimal performance and resource use.

Importance of reducing cloud expenses

When organizations adopt cloud computing solutions they often encounter the task of overseeing and managing cloud expenses. Failure to optimize the use of cloud resources can result in overspending which impacts an organization’s performance significantly. The pay-as-you-go approach in cloud services requires monitoring and optimization to ensure proper resource usage and cost control. Neglecting this model could potentially result in setbacks, for the organization. Let’s explore the benefits that businesses can derive from optimizing their cloud spending:

  • Cost Efficiency: By identifying unnecessary spending, organizations can divide their resources more efficiently.
  • Great Budget Management: Predictable cloud expenses can lead to great budget management.
  • Increased Return-on-investment (ROI): Having good control over cloud resources can lead to an increased ROI.

Now that we understand the benefits & importance, let’s see the 10 open-source tools that will help an organization achieve these cloud cost optimizations.

Overview of open-source tools as cost-saving solutions

The open-source community has stepped up to offer a mass of powerful and cost-effective solutions. These tools can reveal the path to optimized cloud expenses, as they provide organizations with the necessary insights, automation, and control to navigate the cloud landscape.

Here are the top open-source cloud cost optimization tools, that will help you achieve your goals.

Tool 1: Kubernetes

Kubernetes also known as K8s is a dominant open-source container orchestration platform. It is renowned for its ability to automate the deployment, scaling, and management of containerized applications. It works by simplifying the containerized workloads based on demand, across a cluster of machines to ensure increased availability, scalability, and efficient resource use. We may also call this a container orchestration powerhouse tool.

Cost-saving features of Kubernetes

There are numerous features of Kubernetes for cost saving in the cloud:

  1. Autoscaling: Kubernetes consists of some open-source tools (like Horizontal Pod Autoscaler and Cluster Autoscale) that allow the users to dynamically adjust the number of containers to use which prevents the companies from overprovisioning, where they pay for resources that are not being used.
  2. Improved developer efficiency: This also improves the developer efficiency by streamlining deployments, rollbacks, and scaling allowing them to focus on building apps rather than managing cloud infrastructure.
  3. Horizontal scaling: It also gives the users leverage the horizontal scaling, allowing them to distribute workloads across multiple nodes, optimizing resource usage and reducing costs.

Real-World examples

Some companies such as Netflix, Spotify, Pearson, etc. use Kubernetes. These companies used Kubernetes efficiently for their containerized microservices, autoscaling, and other services, and also reduced their cloud infrastructure cost by a large percentage.

Kubernetes GitHub

Tool 2: Terraform

Terraform is better called an infrastructure-as-code tool that helps define your infrastructure resources (like servers, storage, and networks) in declarative configuration files, enabling automation, repeatability, and cost optimization. Terraform has multiple use cases including infrastructure-as-code, managing Kubernetes, managing virtual images, managing network infrastructure, etc.

Note: As of November 2023, Terraform is no longer an open-source project due to the acquisition of HashiCorp by IBM for $6.4 billion, as reported by ycombinator news. If you’re seeking a fully open-source option for the cloud cost optimization, OpenTofu is a promising alternative.

Terraform as a cost-effective infrastructure tool

  1. Infrastructure Optimization:
  • Resource Visibility: Terraform provides a clear overview of your entire infrastructure, making it easier to identify and eliminate unused or underutilized resources.
  • Right-Sizing: You can define resource configurations with the exact specifications your applications require, preventing overprovisioning.
  1. Reduced Errors & Faster Configuration: With Terraform, you can automate the whole process, minimizing costly mistakes. Also, the infrastructure changes are applied quickly and consistently through code which saves time and reduces errors.
  2. Cloud Cost Management: This tool integration in your application can also provide the early cost estimation of infrastructure based on your configurations. This allows you to make informed decisions about resource types and sizes before provisioning. Not only this, automated checks can also be applied to set it within the budget limits.

Real-World examples

Some companies such as Deutsche Bank and GitHub use Terraform to simplify infrastructure provisioning across multiple cloud providers, leading to greater efficiency and cost savings.

Terraform GitHub

Tool 3: Grafana

Grafana is an open-source monitoring and observability system that provides powerful visualization capabilities for analyzing and understanding data from various sources, including cloud services

Grafana as Cost Efficiency Tool

Now let’s see, how grafana helps in identifying cost inefficiencies and optimizing resource usage.

  1. Visualize Cloud Costs: Integrate Grafana with cloud providers (AWS, Azure, GCP) to ingest cost data. Create custom dashboards that display cost metrics like hourly, daily, or monthly spending by service, project, or region. Utilize heatmaps and other visualizations to identify cost spikes, trends, and anomalies.
  2. Drill Down for Insights: Grafana allows you to drill down into specific cost components, such as CPU, storage, network usage, or database instances. Correlate cost data with other infrastructure metrics (e.g., CPU utilization) to understand the root cause of cost variations. This helps you identify idle resources or inefficient configurations that are driving up costs.
  3. Set Cost Alerts: Configure alerts that trigger when spending exceeds predefined thresholds or when anomalous cost patterns arise. Early detection of potential overspending allows you to take corrective actions, such as scaling down resources or optimizing configurations.

Real-World examples

The companies that use AWS can actually use the grafana for getting the visualizations by creating their personalized dashboards that will display:

  • Total daily spending on AWS services.
  • Cost breakdown for EC2 instances, S3 storage, and Lambda functions.

While Grafana is a powerful open-source tool, it requires setting up data sources and configuring dashboards to get a detailed cost analysis, effective cost monitoring, and optimization. Here are some of the dashboards available online, to get you started quickly, that specifically focus on cost monitoring:

  1. Amazon Managed Grafana for Cost Anomaly Detection
  2. Kubernetes Dashboard for cost management

Grafana GitHub

Tool 4: Prometheus

Prometheus is another open-source tool that is designed to work as a monitoring and alerting toolkit, especially for cloud-native platforms. It collects and analyzes the metrics from your cloud infrastructure, enabling you to monitor resource utilization and reduce costs.

How Prometheus assists in monitoring cloud resource utilization and identifying cost-saving opportunities

Prometheus is a powerful real-time monitoring tool that enables alerting capabilities making the one of the reasons to effectively utilize it for cost optimization. There are several features that Prometheus provides including:

  1. Resource Consumption Insights: With the help of this tool, the user can understand how efficiently our resources are being utilized by taking into consideration different metrics like CPU, memory, and storage usage for cloud resources.
  2. Identifying Idle Resources: Another feature of using Prometheus is that we can easily identify resources with consistently low utilization rates, indicating potential overprovisioning.
  3. Cost Correlation: Combine Prometheus metrics with cost data from your cloud provider. This correlation can help you understand which resources are driving up costs and how changes in resource usage affect your cloud bill.

Examples of Prometheus in reducing cloud expenses

There are various examples of using Prometheus to monitor the EC2 instances. With this tool, companies discovered that 20% of instances have consistently low CPU utilization. They right-size these instances to a smaller size, saving on compute costs. SoundCloud (the popular audio streaming platform) is one of the good examples that uses Prometheus for monitoring and cost optimization.

Prometheus GitHub

Tool 5: Apache Kafka

Apache Kafka is another open-source tool developed by LinkedIn mainly for distributed data streaming. Apache might not be the most be best fit for cost-saving tools as compared to those discussed above, but it offers several features that can significantly reduce cloud infrastructure expenses specifically when handling large volumes of online data streams. It is being used by more than 80% of all Fortune 100 companies.

Cost-saving features of Kafka, such as efficient message processing and storage

Apache Kafka has some of its top core capabilities like high throughput, scalable, high availability, etc. But Apache Kafka also offers several features that can actually reduce the cloud infrastructure cost, when handling large amounts of data streams.

There are several features that Apache Kafka provides like:

  1. Efficient message processing: Kafka acts as a buffer between data producers and consumers which is known as decoupling which allows producers to send data at their own pace without affecting consumers and vice versa and finally, reduces the resource consumption on both sides, leading to potential cost savings.
  2. Flexible storage options: Log compaction in Kafka reduces storage costs by removing duplicates and retaining only the latest version, optimizing long-term data retention. Additionally, tiered storage options like Confluent Cloud minimize expenses by offloading older data segments to cheaper cloud object storage.
  3. Resource optimization: Apache Kafka allows the dynamic scale of the clusters in the cloud based on your data volume and processing needs allowing you to prevent overprovisioning and reducing the idle resources cost.

Real-World examples

LinkedIn being the innovator of this tool is the best example of using Apache Kafka to optimize its data processing workflows and reduce cloud expenses.

Apache Kafka GitHub

Tool 6: Elasticsearch

Elasticsearch is a distributed search and analytics tool designed to handle large volumes of data efficiently. It is an open-source tool that organizations use to manage and analyze datasets cost-effectively.

How Elasticsearch helps in managing and analyzing large volumes of data cost-effectively

Elastisearch is a free and open-source tool that can help the organization manage and analyze large volumes of data but not all services are free, some need to be paid to use. Reducing the cost with elastisearch can be done by:

  1. Efficient Data Storage: Using the elastisearch’s inverted indexes for optimizing the searches for specific terms reduces the data scanned along with the lower resource consumption. Another tool Elasticsearch ILM (Index Lifecycle Management) automates the process of moving older data to cheaper storage tiers which helps you optimize storage costs for data with varying access needs.
  2. Horizontal Scaling: Elasticsearch clusters can be scaled horizontally by adding more nodes, allowing you to handle growing data volumes without sacrificing performance. This eliminates the need for expensive hardware upgrades.
  3. Search Appliance Alternative: Elasticsearch can replace traditional search appliances, which can be expensive and have limited scalability.
  4. Use data rollups: Summarize data into a single document, then archive or delete the original data.

Examples of Elasticsearch use cases for reducing cloud costs

Elastisearch is a widely popular and adopted open-source tool that helps many organizations leverage its cost-saving features like data storage, horizontal scaling, etc.

For example, a company that uses Elasticsearch to store and analyze website logs, and if they are implementing ILM, they can easily move older logs to cheaper cold storage, significantly reducing storage costs. Similarly, if a company leverages Elasticsearch to power its e-commerce search engine. The efficient storage and indexing capabilities of Elasticsearch, enable them to handle large product catalogs and high search volumes without incurring high infrastructure expenses.

For those on the AWS cloud platform, AWS OpenSearch Service provides a managed offering with similar functionalities and potential cost-saving benefits.

Elastic GitHub

Tool 7: Hadoop

Hadoop is a Java-based distributed processing framework that is used for big data analytics and processing, not only this, but its scalable and fault-tolerant architecture enables the consumer to reduce the cost of resources running in the cloud.

Cost-saving benefits of Hadoop for data processing and storage in the cloud

Organizations can benefit from using Hadoop to optimize data workflows, minimize data movement costs, and efficiently process large datasets. As it is open-source, it’s free but that doesn’t mean, all of the services are free. The cost will depend on how much the software is being used and at which scale.

Reducing the cost with Hadoop can be done like:

  1. Scalability: The clusters in Hadoop are easily scalable up or down both ways and its tools allow you to pay only for the resources you use, especially in cloud environments with on-demand pricing models like YARN (Yet Another Resource Negotiator).
  2. Open-Source: Like others, Hadoop also discards the licensing costs associated with proprietary data processing solutions.
  3. Data Lake Architecture: The data lake architecture in Hadoop has the ability to store large volumes of structured, semi-structured, and unstructured data in a centralized data lake, and this can separate data silos and simplifies data management, potentially reducing storage costs.

Examples of Hadoop implementations for cloud cost reduction

Several companies use Hadoop services like, Adobe, LinkedIn, Facebook, etc. To have a real-life implementation for cloud cost reduction, an organization can use a cloud-based Hadoop cluster to process vast amounts of user activity data and social media sentiment. With this technique, the organization can reduce the on-demand pricing and scale the cluster based on daily/weekly usage patterns, they optimize their cloud spending.

Hadoop GitHub

Tool 8: OpenStack

OpenStack is an open-source cloud computing platform that allows organizations to create and manage their own private clouds. This tool helps organizations optimize cloud expenses through efficient resource utilization, OpenStack becomes a flexible and cost-optimization alternative tool to other paid cloud tools.

How OpenStack enables cost-effective private cloud deployments and management

OpenStack can enable the organization for cost-effective private cloud deployments, the user can optimize some of the below features:

  1. Compute (Nova): OpenStack’s Compute service allows users to create and manage virtual machines, providing an alternative to proprietary compute services like Amazon EC2 or Google Compute Engine. By utilizing Nova, organizations can potentially reduce costs associated with virtual machine provisioning and management.
  2. OpenStack’s Swift: Swift for object storage and Cinder for block storage which offer alternatives to proprietary storage solutions such as Amazon S3 or GCP storage. By using Swift and Cinder, organizations can avoid vendor lock-in and potentially reduce storage costs.
  3. The Distributed Resource Scheduler (DRS): DRS is another feature that distributes workloads across multiple nodes based on resource usage. Balancing workloads optimizes resource usage, reducing costs.
  4. Use Open-Source Tools: OpenStack is an open-source platform. Consequently, you can use open-source tools to monitor and manage your infrastructure. The open-source tools in OpenStack give organizations detailed control over large volumes of resources including computing, storage, etc.

Examples leveraging OpenStack for cloud expense optimization

OpenStack is being used by various big organizations such as IBM, Walmart, VMware, NASA, etc. However, it’s important to acknowledge the trend of organizations adopting a hybrid approach, integrating OpenStack with Kubernetes (k8s). This combination leverages the strengths of both platforms: OpenStack’s infrastructure management capabilities and k8s’ container orchestration expertise. The users can integrate the openstack-integrator to be able to use the OpenStack native features.

Organizations can benefit the cost reduction with this tool with various features like one, by dynamically scaling the OpenStack cluster based on project requirements, optimizing resource utilization, and avoiding paying for idle resources in a public cloud environment. Another best cloud cost optimization can be done by using a distributed resource scheduler (DRS), reserve tools, and more.

OpenStack is very useful for specific use cases where there are predictable workloads, strict security requirements, or fluctuating computational needs, and with this, it provides flexibility, neutrality, and the potential for significant cost savings in the long term for an organization.

OpenStack GitHub

Tool 9: Docker

Docker is an open-source containerization platform consisting of a variety of components that help in container management. Docker can also become a great tool to reduce and optimize cloud expenses, by optimizing resource utilization and streamlining deployments.

But why use Docker to optimize the cloud expenses in today’s time? The answer is simple, docker allows you to package your applications and their dependencies into lightweight, portable containers. These containers share the underlying operating system of the host machine, eliminating the need for full virtual machines (VMs) and thereby monitoring the control of extra pay for services.

How Docker facilitates efficient resource utilization and deployment in the cloud

There are several reasons why docker can facilitate efficient resource utilization and deployment in the cloud and can lead to cost-saving advantages:

  1. Reduced Resource Consumption: Containers require fewer resources compared to VMs. This translates to lower compute, memory, and storage costs in the cloud. By packing more containers onto fewer servers, you can optimize resource utilization and potentially reduce the number of instances needed.
  2. Faster Deployments: Docker containers are self-contained and boot up quickly, accelerating application deployment and scaling processes. This minimizes the time applications spend in a non-productive state, reducing associated cloud costs.
  3. Simplified Management: Docker facilitates consistent deployments across development, testing, and production environments. This reduces the need for separate infrastructure for each stage, potentially saving on cloud resources.
  4. Microservices Architecture: Docker’s containerization model promotes microservices architecture, where applications are broken down into smaller, independent services. This allows for easier scaling and resource allocation based on individual service needs, further optimizing cloud costs.

Examples of Docker for lowering cloud expenses

Docker nowadays has become a very popular tool for containerization for many organizations and for some to lower the cloud cost too. Big organizations like Google, AWS, Thoughtworks, etc. are using Docker in their tech stack.

Organizations can leverage Docker to optimize cloud expenses by reducing the overhead associated with VMs and improving deployment efficiency. For example, a company that uses docker containers for its microservices architecture, by scaling individual services based on traffic patterns, can optimize resource utilization and reduce cloud costs compared to running monolithic applications on VMs.

Another alternate tool that organizations can leverage for cost saving is Docker Swarm which is a native container orchestration platform from Docker. It allows for efficient management of containerized applications at scale, load balancing, etc.

Docker GitHub

Tool 10: Apache Spark

Apache Spark is an open-source distributed data processing multi-engine that can execute data engineering, data science, and machine learning on single-node machines or clusters. Spark is the best tool for handling the large amount of datasets efficiently. Not only this, it can actually reduce the cloud costs for big data analytics and processing.

Cost-saving features of Spark for big data analytics and processing in the cloud

Spark is a powerhouse for distributed data processing where data is processed in parallel across clusters of machines, handling complex data tasks faster as compared to other approaches. But this isn’t its cost-saving feature, it also includes:

  1. Resource optimization: To ensure efficient resource utilization and avoid overprovisioning of compute resources, we can configure resource allocation for different stages of your data processing jobs with the usage of tools like Kubernetes.
  2. Unified Platform: As Spark contains various big data tasks, including batch processing, real-time streaming, and machine learning, we can hide the need for separate tools and associated infrastructure costs.
  3. In-memory processing: Cost can be reduced like storage costs and faster processing times, allowing you to complete tasks quicker and potentially reduce the number of compute instances needed. Spark can leverage in-memory computing for frequently accessed data, significantly reducing disk I/O operations.

Examples of cost reduction using Apache Spark

Apache Spark is the most widely used engine for scalable computing. It is being used by thousands of organizations, including 80% of the Fortune 500 like Databricks, Yahoo, Netflix, and more.

For example, if an organization uses Spark’s in-memory processing for frequently accessed data, it can achieve faster turnaround times and reduce storage costs compared to traditional disk-based analytics to analyze large datasets of customer transactions for a finance organization. Similarly, if an organization can handle complex computations efficiently and faster, it can lead to cost savings.

Apache Spark GitHub


Optimizing cloud expenses is crucial for businesses striving for long-term financial sustainability. However, it’s a multifaceted challenge. In this article, we explored 10 open-source tools — Kubernetes, Terraform, Grafana, Prometheus, Apache Kafka, Elasticsearch, Hadoop, OpenStack, Docker, and Apache Spark that can reduce cloud costs and may be chosen as cost optimization tools. Additionally, tools like Semaphore CI can be leveraged to streamline and automate cloud deployments, further contributing to cost optimization. For a comprehensive CI/CD solution that streamlines deployments, explore Semaphore CI Cloud. These tools smoothen the operations, minimize wastage, and ensure significant savings in cloud deployments.

Originally published at https://semaphoreci.com on June 11, 2024.




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