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The allure of cloud computing often promises scalability, flexibility, and enhanced performance. But for many companies, the transition to the cloud comes with an unwelcome surprise: escalating costs.

This usually happens due to misconfigured workloads, oversight of optimum pricing models, or simply a lack of understanding about the available discounts. These missteps can lead to an increase in operational expenditures, creating financial and logistical challenges.

Recognizing the importance of this issue, we’ve done the research for you. Our team has curated six insightful case studies that highlight companies making the most out of their AWS and GCP investments. These organizations have not only managed to cut down costs but also have successfully improved performance, turning the cloud from a cost burden to a strategic asset.

How to Save Cloud Costs and Increase Performance

Before diving into the specific companies, let’s shed light on the general strategies that lead to successful cloud cost management:

  1. Right-Sizing Instances: Don’t over-provision resources; it’s a cost sink and inefficient. Use tools like AWS Cost Explorer or GCP’s Cost Management to pinpoint underused resources.
  2. Spot and Preemptible Instances: Utilize AWS’s Spot Instances or GCP’s Preemptible VMs for non-critical, interruption-tolerant workloads. These can cut costs by up to 90%.
  3. Auto-Scaling: Use AWS Auto Scaling Groups or GCP’s Managed Instance Groups to automatically adjust resources based on demand, saving costs and improving application performance.
  4. Data Transfer Optimization: Large data transfers can be costly. Consider AWS Direct Connect or Google Cloud Interconnect to reduce data transfer expenses.
  5. Reserved and Committed Use Discounts: For predictable workloads, look into AWS Reserved Instances or GCP’s Committed Use Discounts, which offer long-term contract savings.
  6. Budget and Alert Tools: Utilize AWS Budgets or GCP Budgets to set custom spending alerts. This proactive approach helps you manage your costs effectively.

GCP Cost Reduction and Performance Increase

We’ll pull back the curtain on the precise techniques and tools these companies employed, offering valuable insights that you can apply to your own cloud strategies.

Arabesque AI – Financial Asset Management, Saved 75%

About the Company
Arabesque AI is a key player in the financial asset management arena, specializing in leveraging artificial intelligence to create highly flexible and efficient investment strategies. A subsidiary of the Arabesque group, the company employs AI to sift through financial markets and identify patterns that can be universally applied to various asset classes.

Cloud Costs, Case Studies, Cost Optimization, Resource Utilization, Arabesque AI, GCP
  • Key Workloads: Data ingestion from third-party pipelines, AI-driven analytics, and customizable investment portfolio creation.
  • Core Tools Used: Google Cloud Functions, Cloud Run, Pub/Sub, GKE, and BigQuery.

Cost Optimizations and Resource Utilization Strategies
The company faced the challenge of efficiently scaling up its compute resources to train new AI models, while also managing costs. They adopted Google Cloud’s preemptible node pools within GKE to dynamically scale resources, which proved to be cost-effective and easy to manage.

  • Optimization Techniques: Use of preemptible instances in GKE, leveraging Cloud Run and Cloud Functions’ pay-as-you-go models.
  • Performance Gains: 10x increase in data streaming and analysis capabilities.

Arabesque AI was able to focus more resources on core research activities, thanks to the reduced operational overhead provided by Google Cloud. With strategic use of preemptible instances and dynamic scaling, they cut their server costs by approximately 75%, all while increasing their data analysis capabilities tenfold.

Current – Financial Tools for Teens, Reduced Costs by 60%

About the Company
Current is a fintech startup providing an innovative debit card and app tailored for teenagers. This one-stop solution assists teens in learning financial management by offering features such as savings goals, real-time balance checks, and chore-based earnings. Parents can also set automated allowances and monitor spending activities through real-time alerts.

Cloud Costs, Case Studies, Cost Optimization, Resource Utilization, Current, GCP
  • Key Workloads: Real-time financial tracking, user relationship management via Neo4j graph database, and GraphQL API for business operations.
  • Core Tools Used: Google Kubernetes Engine, Google Container Registry, and Google Stackdriver.

Challenges and Cloud Optimization Strategies
Initially built on a simpler infrastructure, Current faced scalability issues, particularly with their Neo4j graph database, as they grew to over 25,000 daily active users. The lack of an effective logging system further compounded these issues. By moving to Google Cloud and embracing containerization, they eliminated these bottlenecks and improved their service availability.

  • Optimization Techniques: Hosting applications in Docker containers, automated cluster management with Google Kubernetes Engine.
  • Performance Gains: 400% improvement in app development time-to-market, 80% reduction in error resolution time.

Current saw immediate benefits post-migration. They slashed their cloud hosting costs by 60% and improved app deployment times from days to mere hours. The ability to quickly identify and resolve issues led to an 80% reduction in error resolution time, freeing up resources for feature developments

Apxor – Zero-Code App Development, Saved 30%

About the Company
Apxor is a tech startup that enables mobile app companies to grow and retain their user base by enhancing in-app experiences through data-backed solutions. The startup notably ranks within the Top 100 Software Products in the 2019 G2 Crowd Awards. The company prides itself as a zero-code solution provider, meaning customers can implement its features without writing a single line of code.

Cloud Costs, Case Studies, GCP, Cost Optimization, Resource Utilization, Apxor

Apxor’s data points collection regions

  • Key Workloads: Real-time user feedback analysis, personalized user experience crafting, and data processing.
  • Core Tools Used: Google Compute Engine, Google Dataflow, and TensorFlow.

Challenges and Cloud Optimization Strategies
Before moving to Google Cloud, Apxor faced difficulties with their previous cloud provider, particularly regarding infrastructure deployments and bandwidth. After running several proofs of concept, Apxor switched to Google Cloud. This decision has led to optimized operations, cost savings, and scalability.

  • Optimization Techniques: Database hosting on Compute Engine, data processing with Dataflow, and machine learning applications through TensorFlow.
  • Performance Gains: 30% cost savings in infrastructure, freeing up DevOps time for new product roadmaps, and processing 1.5 billion data points daily.

Since making the transition, Apxor has observed several significant benefits. The startup cut infrastructure costs by 30% and liberated their DevOps team from time-consuming infrastructure management tasks. This newfound time has allowed the team to focus more on developing new product roadmaps and service offerings.

AWS Cost Efficiency and Performance Enhancement

As we delve into a new set of real-world stories, we shift our focus to companies that have leveraged Amazon Web Services (AWS) to transform their cloud economics and operational performance.

Discovery – Leading Media Content Provider, Saved 61% on TCO

About the Company

Discovery, a giant in the media content landscape, is renowned for delivering over 8,000 hours of original programming each year and boasting category leadership across multiple genres worldwide. With an innovative distribution strategy, the company reaches its global audience through diverse channels like direct-to-consumer streaming services and high-profile global partnerships.

Cloud Costs, Case Studies, AWS, Cost Optimization, Resource Utilization, Discovery

Discover’s cloud journey

Harnessing AWS’s Cloud Capabilities

To handle its complex media operations, Discovery places its trust in Amazon Web Services (AWS). The backbone of Discovery’s cloud-based playout infrastructure lies in Amazon Elastic Compute Cloud (Amazon EC2) and Amazon Simple Storage System (Amazon S3).

Approximately 1,000 Amazon EC2 instances are dedicated solely to playout, alongside additional instances allocated for rigorous testing and quality assurance. With this, Discovery manages to store a whopping 15 petabytes of broadcast content at any given time.

AWS Services Leveraged

  • Amazon EC2: For cloud-based playout infrastructure
  • Amazon S3: For reliable and scalable storage
  • AWS Direct Connect: For secure and high-speed data transfer to on-premises facilities
  • Amazon S3 Intelligent-Tiering: For cost-effective storage management

Cost Optimization Strategies

  • Downsizing Server Footprint: Reduced on-premises server racks from 130 to just 10 in London
  • Utilizing Reserved Instances: Leveraging Amazon EC2 Reserved Instances for significant savings
  • Storage Tiering: Using Amazon S3 Intelligent-Tiering to automatically move less frequently accessed data to lower-cost storage tiers
  • Volume Discounts: Bulk purchasing to avail lower prices

Expanding Business Agility

AWS’s cloud capabilities provided Discovery with enhanced operational resilience and agility. It enabled faster deployment of new features and applications and reduced the opportunity cost related to fixed asset management. A TCO analysis done in partnership with AWS Cloud Economics showed a 61% reduction in costs as compared to equivalent on-premises infrastructure.

Airbnb: Online Accommodation Marketplace, Saved 60%

About the Company

Airbnb has been a game-changer in the travel industry since its inception in 2007. From welcoming just three guests in a San Francisco apartment to hosting over one billion travelers across 220 countries, the company has come a long way. Airbnb operates in a digital-first environment, making cloud services crucial for its growth.

Cloud Costs, Case Studies, AWS, Cost Optimization, Resource Utilization, Airbnb

Airbnb’s cloud infrastructure

Key Workloads

  • Reservation and Booking Management
  • Data Analytics and Business Intelligence
  • Customer Experience Optimization
  • Real-Time Payment Processing
  • Content Management and Storage


Scaling at an unprecedented rate brought Airbnb face-to-face with complex cost optimization challenges. While AWS provided an agile platform to support Airbnb’s rapid expansion, it also posed dilemmas on managing resources effectively.

The fragmented data management strategy and increasing storage and compute costs needed to be tackled. Moreover, Airbnb lacked a centralized strategy for cost management, which was crucial for steering the company towards a leaner, more efficient mode of operation.

Tools, Services, and Strategies Used

To combat these challenges, Airbnb turned to a multi-faceted approach leveraging several AWS tools and services:

  • AWS Cost & Usage Report: Used for granular data analytics related to costs.
  • Amazon EC2 and Savings Plans: To secure cost-efficient, resizable compute capacity.
  • Amazon S3 and S3 Intelligent-Tiering: For optimized, dynamic data storage solutions.
  • Amazon EMR: Employs cloud-based big data platforms for advanced analytics.
  • Amazon OpenSearch Service with UltraWarm Storage: For a more cost-efficient logging infrastructure.

Airbnb also developed strategic methods such as:

  • Custom Data Pipeline: Customized analytics for actionable insights.
  • Cost Allocation Visualization using AWS Cost Explorer: For an easier, more intuitive understanding of their spending.

Overall Benefits

Airbnb’s journey with AWS has been one of continual improvement, both operationally and financially. By embracing AWS services, Airbnb was able to slash storage costs by 27% and reduce Amazon OpenSearch Service costs by an impressive 60%.

But the benefits were not purely financial. Airbnb built a cost-efficiency culture, facilitated by custom data pipelines and dynamic views of cost data.

Razer- Gaming Lifestyle Brand, Saved up to 90%


Razer, a titan in the gaming lifestyle brand arena, has been relentlessly pushing the envelope since its inception in 2005. Committed to providing high-performance gaming experiences, the company needed an infrastructure that could dynamically adapt to fluctuating traffic volumes—especially during weekends and post-work hours when gamers are most active.

Cloud Costs, Case Studies, AWS, Cost Optimization, Resource Utilization, Razer

Razer Synapse: The 1st Cloud Based Storage for Personalized Gaming


Razer is a mammoth gaming brand, boasting over 175 million user accounts as of 2021. To serve its user base, the company required a robust yet flexible infrastructure that could not only handle high traffic volumes but also cater to users’ individualized gaming profiles.

Their unique challenge lay in the sporadic nature of the gaming industry’s daily cycle—requiring elasticity to efficiently scale resources up or down, particularly during peak gaming hours. Traditional reserved instances could lead to underutilized capacity, translating into unnecessary costs.

Tools, Services, and Strategies Used

To surmount these challenges, Razer meticulously utilized a range of AWS services:

  • Amazon EC2 Spot Instances: For capturing unused EC2 capacity at up to a 90% discount compared to On-Demand rates.
  • Amazon EC2 Reserved Instances and Savings Plans: For handling predictable workloads at reduced costs.
  • Amazon EC2 Auto Scaling: To dynamically adjust the number of instances based on real-time demand.
  • AWS Graviton Processor: Deployed to achieve significant price-performance benefits across multiple workloads.

Razer strategically combined these services to optimize their operational efficiency:

  • Customized Auto Scaling: Based on market prices, ensuring cost-efficiency during high-traffic scenarios.
  • AWS Graviton Processor Migration: For delivering increased performance at a competitive price point.

Overall Benefits

By fully embracing AWS’ suite of tools and services, Razer slashed its Amazon EC2 costs by an astonishing 70–90%. The utilization of Spot Instances allowed Razer to achieve performance that met user demands, all while staying within budget. A seamless migration to AWS Graviton processors yielded an additional 5-10% cost savings, delivering an unparalleled price-performance ratio.


Managing cloud expenditures effectively is both an art and a science. As our diverse case studies demonstrate, remarkable cost savings are achievable with a customized blend of services and strategies.

Whether through Amazon EC2 Spot Instances, Reserved Instances, Savings Plans, or Google Cloud’s Committed Use Discounts and Custom Machine Types, these companies tailored their approach to align perfectly with their unique workloads and business requirements, resulting in substantial discounts on their monthly cloud expenditures.

If your organization is grappling with skyrocketing cloud bills or struggling to pinpoint the exact causes behind escalating costs, it’s time to consider a specialized solution. Economize offers a comprehensive approach to cloud cost management.

Schedule a free demo with Economize today and discover how we can help your organization slash its cloud costs by up to 30%—all within just 5 minutes.

Adarsh Rai

Adarsh Rai, author and growth specialist at Economize. He holds a FinOps Certified Practitioner License (FOCP), and has a passion for explaining complex topics to a rapt audience.

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