Cloud Cost Optimization

The company, facing inefficiencies with cloud resources after five years of operations, needed to reduce costs and optimize performance. We developed AI-driven predictive models that analyzed resource usage and recommended rightsizing strategies. By implementing these models and optimizing instance selection, the company achieved significant savings, including a 35% year-over-year cost reduction, while enhancing operational efficiency and scalability.

Linquip is an industry-focused B2B supply chain platform for equipment manufacturers, industrial customers, and service providers. We automate the workflow of sourcing processes for industrial buyers and procurement teams and modernize sales and marketing for industrial manufacturers and service providers.

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Sunnyvale

Location

Linquip

Energy

Industry

Challenge

The company had been operating in the cloud for over five years, managing a wide array of resources, including compute servers (both CPU and GPU), petabytes of storage, relational and non-relational databases, clusters, load balancers, and DNS servers. Despite their cloud-native experience, they faced significant financial inefficiencies due to underutilized or oversized resources. The company needed a solution that could identify wasteful operations and help optimize costs in real-time, allowing them to maintain performance while reducing unnecessary expenses.

Solution

Leveraging our AI tooling and model development expertise, we built a predictive modeling solution that analyzed resource logs to identify optimal configurations based on resource usage metrics such as CPU, memory, GPU utilization, and I/O throughput. Our solution provided recommendations for rightsizing server instances and offered alternative configurations with cost options across on-demand and reserved instances.

In addition to optimizing existing resources, we assisted the company in capturing requirements during the bootstrapping of new servers, ensuring they selected the best instance types for their needs. The predictive models, built using Keras and other advanced libraries, delivered high accuracy, enabling the company to realize multi-million dollar savings and achieve a 35% year-over-year cost reduction.

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Discover Our Approach

Resource Log Analysis

We collected and analyzed logs from various cloud resources to determine usage patterns and identify inefficiencies.

STAGE 1

STAGE 2

Predictive Modeling and AI Tooling

Our AI-driven predictive models, developed using Keras-based libraries, recommended optimal instance sizes and configurations to rightsize resources.

Cost Optimization Strategies

We provided recommendations with various cost options, including on-demand and reserved instances, enabling the company to make data-driven decisions.

STAGE 3

STAGE 4

Proactive Server Bootstrapping

By capturing requirements early in the server provisioning process, we ensured that the correct instance types were selected to maximize utilization and minimize waste.

Impactful Savings and Efficiency Gains

The solution enabled the company to reduce cloud costs by 35% year-over-year while maintaining operational performance and scalability.

STAGE 5

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