AWS DevOps Cloud Cost Optimization: How an AI Company saved 90% on Cloud Costs

In this cloud cost optimization case we cut costs by $18K/ month using AWS Devops automation with Gitlab, Kubernetes, and AWS Spot Instances.

AWS Devops Cloud Cost Optimization: How An Ai Company Saved 90% On Cloud Costs


The AI technology company was using regular EC2 instances to process all the data on its machine learning algorithm. They only used the instances 10% of the time so they were over spending by 90%. Not only were they wasting $18,000 on idle compute power every month, their EC2 deployments had been crashing. This slowed down their development cycle and caused unnecessary frustrations for their team. Gennovacap’s AWS devops consulting team optimized the deployment process and switched their EC2 instance types to an EC2 spot fleet so that they could consume compute on demand when they needed it. As a result of this cloud cost optimization strategy the company saved $18,000 / month on AWS cloud costs.


Even if cloud cost optimization wasn’t an issue, the slow, crashing deployments based on their existing AWS devops strategy meant no amount of EC2 computing power could effectively improve their development cycle performance. They needed to overhaul their devops strategy before they could scale. The major issues they faced were:

  • Paying $20,000 per month on EC2 servers

  • It was expensive to scale

  • Deployments took too long to start

  • Deployments would crash

The AI technology company was using the same EC2 Reserved and On-Demand Instance type for its DevOps – instances they only used 30-40% of the time. Not only were they wasting $18,000 on idle computing power every month, their EC2 deployments had been crashing. This slowed down their development cycle and caused unnecessary frustrations for their team.

Hiring an internal DevOps engineer experienced in Gitlab, EKS and EC2 Spot Instances would most likely take months. Training the engineer to be knowledgeable enough about the AI Company’s existing pipeline, would take even more time. Time that engineer would be paid their hefty salary. Time they could have spent scaling up if they had restructured their DevOps sooner.

Project Dates

February 2018 – January 2019

Existing Technologies

Cloud Hosting: AWS EC2

Devops / Deployments: Jenkins 

Machine Learning: AWS Sagemaker

Consulting Objectives

To provide devops as a service which supports all infrastructure and environment modernization (both non-prod and production environments). Secondly, to provide a cloud cost optimization strategy as well as provide much-needed resources to allow in-house staff to concentrate on developing and shipping the ML software.

Solution: Hire Consultants

AWS CLOUD SOLUTION: AWS EC2, Autoscaling, Load Balancers, EKS, ECS EC2 Spot Instances

DEVOPS SOLUTION: Gitlab, Gitlab Runners, Kubernetes, Docker, Terraform, AWS Cloud Formation Templates


Gennovacap’s DevOps Consulting team replaced their CI CD pipeline using Gitlab Runners, Amazon Elastic Kubernetes Service, and AWS Spot Instances instead. Not only did it cut their costs, but the stable development cycle meant they could finally scale up.

Unlike hiring a full time engineer or training an in-house SE to become a devops engineer, Gennovacap can rapidly transform your development cycle into one that is effective, efficient, and can scale with your team. The right team of AWS DevOps consultants know what questions to ask, technologies to use, and solutions make sense for your business.

In just one month, Gennovacap was able to turn the AI Company’s unstable, costly DevOps into a fully automated, seamless, high-performance process. They no longer paid for Reserved and On-Demand Instances they didn’t use, lowering their Amazon AWS billing by 90%. Not only did they dramatically cut their cloud costs, they started seeing those savings much more quickly than if they had hired an engineer.

Few things are a bigger headache for developers than an unstable toolchain. With Gennovacap’s DevOps automation, the AI company no longer had the growing pains caused by crashing docker containers or manual checkpoints.

But the wrong DevOps consultants can certainly be an even bigger pain than crashing deployments. That’s why Gennovacap never follows a stale template that might not be the right fit for your business or tries to sell you unnecessary services. Our experience in AWS enables us to provide devops strategies  that are tailored to your development team.

AWS Devops Strategy + Benefits

90% Cost Reduction In Cloud Costs – Saved $18,000 / month on AWS CLoud

By employing a cloud cost optimization strategy and migrating from expensive monolithic servers to AWS EC2 Spot Instances, we were able to enable to minimize costs when the servers were not being used and obtain a 90% cost savings for the client. 

The AI company had been spending $20,000 per month on monolithic EC2 servers. Gennovacap’s AWS DevOps team was able to reduce this cost by $18,000 by taking advantage of AWS Spot Instances. Before restructuring their cloud to rely less on reserved instances and more on spot instances, the AI company was spending $20,000 /month on EC2. After diversifying their EC2 servers, the AI company was only spending $2,000 per month on cloud costs.

By the time it would take to hire and train the right DevOps engineer, the AI company would be spending tens of thousands of dollars on unnecessary cloud costs. Time that could be spent scaling up their development process sooner.

Not only did they save 90% on their cloud costs, but because their EC2 servers were diversified, their development cycles became quicker and more efficient.

Cloud Cost Optimization Savings Analysis

When compared to the 7 month process of hiring an experienced DevOps engineer, this AI company saved $83,000 hiring Gennovacap instead. Here’s how we calculate the savings:

By switching to EC2 Spot Instances, the AI company’s cloud costs were only $2,000 per month instead of $20,000. That’s $18,000 they saved every month.

The hiring process for a talented DevOps engineer takes an average of three months. And the time for that engineer to restructure your development process can take another four months.

That’s seven months the AI company wouldn’t be saving by switching to Spot Instances, paying $140,000 for their cloud costs. Plus, they would be paying that talented engineer a respectable salary of $50,000 over those four months.

Before they even started to see the cost savings, the AI company would have already spent 7 months time and $190,000.

But because they hired Gennovacap instead, the AI company began saving 90% on cloud costs after just one month. Instead of paying Amazon $140,000 for unused computing capacity over 7 months, they paid just $32,000. That’s a savings of $108,000.

So even by paying Gennovcap’s consulting fee of $75,000, the AI company ended up only paying a total of $107,000 over the same 7 months.

In total, the AI company saved $83,000 by choosing Gennovacap to restructure its AWS DevOps. They were also able to begin scaling their development process six months earlier.

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