Managing Reserved Instances and Savings Plans can be a hassle — it’s easy to overspend or underutilize prepurchased commitments. nOps automatically optimizes your RI and SP — for cost and time savings, maximum flexibility, and 100% utilization of the commitments we manage.

The Design Dilemma

Our users—engineers, architects, and finance leads—faced a trio of frustrations:

• Overwhelming Choices: Deciding how to commit to RIs and SPs felt like navigating a maze blindfolded.

• Unseen Risks: Misjudgments led to wasted funds on unused resources or lost savings from playing it too safe.

• Draining Effort: Hours spent crunching numbers stole focus from creative, high-impact work.

The Design Vision

Compute Copilot Commitment Management became our answer: an AI-powered feature that handles AWS commitments autonomously.

My design challenge was to make this automation approachable and meaningful. Here’s what guided us:

• Intuitive Autonomy: The system forecasts usage and adjusts commitments, designed to feel invisible yet dependable—like a co-pilot users don’t need to babysit.

  
  

• Clarity as a Cornerstone: I focused on interfaces that demystify the numbers, offering glanceable insights into savings and utilization.

  
  

• One-Time Setup Automation: Compute Copilot Commitment Management simplifies your AWS savings with a single setup. Once it’s running, it analyzes your usage, selects the best savings options, adjusts automatically as your needs change

Impact: The Triumph

The payoff? It’s been a game-changer:

The Process: Turning Challenges into Solutions

Creating Compute Copilot Commitment Management was about listening, dreaming big, and building something that works for real people. Here’s how we made it happen:

Discovery: Hearing the Real Story,

We started by getting to know our users’ struggles. We sat down with AWS customers—some stressed, others confused—and listened to their experiences with managing commitments like Reserved Instances and Savings Plans. We also dug into platform data to spot where things were going wrong. The problems stood out: people were tired of the hassle and losing money without realizing it.

Ideation: Picturing a Better Way

Armed with these insights, our team—product managers, designers, and cloud experts—rolled up our sleeves. I sat down with our cloud experts to define exactly what the solution should do and how it could help. We agreed: automation was the answer, but it had to be simple and smart. Together, we mapped out a tool that manages everything automatically, adjusts on its own, and promises real savings. This led to some big questions that shaped our plan:

Research/Interviews Outcomes

• How might we automate AWS commitment management, so users save time and effort?

• How might we make sure users get the best savings without wrestling with AWS pricing details?

• How might we show users clearly how their commitments are being used and improved?

• How might we stop money from being wasted on commitments that just sit there?

  
  

These questions guided every step we took next.

Design: Making It Real

This is where I jumped in. I created wireframes and prototypes—visuals you could click through—that turned our ideas into something concrete. The goal was a simple, hands-off dashboard where users could see their savings grow without any extra work.

It had to feel easy, because that’s what people needed.

Development: Powering It Up

Our engineers brought the magic. They built a system packed with AI and machine learning to watch usage data and tweak commitments in real-time. They linked it up to AWS, making sure it worked smoothly, and tested it over and over. We had to get it right—every penny mattered.

Iteration: Fine-Tuning Together

We didn’t stop at “good enough.” We shared early versions with the team and some users, asking what worked and what didn’t. Their feedback—maybe a button was confusing, or they wanted more alerts—helped us polish it until it shone. Every tweak made it easier to use and more dependable.

Launch: Lift-Off and Beyond

When Compute Copilot finally launched, it was a proud moment. But we didn’t just walk away—we kept watching how it performed, listening to users, and making small updates to keep it spot-on. This wasn’t just my project. Cloud experts, engineers, and customers all pitched in, and together we built something that’s changing the game.