When I started looking at bigger migrations, I realized something: manual discovery just doesn’t work at scale. Doing a few VMs by hand is fine. Doing thousands? Not even close.
I wanted a way to get accurate information about all the workloads without manually opening spreadsheets and notes for every VM. That’s when I started experimenting with automation and AWS tools — not to complete a project for anyone, but just to see what was possible.
Why discovery is hard at scale
The challenge is not collecting the data — it’s understanding it. When you’re looking at thousands of VMs, you want to know things like:
- Which VMs are similar and can be grouped together
- Which workloads are heavy and which are light
- Dependencies between systems
- Which VMs are ready to move and which need work
If you try to track all that manually, things get lost. Decisions end up being guesses instead of being based on real data.
Starting with automation
I started with the AWS agentless connector. It lets you gather VM and system info without touching each machine individually. That immediately made the process manageable. Suddenly, I could get a full view of the environment consistently — and without installing agents everywhere.
Having clean, structured data is half the battle. Once you have it, you can start thinking about patterns instead of just numbers.
Adding AI to make sense of it
Once the data was collected, the next question was: how do I make sense of thousands of rows? That’s where I tried using AWS AI and Transform services.
I didn’t want AI to make decisions for me. What I wanted was help in spotting patterns and outliers:
- Grouping similar workloads
- Highlighting unusual VMs that might need extra work
- Surfacing clusters of resources that could move together
This let me focus on the architecture instead of drowning in spreadsheets.
What I learned from the experiment
A few things became clear:
- Automation is necessary for scale, but insight is still human-driven.
- AWS-native tools like the agentless connector save a ton of effort.
- AI is useful for surfacing patterns, not for making final migration choices.
- Structured discovery early can prevent headaches later in the migration.
Even though this started as just a personal experiment, it completely changed how I approach migration planning.
Wrapping up
For anyone planning large-scale VMware to AWS migrations, the key is to stop thinking of discovery as a checklist. Think of it as building a foundation: structured data, automation to scale, and AI to help interpret it.
It doesn’t make decisions for you, but it makes the problem manageable, predictable, and less stressful.
About me
Hi I'm Nivetha Gopi - Sr. Solutions Architect with hands-on experience supporting, automating, and optimizing mission-critical workloads on AWS. I work closely with cloud infrastructure, DevOps, and automation, and strongly believes that growth comes not just from what we know, but from how effectively we figure out what we don’t.
Top comments (0)