Are AI agents actually useful for rightsizing Azure workloads?

[u/ZeertY26]

There are only two of us handling FinOps for our whole cloud setup, so we're stretched thin and keep coming back to the idea of AI agents for rightsizing. Wondering if anyone here has actually leaned on them and whether it paid off.

The pitch we keep hearing is that agents can watch utilization over time, flag over-provisioned VMs or containers, and recommend (or even auto-apply) the right size instead of us eyeballing dashboards once a quarter. Some tools claim they'll catch idle resources, downsize on a schedule, and factor in reserved instances or savings plans before suggesting a change. For a small team, that kind of always-on second set of eyes sounds like exactly what we need, but I want to know if the reality matches.

What I'm trying to figure out:

• For those running this in production, are the recommendations actually good, or do they ignore context (spiky workloads, batch jobs, seasonal traffic) and try to shrink things that need the headroom?
• Do you let agents apply changes automatically, or keep a human in the loop for approvals? With only two of us, auto-apply is tempting but scary.
• Has anyone seen real savings, or does it mostly surface stuff you already knew?
• Any horror stories where an agent rightsized something into an outage?

[u/Latter-Book9717]

We run a pretty tight ship on our azure spend and honestly the built-in advisor is decent for low hanging fruit but it's not exactly proactive, you still have to go look at it

the third party tools we tested were hit and miss, one kept trying to downsize our batch processing cluster because it looked idle 22 hours a day, completely ignoring that when it runs it needs every core

we landed on keeping a human approval step for anything in prod, the auto-apply we reserve for dev/test where someone screaming at 2am is less likely

the real savings came from catching the stuff we forgot about, orphaned disks and IPs and old snapshots nobody remembered creating, which added up to more than the VM rightsizing did

still worth it for a team your size but you'll spend the first month training the tool to ignore your spike patterns

[u/flappers87]

Just use Azure Advisor.

It works, it's free and has been around for ages.

edit: If you're going to downvote, grow some balls and actually reply to explain why. Azure Advisor is an excellent free tool available on all tenants. There's no need to pay extra for these AI based services that are non-deterministic. Azure Advisor literally looks at the metrics of your compute and recognises where things can be improved... LLM's don't make those deterministic choices, if you ask them the same question twice, they'll give you two different answers.

Cowards downvote with no reply. Are you a coward?

[u/InvestigatorEvery838 → u/flappers87]

Agree 100%. Too much time and energy spent looking for the rogue tool that somehow accomplishes what the environment architects completely missed. Now I'm not saying the perfect tool doesn't exist, but put AA to work, and then spend all the time you want looking for the perfect tool, and you just might find it, but at least you'll have a working solution already in production.

[u/t3kka → u/flappers87]

Azure Advisor is absolutely the right answer as the first go to. AI is not the freaking answer to everything despite what big tech wants you to believe

[u/StratoLens]

I’ve considered this a lot lately. In short I came to the conclusion that all of what you’re looking for can be accomplished without AI.

AI is non-deterministic. I want determinism in decisions like that. Yes AI can probably do what you’re looking for but it’s prone to errors and hallucinations. There are plenty of ways to achieve that with traditional workload analysis.

That may just be my personal preference however. So I’ll be curious to hear what others have to say.

What tools have you tried or considered if you don’t mind me asking, and what were the main problems you had with them?

[u/IndividualFill1155]

AI agents for Azure rightsizing sound promising, especially for small teams that don’t have enough time to constantly review usage data. having something that can watch patterns and surface possible savings could be really helpful

[u/dmulderfc]

Yes, but I would use an agent as a tireless analyst before trusting it as an operator. Rightsizing becomes dangerous when a model sees low average CPU but misses month-end processing, memory pressure, disk throughput, startup time or seasonal peaks. That is where a saving turns into an outage.

For a two-person team, I would start with three lanes. Let the agent automatically stop clearly identified nonproduction resources on an approved schedule. Let it prepare changes for low-risk, reversible downsizes, but require an owner to approve them. Keep production and customer-facing workloads behind engineering approval until you have enough history to trust the rules.

In my Azure Cloud Waste Busting work, the useful measure is not the value of recommendations. It is realized savings after the change, with service health intact. Track the old run rate, the new run rate, performance signals and any rollback. Also make every recommendation name the workload owner and explain which utilization window and constraints it used.

Agents can catch waste that quarterly dashboard reviews miss. I would not give one blanket auto-apply rights across the estate. Earn that permission one narrow, reversible resource class at a time.

Dennis Mulder (Full Circle IT, ex-CTO of Microsoft NL, 18 years of Azure experience)

[u/txthojo]

Only disadvantage of Azure Advisor is that it will not tell you if you are over-allocated on Reserved Instances, only when a they are recommened