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We take a cloud agnostic approach to systems development so we have flexibility. Our team is quite small and we use Manageengine for patching servers and Atera for patching users systems. We only use a few cloud native services like AWS event bridge, load balancers, S3, Lambda, Azure DNS, Azure storage, Azure App service. But if needed we could pull any one of those and move to an open source solution without too much fuss. The red tape comes from exec level and their appetite for risk. For some reason they think cloud is more stable than our own servers. But we had to move VMs off Azure because of instability!
There's a cost to keeping an agnostic solution that maintains that portability. It means forgoing many of the features that make cloud attractive. If your enterprise is small enough it is certainly doable, but if you ever need to scale the cracks start to show.
If you're treating Azure VMs as simply a replacement for on-prem VMs (running in VMware or KVM), then I can see where that might cause reliability issues. Best results means a different approach to running in the cloud. Cattle, not pets, etc. If you were using Azure VMs and have two VMs in different Availability Zones with your application architecture supporting the redundancy and failover cleanly, you can have a much more reliable experience. If you can evolve your application to run in k8s (AKS in the Azure world) then even more reliability can be had in cloud. However, if instead you're putting a single VM in a single AZ for a business critical application, then yes, that is not a recipe for a good time Nonprod? Sure do it all the time, who cares. You can get away with that for awhile with prod workloads, but some events will mean downtime that is avoidable with other more cloud native approaches.
I did the on-prem philosophy for about 18 years before bolting on the cloud philosophy to my knowledge. There are pros and cons to both. Anyone that tells you that one is always the best irrespective of the circumstances and business requirements should be treated as unreliable.
Kubernetes is extremely expensive on cloud so we run our own in house
Our problems with VMs on Azure were: