r/cloudcomputing

Midsized shop on M365 E3 with renewal coming up in 8 months. Did a reorg last year and we're kinda stuck paying fo unused seats which is basically a waste of money for us. Can't drop them till renewal.

Got a quote from TrustedTech for moving to CSP instead of signing another 3 year EA. Pricing wasn't a huge difference overall, which kinda surprised me. Figured it'd be more lopsided one way or the other.

For anyone who's been running CSP a year or two in, dod the flexibility actually pay off, or did it end up feeling pretty similar to EA once you settled in? Also wondering how the partner led support compared to what you had before.

I’ve been noticing that as organizations move more workflows into SaaS platforms like Google Workspace, Slack, and Salesforce, access management becomes much more difficult to reason about than traditional infrastructure permissions.

In cloud infrastructure environments, access boundaries are usually centralized and relatively structured, but SaaS collaboration tools introduce a much more dynamic model where files, folders, links, and third party integrations continuously change who can access sensitive data.

What makes this especially challenging is that exposure often happens gradually over time through inherited permissions, external sharing, and accumulated access rather than a single obvious security event.

Leadership keeps asking why we made certain architecture choices, like going serverless instead of eks for some workloads. they want numbers, not just “it scales better”. we track things like deployment frequency and mttr, but when it comes to questions like kafka vs sqs, i don’t have much beyond rough cost estimates.

last quarter our bill went up around 12% after refactoring parts of a monolith, and finance flagged it pretty quickly.

i have tried pulling data from cloudwatch and cost explorer, but it’s hard to tie that back to actual impact in a way that makes sense to them. how are you handling this. what kind of data actually works when explaining these decisions to non technical leadership?

Every security audit i’ve been in asks is it encrypted and moves on. Nobody asks "do you know where every copy of that data actually lives."

Encryption is the easy part. The hard part is knowing you have PII sitting in a 4 year old RDS snapshot, a test bucket someone forgot about, and a CSV export in a shared drive that predates your current team.

If you cant list every place your sensitive data exists you aren’t protecting it. You just encrypted stuff you lost track of.

anyone else constantly forget to turn off their cloud instances? ran a batch process yesterday that finished in 10 mins, but i had to step away and the machine sat idle for 8 hours while the meter kept running. billing based on reservation time instead of actual code runtime feels so predatory. how do you guys automate shutting down instances the second a container exits without writing custom bash scripts every time?

With AI models getting larger every month, does it still make sense for startups and enterprises to buy expensive GPUs outright — or is on-demand GPU infrastructure the smarter move now?

Curious how teams are handling:

• multi-GPU scaling

• inference latency

• GPU underutilization

• rising NVIDIA costs

• vendor lock-in risks

Are we moving toward a future where computing is rented like electricity? Or will owning GPU clusters still be the competitive advantage?

We are sitting on a mix of old on prem servers and some pretty outdated aws setups. apps are a mix of java monoliths and some .net stuff that barely runs.

every time we try to move even a small piece to something more modern, something breaks. dependencies we didn’t know about, or performance drops hard once it’s in a new environment.

last attempt we lost a prod db connection for hours because some legacy vpc config didn’t play nicely with eks.

now leadership wants a full migration plan, but it’s hard to see how we do this without downtime or blowing the budget fixing things as we go.

How did you approach this.. any gotchas to watch for, or things that helped keep it stable during the move?

I keep getting stuck at the same point when comparing cloud instances. The specs look clear at first, but 2 vCPU / 8 GB RAM can mean very different things depending on the provider, CPU generation, storage setup, burst behavior and how the instance is placed.

So I created an open-source benchmark tool to make the comparison a bit less "lucky": https://fabianwimberger.github.io/cloud-bench/

The part that makes it useful to me is not only having several providers in one place with architecture, vCPU/RAM and monthly price. It also tracks history, so price changes and actually measured performance changes are visible over time.

The process is open source, reproducible and transparent: Terraform provisions fresh instances, Ansible runs the benchmarks, GitHub Actions ties it together and publishes the result.

I updated it recently with more Azure and Google Cloud instances to complete the big three. Azure was especially annoying to represent because a fair comparison needs a mix of burstable, normal x86 and ARM instances.

Obviously this is still not perfect. Storage type, region, CPU steal, burst credits and network latency all matter. But it has already been more useful to me than comparing only vCPU counts and memory.