Show HN: Easy cloud instance comparison (AWS, GCP, Azure, IBM, Alibaba and more)

This is looks like a great tool!

A few notes.

The default sort is price per vCPU, but all the ones that slow up initially are actually fractions of a CPU. Granted, getting the actually fraction is not made easy by the vendors.

Some providers, like digital ocean and linode include some bandwidth with the instance. If you have any egress at all, choosing one of those may have a large cost savings.

Thanks for making this tool, cloud pricing is a maze, and things like this help a lot.

It would be nice to filter by region too. Currently, when you select a global area (like North America) you end up with lots of duplicate machine types for the various regions within. I know the prices can vary between Central US and East US but it would be helpful to narrow these down when searching.

Great tool. Would be good to also include other services like: advania, linode, Hertznet. Often these are cheaper and have more RAM, some even offer DDoS attack. (AWS costs about $6000 from their DDoS team).

very useful tools, bravo! However when I select AWS as provider and Europe as region, it doesn't show anything.

When your initial goal was to find cheap GPU instances did you look at Genesis Cloud ( https://www.genesiscloud.com/ )? They currently offer Nvidia 1080ti and apparently soon RTX3080 and RTX3090 plus some AMD types.

One place for apples to apples comparison is a great idea.

Is there API access?

PSA: not all instances are covered by sustained usage discount

I wonder what are the use cases for this kind of hardware?

(I can't edit my comment as the timer has expired) What I'd find ultra useful would be to have common workloads, like CI/CD builds. For example, building a medium-sized node project and comparing the results.

Of course, that makes things even more subjective and makes burstable instances more difficult to compare but the results could be interesting.

> What am i missing, when i just want some root server?

You are missing the bandwidth charges. And recalculate with those too.

Other thing you're missing is that many people believe you either go cloud or build your own ASML-lithography machines. No in-between.

The use case is different. For the price of a cloud instance, on top of the instance itself, you are paying for:

* availability. On AWS, you can start a couple dozens or hundreds of instances on demand, for a limited time. You are paying for that spare capacity. VPS/Dedicated servers generally have much lower spare capacity, and you're booking things by the month, not by the minute.

* reliability. Most real cloud instances live on networked drives, and your risk of losing data is very low. On root servers, you have to handle data reliability yourself earlier. (you should do backups either way, but you're likely going to use your backups more often on VPS/Dedicated offerings than in the cloud)

* surroundings services. Private networking, security features, etc.

You pay a premium for all that, so for the same raw compute performance, cloud prices will be at least 2-3x the price of a basic root or dedicated server. On the other hand, vps/dedicated servers typically include bandwidth in the price. The best choice depends on your requirements, but most people will blindly go towards cloud servers..

Netcup is where I do all of my video transcoding. I ran tests on a huge swath of providers, and Netcup was by far the best bang for the buck.

That said, they’re not really on-demand (it’s a monthly bill), and the UI is probably the worst of all the ones I tried.

Still, very happy with them for dedicated, long running servers that you rarely touch.

Thanks. I will check if they have a pricing API available.

now*

Just make sure you have a plan to move from the tX instances into something non-burstable as soon as load gets to 30% or do.. when those machines run out of burst, the pain is instant and severe.

1.5x would be quite a large difference, would not expect that

cpus are complex, varrying instruction sets and such, you need to benchmark your particular workload to get a meaningful number

Not to mention sight difference on the underlying hypervisor, that may or may not impact your workload

Can be very different. I recently benchmarked it with a simple python based benchmark (as I use python most often) and in some cases the differences were even 2x.

Some results and benchmark here: https://github.com/vprelovac/python-speed

These were coming mostly from the fact that some providers updated to the latest gen AMD chips which was making big difference compared to 3-4 yr old Intel chips.

They're not even necessarily comparable within a vendor. The underlying hardware changes, and newer generations will (hopefully) perform better than previous generations. E.g., Azure publishes CoreMark perf stats for some¹ of their VM types, and the score can vary by 60%. (Now, I don't know how CoreMark scores translate into raw CPU perf.) But hopefully it's trivial to show that 1 vCPU of yesteryear's model != 1 vCPU of this year's model.

I think EC2 some sort of "ECU" to try to normalize them? I don't know of any other such attempts though.

¹regrettably, the older ones.

For digitalocean at least, there is quite a large difference between the “basic” droplets and ones with dedicated vcpus. In this table, they both look the same. I’ve seen 2-4x differences between the two: https://rob006.net/en/blog/how-more-efficient-is-cpu-optimiz...

Azure used to sell physical cores but AWS was selling Hyper-threaded cores. This made a big difference on pricing and performance of certain workloads like Oracle. These days, everything seems to be sold under the term vCPU which is an abstraction to represent the performance you can expect and is roughly similar to a hyper-threaded core. https://azure.microsoft.com/en-us/blog/introducing-the-new-d...

More than 1.5x variation in my tests. And dedicated Ryzen servers obliterate anything else in their price range.

Interruptible, "spot", or pre-emptible VMs are sold at a much lower cost with the caveat that they can be terminated at any moment. For workloads that pull from a queue for data processing or other "pausable" systems it can save a huge amount on costs. The cloud vendors get to collect ~1/3 - ~1/2 of on-demand rates instead of $0 while their hardware goes unused, effectively monetizing incremental compute power. Usually they are priced via auction, so its a truly efficient market.

From a response to another comment it seems most (all?) have a pricing API