[slyall]

The pricing just for the ingest seems way off. $0.002 for 10,000 metrics might not seem like much by even a simple node_exporter will grab 700 metrics every 15 seconds.

Thats $24/month just to ingest the cpu/ram/diskspace data from each server. Plus storage and query costs.

At work I have a single r4.xlarge instance handling 1.3 million metrics every 15 seconds. Storage is not clustered but cost is only $500/month. It would cost me $45k/month just for the ingest with the new managed service.

[throwaway343233]

Pricing makes sense if you consider how Amazon operates at this point.

You put basically a MVP product out there with abnormal pricing. Your enterprise customers that are drowning in money can start using it and using that money you can grow your org by hiring more engineers. At this point you start working on adding new features and do cost optimization. Since your whole architecture was designed based on "we have to ship this ASAP", you deliver some real nice cost reduction easily. Then you reflect this to your customers and gain goodwill and good PR.

[andrewstuart2]

And let's be honest. We all know a company or two that would throw _way_ more than 45k/year at a global metrics solution to handle that volume, and still wind up with a flaming scrap heap. And a promotion or two.

[ec109685]

I don’t think any company is drowning in money. Everyone has a budget they are working against. At the end of the day, you can bite an engineer or pay aws more. It’s all a trade off.

[mchusma]

Their pricing for these managed services used to be "no brainer" (something like the cost of compute only, or maybe a <30% upcharge). Managed airflow was similarly very expensive (maybe 3x the cost). Just not worth it. Bummer.

[wpietri]

Yeah, it turns out there's a lot of money to be made from people who don't have a good grasp of the fundamentals. We got a marketing email from Huggingface recently about their ML-models-as-a-service offering: https://huggingface.co/pricing

One of my colleagues asked if it might be better than creating our own infrastructure for that. I ran the numbers for one of our recent jobs, feeding a million tweets to two ML models to see which worked better. That would have cost about $1800 on Huggingface. Using AWS spot instances, it was maybe $25 for us to run ourselves.

Of course, we can do it at that price because we are paying for engineers and plan on classifying enormous amounts of text, so it works out for us. Plenty of other people probably should just use Huggingface. But I can't help looking at that 70x markup and think, "Fuck me? No, fuck you!"

[gautamdivgi]

Pricing makes sense for enterprises. Considering that you may need a team (or a part of one) to maintain a self-hosted cluster at possibly 0.995 reliability, do upgrades, manage devs, run all the mandatory security scans, justify why some enterprise scan tool throwing errors isn't an issue, etc. Oh also justify why you need the manpower to do it, at which point your VP will tell you to just use the managed service.

[qz2]

It doesn’t though. I just did a cost projection on our estate and hiring two engineers to look after it on bare metal VMs is 30% cheaper than using the managed service. Plus it doesn’t require a lot of maintenance so we can use those guys on improving the product as well which actually gives direct customer benefits.

[kobalsky]

both google and amazon are insane with their observability services.

we ran away screaming from stackdriver when we saw how costs started piling up.

thank god for prometheus and grafana.

[potamic]

That's probably lesser than your team's payroll budget :) Their positioning is that you can reduce the staff needed to operate and maintain these instances.