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.
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.
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!"
we ran away screaming from stackdriver when we saw how costs started piling up.
thank god for prometheus and grafana.
Edit: The example from their pricing page:
> We will assume you have 1 end user monitoring a dashboard for an average of 2 hours per day refreshing it every 60 seconds with 20 chart widgets per dashboard (assuming 1 PromQL query per widget)... assuming 18ms per query for this example.
Comes out to over $3 per month in query costs. Replace this 1 person with a TV showing the dashboard all day, and the cost jumps to $36, for just one dashboard and (again IME) overly fast query estimates... o.O
Like most metrics systems, under the covers in Prometheus each unique combination of dimensions is the same as a new metric line.
Prometheus is a bit of a different story. It does have some operational overhead when you get to a certain point, and scaling it out is not always trivial.
Assuming it works, there is value-add on this one, and the pricing is more in line with active use (ie, a cost+ model, which is more typical of AWS services)
> AMP counts each metric sample ingested to the secured Prometheus-compatible endpoint. AMP also calculates the stored metric samples and metric metadata in gigabytes (GB), where 1GB is 230 bytes.
Surely that's a typo, right?
I understand that the Google version of Prometheus is deprecated but there is no commercial equivalent.
Borgmon was inspiration for prometheus but was a totally different project so it is a complete rewrite
Almost have to change gears and get into a scientific field that isn't computer science.
We've put a lot of effort into optimizing the Kubernetes experience that non-containerized installations haven't been getting as much attention. We'd be thrilled to have system packages for Loki that also set it up as a service, it's just not something we've been able to spend time doing ourselves yet.
The expectation that someone doing greenfield development is going to jump into k8s just to use the software is kind of weird.
In the announcement it says AWS have a commercial relationship with Grafana Labs, where several Prometheus maintainers, community managers, etc. currently work.
(I work for Weaveworks)
https://grafana.com/blog/2020/12/15/announcing-amazon-manage...
