AWS drops price for managed Prometheus service by 84%

Having used Monarch at Google (basically an explicit relational algebra for a query language), the fixed-function query "language" for DataDog is truly sad. It seems that prometheus made similar mistakes to borgmon in terms of designing a quirky query language, but at least it has one.

For an example of annoying behavior in datadog:

* Zoom in on any chart with default_zero. Eventually the spaces between the points will go to zero. You have to add a rollup whenever you use default_zero to avoid this.

* In general, you have very little control over interpolation, which is a very important part of aggregating and graphing.

* exclude_null will remove any series with any null label. You can't write arbitrary filters on series labels.

* Label names and values have to be the same to match. E.g. you can't join 2 metrics where different labels have the same value.

* The GUI doesn't support a lot of the features, e.g. timeshift.

* The available functions often can't be composed. E.g. you can't min by X and then sum.

I have a ton of complaints about the GUI too (e.g. it's broken by typing too fast), but I'll save those for later.

I'm the worst kind of person to reply, because my brain was already damaged by Borgmon before Prometheus was a thing. But I've run both at relative scale, and I'll take prometheus anyday. Datadogs query language is lacking and they've never added decent support for ratio-of-rate metrics, which are most of the metric queries you should be alerting on. Datadog is expensive, and because it's expensive you'll hold back on some of your most important and expressive metrics. Prometheus scales very far vertically - Far enough that I've yet to use it's sharding features, despite abusing tags badly.

Prometheus's query language is a sharp tool, and you can mess it up. It takes getting used to, and Grafana is a must for making charts navigable to executives (which is one of the top selling points of Datadog: Execs can see and build dashboards for the metrics they want). I would not be likely switch to another system - Even Monarch, Google's next generation monitoring system, has significant downsides compared to what you can do with Prometheus.

I have really wanted to get a Prometheus into production for several years and have tried a few times, but working at small startups without dedicated ops people I‘ve found myself mucking around too much trying to get the metrics that I really care about wired up, whereas Datadog “just works”. Datadog’s pricing gives me a bit of indigestion but it’s pretty slick.

Yeah used both in prod and was pondering the same question. On top of that dd was ridiculously expensive and resource hungry in kubernetes environment. I’m biased though bc i used the prometheus inspiration extensively at google. For someone who doesn’t use as many custom metrics (which I suspect are most orgs out there) dd is probably superior bc it gets the job done (mostly) and has ok ui

Considering that one is free and that the other is not cheap, I'd like to see some data demonstrating this since common sense would dictate otherwise.

Links?

Not necessarily. They might be doing this to get more customers on it since they're losing to Datadog.

Amazon has a long history of being willing to eat a ton of loss to kill competition like in the very popular diapers.com business.

I used to work at AWS and I have no first hand knowledge in this case, but would guess it's far more likely that the engineering teams figured out a way to run it cheaper than at launch, and passed that savings along to customers.

doesn't have to be, they could sustain losses and count it as a marketing expense.