We’ve been building since 2020 (we were part of YC W21 batch), and iterating on the product, building out a team etc. However, back in 2020 one of our users asked if we can also show the carbon impact alongside costs.
It has been itching my brain since then. The biggest challenge has always been the carbon data. The mapping of carbon data to infrastructure is time consuming, but it is possible since we’ve done it with cloud costs. But we need the raw carbon data first. The discussions that have happened in the last few years finally led me to a company called Greenpixie in the UK. A few of our existing customers were using them already, so I immediately connected with the founder, John.
Greenpixie said they have the data (AHA!!) And their data is verified (ISO-14064 & aligned with the Greenhouse Gas Protocol). As soon as I talked to a few of their customers, I asked my team to see if we can actually finally do this, and build it.
My thinking is this: some engineers will care, and some will not (or maybe some will love it and some will hate it!). For those who care, cost and carbon are actually linked; meaning if you reduce the carbon, you usually reduce the cost of the cloud too. It can act as another motivation factor.
And now, it is here, and I’d love your feedback. Try it out by going to https://dashboard.infracost.io/, create an account, set up with the GitHub app or GitLab app, and send a pull request with Terraform changes (you can use our example terraform file). It will then show you the cost impact alongside the carbon impact, and how you can optimize it.
I’d especially love to hear your feedback on if you think carbon is a big driver for engineers within your teams, or if carbon is a big driver for your company (i.e. is there anything top-down about carbon).
AMA - I’ll be monitoring the thread :)
Thanks
I wonder how much this analogy applies to carbon tracking? Does using a wide variety of foods help make the tracking more accurate because no single bad estimate becomes overrepresented? Can a similar approach be taken via a wide variety of cloud technologies being used?
This probably would explain the success of many fad diets if it were the increased awareness of the eating having an effect beyond the decision making about what to eat.
The diets were meh. But the cool thing was that I learnt so much about food in general! I honestly didn't know much about food growing up. I feel like I still don't know that much, but I know the basics, and i'm not afraid of digging into some of the details.
A big focus now is applying this same level of rigorousness to different AI models and their impact. Batching, caching, model size and manufacturer are the choices engineers are making now. We want to ensure that choices being made are cost and carbon efficient.
Curious to know what decision you're making at the moment that's triggered you looking into your own methodology?
I take it from what you say here that you specialise in accuracy and consistency of measurement as a service and let the client judge for themselves what meaning to derive from them. It feels like it might be an invitation to Goodhart's law.
I'm in no decision making position myself (that said, had a few face to face conversations with people writing position papers). My interest is primarily in understanding what has the best outcomes and the ability of strategies to affect those outcomes.
To put an absurd case. Imagine adding a gadget to generators to use all of the CO2 as part of a cyanide manufacturing process which is then emitted. It gives you great CO2 emission numbers, but public health outcomes less so.
Let me ask one of the Greenpixie folks to jump in here, maybe they can explain how they do it!
We know CO2 emissions are not harmless though, so that example doesn't apply here.
Check this out: https://greenpixie.com/gpx-data Thoughts?
I really like the emphasis you place that reducing environmental impact is reducing cost as well. Tying civic mindedness to pragmatism is essential in dollar-hungry spaces.
You might be interested in this : https://github.com/Boavizta (open source it environnemental data)
Best regards,
Laurent
What does this say about accuracy, and I guess ultimately the impact of the emissions?
Whenever I have tried to find a meaningful measurement of environmental impact of power use I have gotten into a quagmire of statistics taking past each other, with arbitrary mixing of units and definitions. (Like energy/power/electricity being defined differently but used interchangeably. Similarly water usage being blended regardless of whether it is potable or from an area of scarcity)
The end result has to be what harm is caused, because harmless use of something at any magnitude is still harmless.
How do you figure out what that level is with any degree of accuracy. It's a difficult problem, but it seems that easier answers are not likely to be useful if they are not accurate.