CloudFactory launches Accelerated Annotation after acquiring
04.01.2022 — Vladimir

Pricing a vision AI project part 2: Understanding the costs

In our previous article in the series, we looked at how you can prioritize AI projects and gave a quick back-of-the-napkin calculation to use for getting an initial budget. In this article, we'll look at the costs you'll encounter when working on an AI project. We will also highlight some hidden costs that you might not have considered if it's your first time working with AI.

Pricing a vision AI project part 2: Understanding the costs

Building proofs-of-concept to understand needed resources

The most common question we get in Hasty goes something like this "How much data do I need to label to get my metric to X accuracy?". This is an impossible question to answer. As all AI models are black boxes there's no way of telling how much work you have to put in to get the result you are looking for when you are starting out.

This is not really an experience question either. Internally in Hasty, we've seen hundreds of projects up close. You might think that we would be able to look at similar projects we've seen before and give a very rough benchmark answer to the question above. 

To make it more concrete, let's say we've worked with a research institution in Germany that worked on a forestry application to detect wildfires automatically. Now, another research institution in Brazil approaches us with the same use case. If given permission, it would be easy for us to tell them exactly what they need, right?

Unfortunately not. By changing the location, you've changed the data you need to look at. For the sake of argument, let's say that in Germany slash-and-burn agriculture is less common, but in Brazil, it's quite popular. And the Brazilian research institution wants to include those situations in their model.

Furthermore, maybe one team has data from drones, and the other team uses satellite imagery. Now we changed another variable. And so on, so forth. It's highly unlikely that anyone will have seen a project with the same variables as yours - and if that's the case, it's probably an organization in direct competition with you.

With that in mind, how do you know how much you should pay for an AI project?

The answer is to continually work in quick iterations. The further you get into the project, the easier it is to estimate the cost of the next step. With that in mind, the recommendations we give all our new customers are:

The costs of a vision AI project

So we've discussed prototypes but we still haven't talked about the costs you'll encounter when working on AI. Let's change that.

In general, the costs of developing vision AI solutions can be divided into six groups:

How those costs are split differs from project to project. But making a rough estimate of a "typical" project, based on what we have seen helping with 100+ projects, we can say that data creation and curation will make up between 20% and 40% of your costs. Machine learning engineering is another 20%, as is infrastructure development. App development is another 10%. Data acquisition is another 5%, leaving us with 5% of the budget for software costs and unforeseen overheads.

These costs will of course vary quite a bit. Let's say you are doing something where you need vast amounts of data like autonomous driving. If so, data creation and curation might take up 70–80% of your budget. On the other hand, if there are already good data available or if you have a use case with little variance in your data, the data creation and curation budget might only be 20%.

The budget distribution also tends to change over the lifetime of a project. At the start, the main cost tends to be machine learning engineering and setting up infrastructure. Over time, as you have the foundations in place, that shifts to spending more on data creation, curation, and app development.

The hidden costs of a vision AI project

To make things even more difficult, there are many potential hidden costs in any AI project. These are:


In this article, we have gone through how you can start a project in the best possible way for your budget. We have also detailed the existing costs that you will encounter during an AI project. In the next article in this series, we will give a more concrete example of costs where we illustrate the costs for various stages of the development phase.

Shameless plug time

Only 13% of vision AI projects make it to production. With Hasty, we boost that number to 100%.
Our comprehensive vision AI platform is the only one you need to go from raw data to a production-ready model. We can help you with:

All the data and models you create always belong to you and can be exported and used outside of Hasty at any given time entirely for free.

You can try Hasty by signing up for free here. If you are looking for additional services like help with ML engineering, we also offer that. Check out our service offerings here to learn more about how we can help.

Keep reading

Get ready to scale your project

For 80% of vision AI teams, data is the bottleneck. Not with us.