Check out this forecast model I built using their new regression tool

That R-squared metric looks good on paper, but what's the actual sample size here? Six months of billing data for a quarterly forecast is practically anecdotal for FinOps.

I'd be more interested in seeing how the model performed on a holdback sample from your *own* historical data. Did you test it against a real quarter you already know the answer to, or is this all forward-looking prediction with no backtesting?

The clean formula is convenient, but it also makes it easy to miss underlying assumptions about variable independence that rarely hold true in real cloud spending.

Good point on backtesting, that's a crucial step the initial post didn't mention. Even with a solid R-squared, a model can fail completely out of sample if the relationships aren't stable.

The tool does let you split your dataset for validation, but you have to configure it manually. In a similar use case, I found my forecast fell apart when I applied it to a previous quarter because a key discount program changed mid-period. The regression just saw it as noise.

Always test against known history first, ideally more than one period if you can.

Absolutely, that manual split is crucial but it's still just a random temporal slice. For something like reserved instances, the seasonality or contract renewal cycles might not be captured properly if you just do a random 80/20 split.

You really need to configure the validation period strategically - like, train on Q1-Q3 and validate against Q4 to simulate the actual forecast you're trying to make. If the tool only lets you split by percentage, you could be validating with data from the same contract period you trained on, which gives you a false sense of security. Been burned by that myself!

That formula output is really interesting, especially for something you can plug into a spreadsheet. I'm new to forecasting but trying to learn for my own cost reports. How much manual data cleaning did you need to do before the import? Like, did your billing data need a lot of prep to get those clean categories like `ri_expiration_flag`?

That "clear formula" is the part that worries me. Handing a finance team a spreadsheet formula based on six months of data is a great way to trigger a budget variance post-mortem when reality hits.

You're modeling contract decisions, which are lumpy and discrete. A coefficient for `ri_renewal_coefficient` assumes the cost impact is linear and smooth, but in reality it's a step function. Renewing ten reservations isn't ten times the impact of renewing one, it's entirely dependent on which specific reservations they are and their individual pricing tiers. The tool is giving you a false sense of mathematical precision for a fundamentally non-linear business decision.

Did you check if the tool even lets you model interactions between the variables, or are you stuck with additive terms?

The point about variable independence is critical. I've built similar models and found you often need to manually create interaction terms - for instance, `compute_usage * ri_expiration_flag` - to capture that the cost impact of a reservation renewal is not constant but depends on the underlying workload volume. Does the tool's schema builder allow you to define these multiplicative variables, or are you limited to the base fields from your import?

Even with that, the linear assumption is a fundamental limitation. A better architectural approach might be to use the regression output for the baseline forecast and then layer a separate, rules-based simulation on top for the discrete renewal decisions, treating that cost delta as a separate scenario. Trying to force discrete business logic into a continuous coefficient will almost always break in production.

That clear formula output is actually a killer feature for me, even with the linear limitations everyone's pointing out. Having something you can immediately drop into a spreadsheet or a simple script beats wrestling with a black-box model when you're just trying to get a directional sense.

My question is about that `ri_expiration_flag`. Did you encode it as a simple 1/0 for "expires next quarter"? I've found the forecast gets way more useful if you can weight it, like flagging reservations by their monthly amortized cost. That way, the renewal coefficient isn't just counting contracts, but approximating their dollar impact. Does the tool let you use a numeric field for that, or are you stuck with true/false categories?

Good question. The cleaning effort depends entirely on your source data's structure. For a CSV export from AWS Cost Explorer or a similar tool, you need to manually create that `ri_expiration_flag` column. It's not automatic.

In my case, I had to join two datasets: the main billing line items with a separate reservation inventory list. I flagged any instance in the billing data that matched a reservation expiring in the forecast window. That join and flagging was done in a Python script before import.

> How much manual data cleaning did you need to do?

For a basic model? A few hours of scripting. To make it robust? Days. The real time sink is getting consistent categories over your entire historical period, especially if your tagging strategy or account structure changed. The regression tool needs perfectly aligned columns across all rows, so any missing data from six months ago can break the import.

The clean interface sounds great for getting started. As someone just learning this, was it difficult to figure out which historical fields to map as dependent vs independent variables? I sometimes get those mixed up in my own tinkering.

That clean interface really is a win, isn't it? I remember trying to teach someone linear regression for a similar forecast, and just getting them past the mental block of "which variable is which" was half the battle.

You hit the nail on the head about the formula output. The biggest practical hurdle for these tools is making their output *actionable*, not just a graph in a dashboard. Being able to copy that `Predicted_Cost = ...` line straight into a Google Sheet or a basic Python script for scenario planning is what gets it adopted. It's a bridge between the data science and the finance folks who just need a number to plug into a budget.

Did you run into any limitations on the number of independent variables it would let you map in the point-and-click builder?

The cleaning is the whole job. If your source data isn't already a clean time series, the tool is useless.

> How much manual data cleaning did you need to do?
All of it. You're not feeding it raw CUR files. You need to build a single table with consistent period-over-period dimensions, which means normalizing tags, handling account migrations, and backfilling any new categories. That flag column? That's a separate data pipeline joining reservation inventory to billing line items. Without that prep, your regression is garbage in, garbage out.

The tool just does the math. The engineering is on you.