After years of wrestling with surprise invoices and murky cost allocations, I finally snapped and built an internal dashboard to track our analytics spend against actual business value. It pulls data from our usage logs, Snowflake credits, and our own revenue/engagement metrics.
The goal is simple: connect the dots between what we pay and what we get. Early days, but seeing which dashboards are actually used vs. which are just expensive "shelfware" has been eye-opening. Anyone else tried something similar? Would love to compare notes on what metrics you track.
~Isla
I'm a community lead for a mid-market SaaS, and we've been running a custom cost-value dashboard in production for about 18 months, built on top of our existing Metabase and Snowflake stack.
Here's a breakdown based on what we learned building it versus evaluating a couple of commercial options (like CloudTruth and Stem):
**Deployment / integration effort**: Ours took about 6-7 engineering weeks to get to a stable v1. The biggest time sink wasn't the dashboards but building and maintaining the data pipeline that joins billing feeds, query logs, and our internal product events. Expect to spend a day or two per source system initially.
**Real pricing**: Commercial tools we looked at were roughly $4-8/user/month for the core team, but required an annual commit. The real hidden cost was the compute for running their data collection agents, which added about 10% to our Snowflake bill until we optimized the queries.
**Where it breaks or the honest limitation**: Attribution is the hardest part. You can see a dashboard cost $500 last month, but connecting it to a specific revenue spike is mostly guesswork. Our home-built system only shows correlation, not causation, which is a ceiling we haven't broken through.
**Where it clearly wins**: Having full control let us tie in niche business metrics that no off-the-shelf tool supported, like cost per active committee (we're in advocacy software). It also forced our finance and data teams to agree on definitions, which was an unexpected benefit.
I'd recommend building your own if you have the engineering bandwidth and your value metrics are truly custom, like tracking cost per active committee. Go with a commercial tool if your main goal is straightforward cost allocation and your team needs a solution in under two weeks. What's your team size and timeline?
Stay constructive
This is such a classic and frustrating pain point, I'm glad you're tackling it. The "shelfware" insight alone is often worth the build.
We did something similar, but our biggest learning was to tag the *business goal* for each dashboard or report at creation (e.g., "retention alert," "sales pipeline forecast"). Then, when you look at cost vs. usage, you can also ask if the stated goal is still active. We found a few expensive reports that were used weekly but were tied to a product feature we'd deprecated a year ago 😅.
What are you using to define "business value" on your end? Is it purely downstream revenue attribution, or are you factoring in operational or support savings too?
That's a great idea, the surprise invoice problem is so real. I'm building something similar but I'm terrified of messing up the data pipeline and making wrong decisions. How are you handling the join between the Snowflake credits and the usage logs? I'm worried about time zone mismatches or latency in the logs skewing the daily cost attribution.
Time zone mismatches are a killer with this kind of join. We handle it by converting everything to UTC at the raw data stage, before any joins happen. Our pipeline uses dbt for that transformation layer.
For latency in Snowflake's usage logs, we found a ~3 hour delay was typical. We built our cost attribution logic to look back at the previous day's full logs, and we only consider a day "final" after 6 AM UTC. That means our dashboard is always one day behind, but the data is consistent.
What's your extraction method? The `SNOWFLAKE.ACCOUNT_USAGE` schema is the most reliable source for credits, but it can lag. If you're using something like the Snowflake API or a third-party tool, you might have more variance to account for.
Data is the new oil - but it's usually crude.
The "shelfware" realization is the first step toward actual cost control, but I'm always wary of what comes next. You're connecting spend to usage, which is good, but how are you defining "actual business value" from your revenue metrics? That's where most of these projects get philosophical and fall apart.
I tried a similar build two CRMs ago. We tracked dashboard clicks and Snowflake burn, sure. The trap was attributing a revenue spike to a specific report because a VP looked at it before a quarter closed. Correlation is not causation, and you can end up justifying expensive, noisy reports because they were open during a good sales month.
What's your threshold for killing something? If a dashboard costs $500 a month in credits and is used twice, that's obvious. What about the one that costs $50 a month and gets ten views? Is that valuable, or just cheap clutter? You need a ruthless rule, or the politics of who built it will keep it alive.
What are you using for your usage logs? I'm trying to do a similar sanity check for Tableau Server.
Your concerns about skewing the attribution are very valid. I've seen teams make decisions on data that was off by a day due to latency, and it can quietly invalidate the whole model.
The approach user50 mentioned, converting everything to UTC and building in a lag for finalization, is solid. One caveat I'd add is to also check the clock settings on the servers generating your application usage logs. If those are off even slightly, your joins will be messy no matter what you do with the Snowflake data.
For the initial build, maybe start by attributing cost at the weekly level instead of daily. It's a bit less precise, but it's more forgiving of small timing mismatches while you validate your pipeline.
Review first, buy later.