Most organizations don't have a clear picture of what their BI software actually costs on a per-active-user basis. They know the contract value — the annual fee they signed — but they don't know how that maps to the people who are actually using the tool each month. Before you can evaluate whether you're overpaying, or make a meaningful comparison to alternatives, you need to audit your actual usage. Here's how to do it.
Step 1: Pull Your Current Provisioned User Count
Start with the raw numbers from your current BI tool's admin panel. You want to know: total accounts in the system, broken down by license tier if applicable (for role-based tools like Tableau). This is your "provisioned" number — the users you're currently paying for.
For most admin consoles, this is a straightforward export. Look for user management, license management, or admin settings — most tools surface this at the top level. Note how many are active vs. inactive or disabled accounts — some organizations accumulate provisioned accounts for departed employees that are still being billed.
Step 2: Pull Login Activity for the Past 90 Days
This is the number that matters. You want to know: how many distinct users actually logged in during each of the past three months? Three months gives you a representative sample that smooths out one-off anomalies (a big quarterly review that inflated one month, or a slow holiday month).
Most BI tools expose this in an audit log, usage report, or admin analytics section. The specific path varies by tool — in Power BI, it's the Admin portal under Usage metrics; in Tableau, it's the Site status or Views by user report. If you can't find it directly, your IT team or BI admin should be able to pull a login count from the access logs.
What you're building: a table that shows, for each of the past three months, how many unique users logged in at least once. Average those three months to get your working active user number.
Step 3: Segment by Login Frequency
Not all active users are the same. Break your user list into three groups based on login frequency over the past 90 days:
Regular users: Logged in at least 8–10 times across the 90-day window (roughly weekly or more). These are your core users — analysts, managers who use the tool as part of their regular workflow.
Periodic users: Logged in 2–7 times over 90 days. Monthly or bi-monthly usage. These users have a genuine need for the tool but aren't daily users.
Occasional users: Logged in once or not at all in 90 days. Likely checking in for a specific purpose, or effectively idle.
This segmentation matters because it tells you the realistic range of your monthly active user count — your regular users will be active nearly every month, your periodic users most months, your occasional users rarely.
Once you have your active user count, it's straightforward to run the DashboardFox pricing math against what you're currently paying. The numbers are published — no sales call required.
Step 4: Calculate Your Cost Per Active User
Now you have what you need to calculate the real cost of your current setup. Take your annual BI spend and divide it by your average monthly active user count, then multiply by 12 to get an annualized cost per active user.
Formula: Annual BI cost ÷ average monthly active users = annual cost per active user
Example: You're paying $12,000/year for Power BI Pro on 72 provisioned seats. Your audit shows an average of 28 users logging in each month. Your cost per active user is $12,000 ÷ 28 = $428/year per active user, or about $36/month per active user. That's more than double the $14 sticker price.
This calculation can be illuminating. The sticker price per seat often looks reasonable; the cost per person who actually uses the tool regularly is frequently much higher.
Step 5: Map Your Active User Count to Alternative Pricing Models
With your average monthly active user count established, you can now run cost comparisons against other models accurately. Take your average active user number — let's call it 28 in our example above — and plug it into the pricing structures of tools you're evaluating:
Power BI Pro (per-seat, all provisioned users): 72 × $14 = $1,008/month — $12,096/year
Power BI Pro (per-seat, just active users): 28 × $14 = $392/month — but this isn't actually available; you pay for all provisioned accounts.
DashboardFox Growth (MAU, covers 30 MAU): $249/month — $2,988/year. All 72 accounts exist; you only pay for the 28 who actually log in, which fits within the 30 MAU cap.
The $2,988 vs $12,096 comparison is dramatic, but it reflects real math for a team with this usage pattern. The question is whether the feature sets are comparable for your specific use case — which is where the rest of your evaluation comes in.
Step 6: Factor In Gated Feature Costs
Per-seat pricing is only part of the total cost picture. Some features that may be critical to your deployment are gated behind higher tiers, and they add to the annual figure:
Row-level security — needed for any deployment where different users should see different data. In Power BI, basic RLS is included in Pro but complex enterprise RLS scenarios require Premium or Fabric. In Tableau, RLS requires the Enterprise tier. In Metabase, it requires the Pro tier at $575+/month.
White-label or custom domain — needed if you deliver reports to external clients or want branded dashboards. Power BI has no white-label at any tier. Tableau added a custom subdomain in 2025, but it's not full white-label. Klipfolio charges $299/month extra for white-label branding.
When you're calculating total cost, add these tier upgrades and add-ons to your base seat cost. A tool that looks cheaper per-seat may be more expensive once you account for the features you actually need.
Building a Comparison Spreadsheet
A simple framework for making this comparison concrete. Set up columns for each tool you're evaluating, and fill in: monthly base cost at your active user count, any feature add-ons required for your use case, annual commitment requirement (yes/no), and annual total. Add a row for "cost per active user/month" by dividing the annual total by your average MAU count × 12.
That last row is the most useful for apples-to-apples comparison. It normalizes for different pricing models and gives you a real number to compare. In the next chapter, we put together exactly this comparison for the major tools at several realistic user scales.
