How do I estimate whether aDAU will materially reduce identity costs?

Estimate your average daily authenticated users from production logs, then compare that figure to the monthly unique user count. If the same users return across many days or your traffic is highly bursty, aDAU will usually narrow the gap between billing and actual usage. Use your own DAU/MAU ratio rather than a generic benchmark.

Why This Matters for Security Teams

Estimating whether aDAU will materially reduce identity costs is not a finance exercise alone. It is a usage-shape question: how many distinct users actually authenticate in a day, how often they return, and whether monthly licensing is being inflated by infrequent but recurring access. For NHI-heavy environments, the same logic applies to service accounts and automation, where identity volume can dwarf human usage and distort cost models. NHI Mgmt Group notes that NHIs outnumber human identities by 25x to 50x in modern enterprises in the Ultimate Guide to NHIs.

The cost mistake many teams make is comparing aDAU to a vendor’s headline billing metric without first testing whether their access pattern is bursty, repetitive, or session-heavy. If the same people or workloads return across many days, aDAU can close the gap between paid capacity and real demand. If usage is broad but shallow, the savings may be marginal. The right question is not whether daily active identities are smaller than monthly uniques, but whether that difference is large enough to justify a pricing change against operational overhead and governance friction. Security teams also need to consider how identity sprawl affects visibility and control, a theme reinforced in NHI Mgmt Group’s Top 10 NHI Issues. In practice, many teams discover the cost gap only after renewal forecasts are already locked and the usage pattern has been poorly measured.

How It Works in Practice

Start with production logs, not procurement assumptions. Count authenticated users per day over a representative period, then compare that count to monthly unique users and to the average number of active days per user. If the DAU-to-MAU ratio is low, users are infrequent and aDAU may create savings. If the ratio is high, recurring usage already approximates monthly access, so the billing model may not change much.

For a meaningful estimate, practitioners usually test three things:

• How many distinct identities authenticate on a typical day.
• How concentrated usage is during peaks, launches, batch jobs, or customer support cycles.
• How often the same identity returns across the month, because repeated reuse weakens the case for a lower daily-based price.

Use this same discipline for NHIs and automation accounts. A service account that runs every day may justify a different cost model than one used once per month, but it also demands tighter governance because static credentials and long-lived tokens carry risk. That is why NIST’s NIST SP 800-63 Digital Identity Guidelines and NIST SP 800-53 Rev 5 Security and Privacy Controls remain useful reference points for identity assurance and access control hygiene, even when the pricing question is commercial.

If your environment has strong observability, compare active identities by application, region, and business unit rather than using enterprise-wide averages. That avoids overestimating savings in one business line to subsidize another. These controls tend to break down in environments with shared kiosks, federated tenants, or heavily cached sessions because authentication events no longer map cleanly to unique active users.

Common Variations and Edge Cases

Tighter measurement often increases reporting overhead, requiring organisations to balance pricing accuracy against data quality and analyst effort. That tradeoff matters because aDAU can look attractive on paper while offering little practical benefit in environments with irregular access patterns or mixed human and machine usage.

The biggest edge case is blended identity estates. If employees, contractors, and automated workloads share the same licensing domain, the DAU estimate can be misleading unless machine identities are separated from human identity metrics. Another common exception is seasonal demand: aDAU may understate the peak cost of onboarding, support, and access provisioning even if it lowers the monthly bill.

There is no universal standard for how much DAU/MAU spread is “enough” to justify switching pricing models. Current guidance suggests using your own production ratio, not a market benchmark, because repeat logins, session duration, and identity sharing patterns vary too much by environment. Also, if your identity stack is already burdened by poor visibility or excessive privileges, savings from aDAU may be offset by remediation work. NHI Mgmt Group’s Ultimate Guide to NHIs and 52 NHI Breaches Analysis show why identity cost decisions often surface alongside broader access risk, not in isolation.

Related resources from NHI Mgmt Group

• When does a machine identity become a compliance problem?
• Why is it important to integrate identity and data governance?
• When does secret exposure become a broader identity risk?
• What is the difference between patching a vulnerability and reducing identity blast radius?