A single spike day can inflate your MAU count by 5-7x. Learn how aDAU pricing works, estimate the difference for your application, and compare costs across billing models.
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Most identity providers bill by Monthly Active Users (MAU). How a provider counts "active" can shift your invoice by an order of magnitude. This post compares MAU and average Daily Active Users (aDAU) and provides a method for estimating the difference.
MAU counts every unique user who authenticates at least once in a billing month. A user who logs in once and a user who logs in daily both count as 1 MAU.
This creates two problems. First, spike sensitivity: a single high-traffic day inflates the MAU count for the entire period. Second, cost misalignment: identity infrastructure costs scale with request volume, not with the number of occasionally-active users. A user who authenticates twice a month generates a fraction of the compute load of a daily user, yet both carry the same billing weight.
aDAU counts unique authenticated users per day, then averages over the billing period:
aDAU = (sum of daily active users across all days) / (days in billing period)
A daily user contributes 1.0 to the aDAU. A once-per-month user contributes approximately 0.03.
An application with 1,000 daily active users experiences a one-day spike to 5,000 users during a promotion.
aDAU: (1,000 x 29 + 5,000 x 1) / 30 = ~1,133
MAU: at least 5,000 from the spike day alone, potentially 5,000-7,500 total depending on user overlap across the month.
Identical infrastructure load. The MAU-based invoice runs 5-7x higher.
Industry data places most internet applications between 6% and 20% DAU/MAU, meaning the average user authenticates 2-6 days per month. Across Ory Network's customer base, the observed average is approximately 7.6%. At that ratio, MAU exceeds aDAU by more than 10x.
Estimated aDAU = MAU x (DAU/MAU ratio)
Reference ratios: SaaS tools with workday usage (15-20%), consumer apps with moderate engagement (8-15%), low-frequency apps like tax or compliance software (3-8%). Without specific data, 10% is a reasonable default for B2B SaaS; 7-8% for consumer applications.
Variable traffic: aDAU absorbs spikes without penalizing the full billing period. Low-frequency authentication: periodic-use applications see the largest cost reduction versus MAU. Infrastructure alignment: aDAU tracks closer to actual compute consumed, producing a more proportional relationship between price and resource utilization.
Use the slider below to set your base daily user count and see how three common traffic patterns affect MAU vs. aDAU billing.
Product launch or viral moment causes a 5x spike on day 10
The spike brings 3,883 new users who mostly do not return. MAU jumps to 5,183, but aDAU stays at 1,177 (77% difference).
Business apps see 50% fewer users on weekends
High user overlap means MAU (1,300) stays closer to peak DAU. Weekend drops still reduce aDAU to 864 (34% savings).
Sports and entertainment apps spike on event days
Different users show up on different event days, inflating MAU to 2,994. Daily average is 793 (74% savings with aDAU).
Billing metrics across all three patterns
Real-world usage patterns rarely produce consistent daily activity. MAU-based billing charges for peak monthly presence, while aDAU tracks actual daily usage, typically reducing costs by 30-75% depending on traffic pattern.
Data based on 1,000 base daily users · Unique user simulation with pattern-specific overlap
Ory Network bills all plans on aDAU, so costs track authenticated usage rather than peak monthly presence. Combined with workspace-based billing that consolidates production, staging, and development environments under a single subscription, the model keeps identity costs predictable as traffic scales.
For high-engagement applications with DAU/MAU ratios above 30%, the gap between metrics narrows. The structural advantage is proportionality: the bill reflects daily usage, not a high-water mark from one anomalous day.
For plan details and pricing, see ory.com/pricing.