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19/08/2026 3:37 pm
TL;DR: MAU billing can inflate identity infrastructure invoices by 5x to 7x on a single spike day, while aDAU better tracks authenticated usage and can reduce costs by 30% to 75% depending on traffic patterns, according to Ory. For IAM and platform teams, the issue is proportionality: pricing tied to monthly peaks can misrepresent actual identity load and complicate capacity, budgeting, and procurement decisions.
NHIMG editorial — based on content published by Ory: MAU vs. aDAU, comparing pricing models for identity infrastructure
By the numbers:
- Across Ory Network's customer base, the observed average is approximately 7.6%.
- Real-world usage patterns rarely produce consistent daily activity, and aDAU typically reduces costs by 30-75% depending on traffic pattern.
Questions worth separating out
Q: How should identity teams compare MAU and aDAU pricing models?
A: Start with actual usage behaviour, not vendor price cards.
Q: When does MAU billing become a poor fit for identity infrastructure?
A: MAU becomes a poor fit when a small number of spike days, event-driven logins, or one-off authentication surges dominate the month.
Q: How do I estimate whether aDAU will materially reduce identity costs?
A: Estimate your average daily authenticated users from production logs, then compare that figure to the monthly unique user count.
Practitioner guidance
- Benchmark your DAU/MAU ratio before choosing a billing model Use real authentication telemetry from production traffic, then test whether your workload sits closer to workday, spike, or cyclic behaviour.
- Model spike-day exposure separately from average load Run a month simulation with one or two abnormal traffic days so you can see how much a high-water mark inflates the invoice.
- Align procurement reviews to usage proportionality Ask vendors whether the billing unit reflects daily authenticated activity or monthly presence, and require both forecast scenarios in pricing discussions.
What's in the full article
Ory's full blog post covers the operational detail this post intentionally leaves for the source:
- The billing comparison calculator and slider examples that show how different traffic patterns change MAU and aDAU outcomes.
- The specific formula and assumptions used to estimate aDAU from MAU for your own application.
- The example patterns for launch-day spikes, work-week usage, and cyclic traffic that help teams benchmark their own environment.
- The pricing context for Ory Network workspace-based billing and how it affects subscription planning.
👉 Read Ory's comparison of MAU and aDAU pricing for identity infrastructure →
MAU vs aDAU pricing: what identity teams need to factor in?
Explore further
19/08/2026 4:17 pm
MAU is a billing proxy, not an operational identity measure. The article shows that monthly active user counts can overstate actual usage when authentication is uneven across the month. That makes MAU a poor stand-in for workload intensity in environments where identity traffic spikes around launches, events, or periodic workflows. For practitioners, the lesson is to separate presence from consumption when evaluating identity infrastructure economics.
A few things that frame the scale:
- 70% of organisations grant AI systems more access than they would give a human employee performing the exact same job, according to The 2026 Infrastructure Identity Survey.
- Only 13% of organisations feel extremely prepared for the reality of agentic AI, according to The 2026 Infrastructure Identity Survey.
A question worth separating out:
Q: What should teams ask before committing to usage-based identity billing?
A: Ask what is being measured, how spikes are treated, and whether pricing follows daily activity or monthly presence. You should also ask for examples from workloads similar to yours, because the right model for a work-week SaaS app may be a bad fit for consumer or event-based authentication patterns.
👉 Read our full editorial: MAU pricing distorts identity infrastructure costs on spike days
