Licensing models have lagged behind the realities of modern systems. As software shifts toward automation, APIs, and machine-driven actions, the rise of non-human identities—service accounts, bots, machine learning agents, CI/CD pipelines—forces a rethink. These entities are not tied to a single user, yet they consume resources, trigger workflows, and create revenue impact just like humans do. Traditional seat-based licenses cannot track value here.
A licensing model for non-human identities must adapt to different lifecycles and activity patterns. Machine accounts scale up or down instantly. They may act thousands of times per second or go dormant for weeks. They are often ephemeral, spun up in serverless environments or containers, and destroyed minutes later. Any effective licensing structure must account for this volatility while remaining predictable for budgeting and billing.
One option is activity-based licensing. Charge for API calls, executed jobs, or compute minutes. This connects value to measurable actions and works for short-lived, high-frequency identities. Another approach is a capacity model—billing for the maximum concurrent non-human sessions over a period—suited for stable, long-running services. Hybrid models blend these, using metered metrics for bursts and base capacity for constant processes.