A16Z Speedrun: Make ARR Useful Again. There’s a lot of aggressiveness in calculating ARR numbers in the current market.
A16Z writes about how to approach calculating MRR (monthly recurring revenue) and ARR (annual recurring revenue), the most common ARR sins and difference between annual recurring revenue and annual run rate.
On building up ARR from MRR
“MRR, by contrast, is a management metric. This is a point-in-time monthly value of recurring revenue you expect to keep happening. It’s tempting to say “ARR = 12 × MRR” and stop there. But that identity is only useful if “recurring” really means recurring—meaning you exclude one-offs (implementation, training, hardware) and you’re disciplined about usage. The clean approach is simple to state and powerful in practice. You should treat subscription and contracted minimums as MRR. Truly variable usage is its own line item and only when it proves stable or becomes contractually committed you can graduate it into MRR and ARR.”
MRR and ARR specifically for AI companies
“AI inference takes every MRR bad habit and amplifies them. Demand is often bursty (launch weeks, seasonal spikes), units are tiny (per 1,000 tokens, per request), and capacity can be either provisioned or opportunistic. That means you need an MRR policy that respects how the infrastructure is priced.
A founder-practical way to handle this without twelve sub-metrics is to:
Keep MRR for the part of your AI product that is contracted with items like fixed subscription fees, platform access, support tiers, and committed throughput minimums. If a customer signs for a model unit per month or a token floor, that is MRR.
Track Measured Usage separately for everything else: pay-as-you-go tokens, burst capacity, overages. Report it every month with a trailing average. If usage stabilizes for, say, six months, you can promote it into MRR with a straight face.