Estimate infrastructure or operational capacity needed for Guest App at global scale

Problem Statement Description

Product context: Airbnb is a travel marketplace connecting guests and hosts; its products include stays, experiences, host tools, guest booking, trust and safety, payments, and reviews.

Airbnb’s Guest App supports travelers around the world as they search, compare, book, manage, and get support for stays and experiences. For this guesstimate, estimate the infrastructure or operational capacity Airbnb would need to reliably serve the Guest App at global scale, with particular attention to family travelers who may have more complex search criteria, longer planning cycles, group decision-making, and higher support expectations.

Think of “capacity” broadly, but keep your scope explicit. You may choose to estimate technical infrastructure such as app/API traffic, search queries, booking transactions, messaging volume, payments load, image/content delivery, or customer-support operations such as contacts, dispute handling, and safety escalations. The expectation is not a perfect number, but a structured, defensible sizing model with clear assumptions.

Your estimate should account for Airbnb’s marketplace context: global demand, seasonality, weekend and holiday spikes, different travel regions, mobile-heavy usage, trust and safety needs, and the dependency between guest demand and host supply quality. You should also be clear about the unit you are estimating and the time horizon, such as peak requests per second, daily active users, monthly bookings, support tickets per day, agent staffing, storage/bandwidth, or regional operational coverage.

The experience should consider:

- The precise scope and unit of capacity being estimated, including what is included and excluded.

- The target population, such as global travelers, Airbnb guests, families, active app users, or booking-intent users.

- Adoption and usage frequency assumptions across browsing, search, listing views, wishlists, booking, messaging, payments, trip management, and support.

- Peak-to-average ratios driven by seasonality, holidays, weekends, regional time zones, and major travel events.

- Family-specific behaviors such as larger group size, longer stays, more filters, higher comparison activity, accessibility or safety checks, and increased pre-trip communication.

- Infrastructure or operational drivers such as API calls, search/index load, image delivery, payment attempts, host-guest messages, customer-service contacts, and escalation rates.

- Sensitivity analysis showing which assumptions most affect the estimate and how the answer changes under conservative versus aggressive scenarios.

- Sanity checks against marketplace scale, competitor benchmarks, travel industry norms, or internal consistency between searches, bookings, trips, and support volume.

The goal is to produce a clear, logical estimate that a product or infrastructure team could use for planning conversations, capacity buffers, staffing decisions, or investment prioritization without requiring exact internal Airbnb data.