Estimate infrastructure or operational capacity needed for Frame.io

Problem Statement Description

Product context: Adobe is a creative, document, and marketing software company; its products include Creative Cloud, Photoshop, Illustrator, Acrobat, Adobe Express, Firefly, and Experience Cloud.

Adobe wants to understand the infrastructure or operational capacity required to support Frame.io for marketing teams that use it to upload, review, approve, and distribute creative video assets. The estimate should focus on a realistic business use case: marketers collaborating with agencies, editors, designers, brand teams, and stakeholders across campaign workflows.

Frame.io usage can create demand across storage, upload/download bandwidth, video transcoding, preview streaming, comments/annotations, notifications, permissions, integrations, and customer support. Your task is not to produce an exact internal forecast, but to build a structured estimate that shows how demand scales with customer count, team size, asset volume, file size, collaboration frequency, and workflow intensity.

Frame the problem clearly before calculating: define which customer segment, geography, time horizon, usage unit, and capacity type you are estimating. You may choose one primary capacity metric, such as storage, monthly data transfer, concurrent review sessions, transcoding workload, or support staffing, and explain why that metric is useful for planning.

The experience should consider:

- The target user population, such as enterprise marketing teams, agencies, or mid-market creative teams using Frame.io.

- The unit of estimation, for example per customer, per active team, per campaign, per month, or at peak daily usage.

- Adoption assumptions, including number of customers, active users per account, and share of users who upload versus only review.

- Workflow frequency, such as campaigns per month, videos per campaign, review rounds, and stakeholder interactions.

- Asset assumptions, including average file size, video length, resolution, versions, and retention period.

- Capacity drivers such as storage growth, upload/download bandwidth, preview streaming, transcoding jobs, notifications, and operational support.

- Sensitivity checks for high-usage accounts, seasonal campaign spikes, global collaboration, and enterprise security/compliance overhead.

- Sanity checks against real-world marketing workflows and whether the final estimate feels operationally plausible.

Goal: Produce a transparent, defensible guesstimate for the infrastructure or operational capacity Frame.io would need to serve marketers at scale, while making assumptions explicit and identifying the variables that most affect the estimate.