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Google·Product Manager·Onsite - Product Sense / Strategy·Senior

Senior
May 2026

Summary

Google product management interview with a classic estimation question about YouTube storage costs. Nothing too surprising but it requires you to actually know how to structure a back-of-envelope calculation under pressure.

Questions Asked (1)

Q1

Estimate the incremental cost of storing all videos uploaded to YouTube over the course of a single year.

Product Analytics & MetricsSystem DesignTechnical Trade-offs
Author's notes

I started by estimating daily upload volume, which felt okay, but then I got tangled up trying to figure out storage cost per GB and whether to account for multiple resolution copies.

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AI HintsAI Generated

Suggested Approach

Break the problem into estimating total video upload volume (hours) and then converting that to storage cost using assumptions about resolution, codec, and storage tier. Structure your answer with clear assumptions, a step-by-step calculation, and a final cost estimate, while noting key sensitivities.

Pro tip: Anchor your estimate with a known metric (e.g., 500 hours of video uploaded per minute) and state that you'd validate it with internal data; this shows you can leverage public benchmarks and think like a Googler.

1. Estimate total upload volume

Calculate total hours of video uploaded in a year using a known rate (e.g., 500 hours/minute) and convert to seconds or bytes.

2. Determine average video size

Assume an average resolution and bitrate (e.g., 1080p at 5 Mbps) to compute storage per hour of video.

3. Compute raw storage needed

Multiply total hours by storage per hour to get total petabytes or exabytes for the year.

4. Apply storage cost per GB

Use a blended cost per GB per month for Google's storage infrastructure (e.g., $0.02/GB/month) and multiply by total GB and 12 months.

5. Sanity check and summarize

Compare your estimate to known data (e.g., total YouTube storage) and present the final incremental cost with key assumptions.

Key Points to Mention

  • Use of known public metrics (e.g., 500 hours uploaded per minute) as a starting point.
  • Assumptions about average video resolution, bitrate, and codec efficiency.
  • Storage cost per GB for large-scale infrastructure (e.g., Google Cloud Storage rates).
  • Consideration of replication and redundancy in storage costs.
  • Differentiation between raw storage and incremental cost (e.g., existing infrastructure vs. new).
  • Sensitivity analysis: how cost changes with different assumptions.

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.