System Design Estimation Calculator

Back-of-envelope calculations for system design interviews. Pick a scenario, adjust parameters, get instant estimates.

Worked Example: Design Twitter/X

Estimate infrastructure for a Twitter/X-like service with 600M total users and 250M DAU (2026 scale).

1Assumptions: 250M DAU, 5 tweets/user/day, 100:1 read:write ratio, 280 bytes/tweet
2Total QPS = 250M × 5 / 86,400 ≈ 14,468 QPS
3Write QPS = 14,468 / (1 + 100) ≈ 143 QPS
4Read QPS = 143 × 100 ≈ 14,325 QPS
5Peak QPS = 14,468 × 3 ≈ 43,403 QPS
6Daily Storage = 143 × 280 × 86,400 ≈ 3.47 GB/day
7Ingress = 143 × 280 ≈ 40.0 KB/s
8Egress = 14,325 × 280 ≈ 3.91 MB/s
9Cache (80/20) = 14,325 × 86,400 × 280 × 0.2 ≈ 69.3 GB
10Servers = ceil(43,403 / 10,000) = 5 servers at peak

Result

~14.5K QPS, ~3.47 GB/day storage, ~3.91 MB/s egress, ~69 GB cache, 5 servers

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Want to go deeper? Read about database sharding, cascade failures, or see all the estimation formulas.

Frequently Asked Questions

Why divide by 86,400 for QPS?

There are 86,400 seconds in a day (24 × 60 × 60). Dividing daily operations by 86,400 gives average operations per second, assuming uniform distribution.

What is the 80/20 rule for caching?

The Pareto principle: 20% of data accounts for 80% of requests. Caching the top 20% of daily data in memory can serve most read traffic without hitting the database.

What peak multiplier should I use?

Use 2x for steady workloads, 3x for social media with viral spikes, 5-10x for event-driven systems (ticket sales, flash sales, live sports).

How accurate do these estimates need to be?

In interviews, order of magnitude is sufficient. The difference between 10K and 100K QPS matters; 10K vs 12K does not. Focus on reasoning, not exact numbers.

What is a reasonable QPS per server?

Web servers: 5K-20K QPS. Database servers: 1K-10K QPS. Cache servers (Redis): 50K-100K QPS. API gateways: 10K-50K QPS. It depends on payload size and processing complexity.

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Worked Example: Design Twitter/X

Estimate infrastructure for a Twitter/X-like service with 600M total users and 250M DAU (2026 scale).

1Assumptions: 250M DAU, 5 tweets/user/day, 100:1 read:write ratio, 280 bytes/tweet

2Total QPS = 250M × 5 / 86,400 ≈ 14,468 QPS

3Write QPS = 14,468 / (1 + 100) ≈ 143 QPS

4Read QPS = 143 × 100 ≈ 14,325 QPS

5Peak QPS = 14,468 × 3 ≈ 43,403 QPS

6Daily Storage = 143 × 280 × 86,400 ≈ 3.47 GB/day

7Ingress = 143 × 280 ≈ 40.0 KB/s

8Egress = 14,325 × 280 ≈ 3.91 MB/s

9Cache (80/20) = 14,325 × 86,400 × 280 × 0.2 ≈ 69.3 GB

10Servers = ceil(43,403 / 10,000) = 5 servers at peak