scale-benchmarks
Contains references on how to measure system performance. Useful for ensuring systems can handle many tasks.
What it does
This tool helps estimate the capacity needed for an application, such as how many users it can handle and how much storage and network traffic it will require. It provides formulas and examples for different audience sizes.
When to use it
- ✓When planning the infrastructure for a new application.
- ✓When estimating hosting costs for a website or service.
- ✓When scaling an application to handle more users.
Example of what to ask your AI
“How much traffic and storage would an app with 100,000 daily active users likely need?”
Want more? Open the sections below.
How to use
The easiest way: copy the message below and paste it into Claude, ChatGPT, Cursor or any AI. It reads the skill and follows the instructions.
Skill link
Ready-to-paste message
How it works
The tool uses formulas based on user numbers, data size, and usage frequency to calculate metrics like requests per second, disk space, and bandwidth. It also compares common technologies.
Installation
Easiest way: copy the skill link and paste it into Claude, ChatGPT or Cursor asking it to follow the instructions.
In the terminal (Claude Code, Codex, Cursor), in your project folder:
npx skills add sickn33/agentic-awesome-skills --skill scale-benchmarksOr download just the file:
curl -L https://raw.githubusercontent.com/sickn33/agentic-awesome-skills/main/plugins/agentic-awesome-skills-claude/skills/monopoly/scale-benchmarks/SKILL.md -o SKILL.mdTechnical content (original SKILL.md)
Original text by the author, straight from GitHub.
MONOPOLY — Scale Benchmarks & Estimation Formulas
When to Use
- Use this skill when the task matches this description: Reference document for monopoly scale-benchmarks.
Quick Estimation Formulas
User → RPS Conversion
Requests per second (avg) = DAU × avg_requests_per_user_per_day / 86400
Requests per second (peak) = avg_RPS × peak_multiplier
Peak multipliers by app type:
Social media: 5–10×
E-commerce: 3–5× (higher during sales)
News / media: 10–20× (breaking news spike)
B2B SaaS: 2–3× (business hours spike)
Gaming: 5–15× (event-driven)
Storage Estimation
Storage per day = requests_per_day × avg_payload_size
Storage per year = storage_per_day × 365
With replication = storage_per_year × replication_factor (3× typical)
With CDN/cache = reduce by cache_hit_ratio (80% hit = 20% origin load)
Common payload sizes:
Tweet / short text: 500B
Social post with text: 2KB
Profile data: 5KB
Image (compressed): 200KB–2MB
Video (per minute): 50MB (720p), 150MB (1080p)
API JSON response: 1–20KB
Bandwidth Estimation
Inbound bandwidth = avg_request_size × RPS
Outbound bandwidth = avg_response_size × RPS
Convert: 1 Gbps = 125 MB/s
10 Gbps = 1.25 GB/s
Known Scale Limits of Common Technologies
Databases
| Technology | Single Node Writes | Reads (with replicas) | Recommended Shard/Cluster Trigger |
|---|---|---|---|
| PostgreSQL | ~5K–20K writes/s | ~50K–200K reads/s | >5TB data or >20K writes/s |
| MySQL | ~10K–25K writes/s | ~60K–250K reads/s | >5TB or >25K writes/s |
| MongoDB | ~20K–50K writes/s | ~50K–100K reads/s | >100GB or >50K writes/s |
| Cassandra | ~200K–1M writes/s | ~200K–500K reads/s | Almost never needs explicit sharding |
| DynamoDB | Unlimited (managed) | Unlimited (managed) | Use provisioned capacity mode |
| Redis | ~500K–1M ops/s | Same | >50GB data or cluster needed |
| Elasticsearch | ~10K–50K docs/s | ~1K–10K queries/s | >100M documents per index |
Queues / Streams
| Technology | Max Throughput | Max Consumers | Retention |
|---|---|---|---|
| Kafka | 1M+ msgs/s per cluster | Unlimited consumer groups | Configurable (days–forever) |
| RabbitMQ | ~50K–100K msgs/s | Limited by connections | Until consumed |
| SQS Standard | Unlimited (AWS-managed) | Unlimited | 14 days |
| SQS FIFO | 3K msgs/s per queue | Per group | 14 days |
| Redis Pub/Sub | ~1M msgs/s | Limited by subscribers | None (fire-and-forget) |
Caching
| Technology | Max Memory (single) | Max Throughput | Latency |
|---|---|---|---|
| Redis | ~1TB RAM | ~1M ops/s | <1ms |
| Memcached | ~64GB RAM | ~1M ops/s | <1ms |
| In-process (Caffeine/Guava) | JVM heap | Unlimited (local) | <0.1ms |
Capacity Planning by User Scale
1K DAU
Avg RPS: ~1–5 RPS
Peak RPS: ~10–50 RPS
DB size/year: ~10–50GB
Infra needed: Single server, managed DB (RDS t3.medium), basic CDN
Monthly cost: $50–200
10K DAU
Avg RPS: ~10–50 RPS
Peak RPS: ~100–500 RPS
DB size/year: ~100–500GB
Infra needed: 2–4 app servers, RDS r5.large, Redis t3.medium, CDN
Monthly cost: $300–800
100K DAU
Avg RPS: ~100–500 RPS
Peak RPS: ~1K–5K RPS
DB size/year: ~1–5TB
Infra needed: ASG (5–10 app servers), RDS r5.xlarge + 2 replicas, Redis cluster, CDN, ALB
Monthly cost: $2K–8K
1M DAU
Avg RPS: ~1K–5K RPS
Peak RPS: ~10K–50K RPS
DB size/year: ~10–50TB
Infra needed: ASG (20–50 servers), DB sharding or Aurora, Redis cluster, Kafka, CDN, WAF
Monthly cost: $20K–80K
10M DAU
Avg RPS: ~10K–50K RPS
Peak RPS: ~100K–500K RPS
DB size/year: ~100–500TB
Infra needed: Multi-region, microservices, distributed DB (Cassandra/CockroachDB), full CDN, dedicated SRE
Monthly cost: $200K–2M+
Common SLO Targets
| Tier | Availability | Monthly Downtime Allowed |
|---|---|---|
| 99% | Basic | 7.2 hours/month |
| 99.9% (three nines) | Standard production | 43.8 minutes/month |
| 99.95% | Important services | 21.9 minutes/month |
| 99.99% (four nines) | Critical services | 4.38 minutes/month |
| 99.999% (five nines) | Telecom / payments | 26 seconds/month |
…
FAQ
What do 'DAU' and 'RPS' mean?
'DAU' stands for 'Daily Active Users', and 'RPS' stands for 'Requests Per Second'.
How does payload size affect storage?
Larger payloads mean each request uses more space, rapidly increasing storage needs over time, especially with many users.
What does '99.9% availability' mean?
It means the service should be up and running 99.9% of the time, allowing for about 43 minutes of downtime per month.
Details
Original description by the author
Reference document for monopoly scale-benchmarks.
Popularity
- GitHub stars
- 48k
- Ranking position
- #3620
- License
- MIT
- Updated
- 10/15/2026
Comments
- …
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