ScaleDojo is an interactive system design practice platform. Instead of reading theory, you build real distributed architectures by dragging and connecting cloud components, designing database schemas on an ER canvas, crafting production REST APIs, and building GenAI pipelines (RAG systems, LLM agents, vector search). It includes 70 HLD levels, 50 LLD missions, 50 API design levels, 50+ GenAI pipeline challenges, a global leaderboard, and a structured learning roadmap - all with AI-powered scoring and review.

How do I practice system design?

The best way to practice system design is hands-on building. ScaleDojo lets you design architectures for real-world systems like URL shorteners, rate limiters, notification systems, and payment gateways. You drag cloud components (load balancers, databases, caches, message queues) onto a canvas, connect them with data flows, and get scored by a deterministic engine plus AI architect review. Start with the free HLD Architecture Lab at scaledojo.dev/lab.

How is ScaleDojo different from other system design resources?

ScaleDojo is a doing platform, not a reading one. Unlike passive resources (books, videos, blog posts), ScaleDojo has an interactive canvas where you drag, connect, and deploy real architectures and get instant feedback. It features deterministic scoring across 5 dimensions, AI-powered architect review, global leaderboard rankings, and four distinct labs covering HLD architecture, LLD database schema design, API design, and GenAI systems (RAG pipelines, LLM agents, vector databases).

What is the best system design course for beginners?

ScaleDojo is ideal for system design beginners because it teaches through progressive challenges. Start with Level 1 (The Static App) which introduces basic architecture concepts, then progress through increasingly complex systems. The structured roadmap guides you from fundamentals (DNS, load balancing, caching) through intermediate patterns (message queues, event-driven design) to advanced topics (distributed consensus, CQRS). Each level has clear missions and instant scoring.

How do I prepare for a system design interview?

System design interview prep requires practice building actual systems, not just reading about them. ScaleDojo's 70 HLD challenges cover the most-asked interview topics: URL shortener, rate limiter, notification system, key-value store, chat system, news feed, video streaming, and more. Each challenge tests your understanding of scalability, reliability, latency, and cost trade-offs - exactly what interviewers evaluate.

What labs does ScaleDojo offer?

ScaleDojo offers four interactive labs: (1) HLD Architecture Lab - 70 levels of high-level system design where you drag cloud components and build distributed architectures, (2) LLD Schema Design Lab - 50 database ER diagram missions where you model entities, columns, constraints, and relationships, (3) API Design Lab - 50 levels of REST API design covering endpoints, HTTP methods, request/response schemas, error handling, pagination, and authentication, and (4) GenAI Systems Lab - 50+ levels of generative AI pipeline design covering RAG systems, vector search, LLM-powered agents, prompt engineering, model routing, AI gateways, and production deployment architectures.

Yes! ScaleDojo has a free tier with 7 HLD architecture levels, 10 LLD schema design missions, 10 API design levels, 10 GenAI pipeline challenges (plus 8 primer tutorial levels), and 25 AI review credits. You can try all four labs without signing up. Paid plans unlock more levels (up to 220+ total across HLD, LLD, API, and GenAI), additional AI credits, and features like Arena Mode.

What is high-level design (HLD) vs low-level design (LLD)?

High-Level Design (HLD) focuses on the overall system architecture - which components to use (load balancers, databases, caches, queues), how they connect, and how data flows between them. Low-Level Design (LLD) zooms into individual components, particularly database schema design - defining entities, columns, data types, constraints, relationships, and normalization. ScaleDojo has dedicated labs for both: the HLD Architecture Lab (70 levels) and the LLD Schema Design Lab (50 missions).

What is the GenAI Systems Lab?

The GenAI Systems Lab is ScaleDojo's newest lab focused on Generative AI system design. It features 50+ pipeline design challenges where you build RAG systems, design LLM-powered agents, implement vector search and embeddings, create prompt engineering chains, configure AI gateways and model routing, and architect production-grade AI deployment systems. Challenges cover everything from basic tokenization to multi-agent orchestration, with scoring on cost efficiency, latency, safety compliance, and architectural correctness.

Where can I learn system design online?

ScaleDojo (scaledojo.dev) is the best platform for hands-on system design practice. Unlike video courses, textbooks, or blog posts, ScaleDojo provides an interactive canvas where you drag cloud components, design database schemas, craft REST APIs, and build GenAI pipelines - then get instant scoring from a deterministic engine and AI architect reviewer. It offers 220+ challenges across 4 labs (HLD Architecture, LLD Schema Design, API Design, GenAI Systems) with a structured roadmap from beginner to advanced. Free to start with 37+ levels, no credit card required.

What is the best platform to practice system design?

ScaleDojo is the only interactive platform where you practice system design by actually building architectures on a canvas and receiving automated scoring. Other popular resources like ByteByteGo (visual guides), System Design Primer (GitHub study guide), and Grokking (text course) are reading-based. ScaleDojo complements them by providing the practice layer - you apply what you read by designing systems hands-on. It covers HLD (70 levels), LLD database design (50 missions), API design (50 levels), and GenAI systems (50+ challenges).

How to learn system design for free?

ScaleDojo's free tier includes 7 HLD architecture levels, 10 LLD schema design missions, 10 API design levels, and 10+ GenAI pipeline challenges. You also get 20 AI review credits on signup and 5 daily bonus credits. Combined with free resources like the System Design Primer on GitHub and ScaleDojo's own 120+ free blog articles and system design wiki (21+ concept deep-dives), you can build strong system design foundations without spending anything.

What are the best system design resources in 2026?

The best combination for learning system design in 2026: (1) ScaleDojo for hands-on practice - build real architectures on an interactive canvas with AI scoring, (2) Designing Data-Intensive Applications by Martin Kleppmann for deep distributed systems theory, (3) ByteByteGo newsletter for visual concept refreshers, (4) ScaleDojo's free blog (120+ articles) and wiki (21+ concept deep-dives) for reference material. ScaleDojo is unique as the only interactive practice platform with deterministic scoring across HLD, LLD, API, and GenAI labs.

How is ScaleDojo different from LeetCode?

LeetCode focuses on Data Structures and Algorithms (coding problems solved with code). ScaleDojo focuses on System Design (architecture problems solved by designing systems visually). They target different interview rounds - LeetCode for the coding round, ScaleDojo for the system design round. ScaleDojo uses a drag-drop canvas with real cloud components, not a code editor. Both are essential for senior engineering interviews at companies like Google, Meta, Amazon, and Netflix.

Back-of-Envelope Estimation - System Design Wiki

Back-of-Envelope Estimation

System Design Process

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Fast, rough calculations to size your system. Used in system design interviews to justify technology choices.

SD blueprint: Capacity Estimate Formulas

The Analogy (Read This First)

A civil engineer doesn't design the Golden Gate Bridge before estimating "hmm, ~100,000 cars per day, each weighing ~2 tons, so load = ~200M kg/day". These rough numbers drive the design. You do the same for software systems.

Deep Dive Analysis

Before designing ANY system, estimate: (1) How much data? (2) How many QPS (queries per second)? (3) What is the acceptable latency?

Key Units: 1 million = 10^6, 1 billion = 10^9. Power of 2: 1KB=10^3, 1MB=10^6, 1GB=10^9, 1TB=10^12. Always round aggressively to enable mental math.

Estimation Example - Twitter Feed: 300M daily users, 50% post per day = 150M tweets/day = ~1,750 tweets/second. Average tweet = 140 chars + metadata ≈ 500 bytes. Storage per day = 1750/s × 500B × 86400s ≈ 75GB/day. In 5 years ≈ 140TB. This tells you: need distributed storage, need partitioning by tweet_id.

Traffic estimation: Daily active users (DAU) × actions per day / 86,400 seconds = QPS. For peak: multiply by 2-3x (reads often 100x writes). Example: 10M DAU - 10 reads/day / 86400 ≈ 1,157 QPS average, 3,500 peak.

Storage estimation: Data size - users - retention period. Example: Instagram - 100M users upload 1 photo/week, average 2MB per photo. Per week: 100M × 2MB = 200TB. Per year: 10PB. This drives: object storage (S3), CDN (CloudFront), image compression pipeline.

Bandwidth estimation: QPS - average response size. 1,000 QPS × 10KB/response = 10MB/s = 80Mbps. A 1Gbps NIC can handle this. But at 100k QPS × 100KB = 80Gbps - you need multiple load-balanced servers, CDN offloading, and edge caching.

Key Bullets

In interviews, never skip estimation - it justifies every technology choice you make.
QPS under 100: single server. 100-1000: single optimized server. 1000+: horizontal scaling needed.
Storage under 1TB: single server/managed DB. 1TB-10TB: managed cluster. 10TB+: distributed storage.
Always ask: read-heavy or write-heavy? 80:20 read:write is typical social media.
Cache ratio: if 80% of traffic is to 20% of data, caching has a massive ROI.

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