Is this a video course?

No. This is an interactive, slide-based learning platform. Each lesson has rich text, animated diagrams, live code editors, and quizzes. You learn by reading, interacting, and doing, not by watching videos passively.

How long do I have access?

Forever. Both pricing tiers are one-time payments with lifetime access. This includes all current 766 lessons and any future content we add.

What level of experience do I need?

None. We start from absolute basics like 'What is latency?' and build up to distributed consensus protocols. The Foundation level assumes zero prior knowledge of system design.

How much does the system design course cost?

7.99 US dollars for lifetime access globally, or 499 Indian rupees for lifetime access in India. One-time payment, no subscription, no hidden fees. 11 lessons are free with no signup required.

What technologies are covered?

Everything from DNS and load balancers to Kubernetes, Kafka, distributed databases, consensus protocols, stream processing, security architecture, and observability. We cover principles and real-world implementations used at Netflix, Google, Amazon, Uber, Stripe, and more.

Is this useful for system design interview preparation?

Yes. The lessons are structured around the exact topics asked in system design interviews at FAANG and top-tier companies. Interactive diagrams help you practice whiteboard-style explanations. Covers everything from URL shortener design to distributed payment systems.

How is this different from ByteByteGo or Educative?

766 interactive lessons (4x more than most competitors), 16 different diagram types that build step by step, real production examples from Netflix, Google, Amazon, Uber, and Stripe, and lifetime access for a one-time payment of $7.99 instead of annual subscriptions costing 100 to 200 dollars per year.

Why is embedding storage particularly expensive compared to storing traditional structured data?

Each vector has hundreds to thousands of float dimensions (e.g., 12 KB per vector), and the indexes needed for fast search must fit in memory, A 3,072-dim float32 vector is 12 KB. 100 million of them is 1.2 TB. The HNSW or IVF index on top adds even more. For low-latency search, this data needs to be in memory or fast SSD, and that's expensive at terabyte scale.

How does product quantization (PQ) achieve 32x compression on embeddings?

It splits each vector into sub-vectors and replaces each with an 8-bit codebook ID, so a 6,144-byte vector becomes ~192 bytes, PQ divides a vector into groups of dimensions, trains a codebook of centroids for each group, and stores just the centroid index (8 bits) instead of the full float values. A 1,536-dim vector split into 192 groups becomes 192 bytes instead of 6,144.

What is the simplest way to reduce embedding storage if you can tolerate slight accuracy loss?

Reduce the number of dimensions, use 512 instead of 1,536 via Matryoshka embeddings or PCA, Dimensionality reduction is the lowest-effort optimization. Models like OpenAI's text-embedding-3-small support truncation, using 512 of 1,536 dimensions gives 3x compression with only 2-5% accuracy loss. No retraining, no complex quantization pipeline.

Embedding Storage

Name: System Design Masterclass
Availability: InStock

in Database Types Storage

This lesson is part of the full System Design Masterclass. Unlock it along with 765 other lessons for a one-time payment.

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Foundation lessons free, try before you buy

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India

₹999₹499/lifetime

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Foundation lessons free, try before you buy

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“The interactive diagrams alone are worth way more than the price.”

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All 766 interactive lessons

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