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.

What is the difference between model routing and a model cascade?

A router decides up front, reading features of the request before any model runs and picking one model to answer, which costs one call but predicts difficulty blind. A cascade decides after evidence, running the cheap model first, checking the answer, and escalating to a strong model only when the check fails, which reasons on a real answer but pays for a second call whenever it escalates.

Why is self-reported LLM confidence a bad escalation signal?

Because language models are frequently overconfident when they state a confidence, and the stated number correlates weakly with whether the answer is actually correct. Escalating on it means trusting the model to know when it is wrong, so cascades should escalate on deterministic validator failures, like a schema that does not parse or code that does not compile, rather than on the model's opinion of itself.

When does a model cascade stop saving money?

A cascade stops paying when the escalation rate climbs high enough that the cheap first pass becomes overhead you pay on every request while still sending most traffic to the strong model. Because every request pays for the cheap call and escalations pay for both, cascade cost is one plus the escalation rate times the price ratio, so as traffic hardens and escalation rises, the cost approaches the all-strong bill while quality stays below it.

What is an LLM gateway and what should it not do?

An LLM gateway is a single API surface in front of every provider and model that centralizes authentication, retries, fallbacks, logging, cost accounting, budgets, and model selection. It must not silently swap the model under a pinned request or a pinned evaluation, because doing so grades an eval baseline against a model it was not measured on and makes every downstream number wrong. Fallbacks are fine when a provider hard-fails, but the swap must be recorded and never happen underneath a pin.

Do I need to re-run evals when I change a route?

Yes. A model swap, a raised escalation threshold, an added specialist, or a shift of traffic between tiers all change the output distribution even when no application code changed, so each is a deploy. Route config belongs in version control, and a change should trigger the same benchmark CI that gates code, blocking on a drop in per-facet pass rates.

Should I use static rules or a learned router?

Use static rules keyed on task type when the task label is a good proxy for difficulty and you value transparency, since they need no training and anyone can read them. Move to a learned router when difficulty varies a lot inside a single task, so the label no longer predicts which model will succeed, and accept that the router is a model you must label, monitor for drift, and retrain.

Your cheap model costs 1 unit and your strong model costs 16 units per request. A cascade sends every request to the cheap model first and escalates 40 percent of them to the strong model. What does the cascade cost per request, and how does it compare to sending everything to the strong model?

1 + 0.40 times 16 = 7.4 units, still well below the all-strong 16 units, Every request pays 1 for the cheap call, and the 40 percent that escalate pay an extra 16 each, so the cost is 1 + 0.40 times 16 = 7.4 units against 16 for all-strong. The cheap first pass is never free: it is paid on every request, including the ones that later escalate, which is exactly why a high escalation rate erodes the saving until the cascade stops paying.

A cascade escalates from the cheap model to the strong model whenever the cheap model reports a confidence below 80. Quality is poor: the cheap model keeps returning wrong answers with high confidence, and correct answers get escalated. What is the core mistake?

The escalation signal is self-reported confidence, which correlates weakly with correctness; escalate on a deterministic validator instead, Language models are often overconfident, and their stated confidence tracks correctness weakly, so a wrong answer can carry high confidence and a right one low. Tuning the threshold does not fix a signal that is not measuring correctness. The fix is to escalate on something checkable, a validator on the output such as schema parsing, compilation, unit tests, or a groundedness check, rather than on the model's opinion of itself.

An engineer configures the gateway to transparently serve gpt-4o-mini in place of gpt-4o when load is high, without changing the caller or the logs. The nightly eval, whose baseline was measured on gpt-4o, still reports against the gpt-4o config. What is wrong?

The gateway is silently swapping the model under a pinned eval, so the baseline now grades a different model and every downstream number is wrong, Model identity is part of the contract. When the gateway substitutes a different model under a pin that the eval depends on, the baseline measured on gpt-4o is grading gpt-4o-mini, so the pass rates, cost figures, and the gate itself are invalid. A gateway may fall back on a hard provider failure, but the swap must be recorded and surfaced, and it must never happen silently underneath a pinned model.

A team wants to move 25 percent of traffic from the strong model to the cheap model to cut cost. No application code changes. What should happen before this ships?

It is a deploy: run the eval set in CI and gate on per-facet pass rates, because shifting traffic between models changes the output distribution, Shifting traffic between models changes what users get even though no application code changed, which makes it a deploy. Route config belongs in version control, and the change should trigger the same benchmark CI that gates code, blocking on a drop in per-facet pass rates. Shipping a route change without the eval gate is how quality regresses with a green build.

Model Routing and Cascades: Running Three Models Without Chaos

Name: System Design Masterclass
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