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 an LLM agent so much more expensive than a chatbot on the same model?

Because an agent resends its stable prefix on every step of the loop, while a chatbot pays for it once per turn. The system prompt and tool descriptions are billed on every call, and the conversation history grows and is replayed each step, so a task that takes ten steps pays for the prefix ten times and for an ever-larger history. The user's actual question is usually the smallest part of the bill.

How does prompt caching reduce token cost?

Prompt caching stores the stable leading prefix of your request and bills it at a fraction of fresh input price on later calls that match it, so the largest resent bucket becomes cheap. It is prefix-based, meaning it only works up to the first token that differs, so you must place stable content first and keep it stable. A cache miss costs full input price, and on some providers a small write premium, so caching pays only when the hit rate is high enough to repay the writes.

Why are output tokens more expensive than input tokens?

On every major provider output tokens are priced several times higher per token than input, commonly three to five times, because a model generates output one token at a time while it reads the whole prompt in a single parallel pass. This means chain-of-thought, reflection, and verbose formatting all land in the priciest bucket, so a token trimmed from output moves the bill more than a token trimmed from input. Always check the live provider pricing page for the current ratio.

What is cost per successful task and why use it instead of cost per token?

Cost per successful task is total token spend over a period divided by the number of tasks that actually succeeded, which folds in resends, reflection, retries, and the tokens burned on failed attempts. It is the honest per-outcome cost, and it is routinely two to three times the sticker price of one clean call. Cost per token is the least useful unit because it says nothing about how many tokens a real outcome takes.

When does the Batch API make sense for cutting LLM cost?

The Batch API makes sense for any work where no user is waiting, such as bulk classification, data enrichment, offline evaluation, and nightly summaries. OpenAI's Batch API and Anthropic's Message Batches both document roughly half the synchronous price for a turnaround window of up to about a day on the same models, so you trade immediacy for a standing discount with no quality change.

Does slimming the prompt still help once prompt caching is on?

Yes, because caching makes the prefix cheap but not free. A slimmer prefix is billed at the cache rate on every hit, costs less to write on every miss, and is the only lever that helps when the hit rate is low or the entry has expired. Cutting unused tools and verbose schemas also tends to improve tool selection, so slimming often lowers the bill and raises reliability at the same time.

An agent takes 8 steps to finish a task, with a 4,000-token system prompt and tool schema. Why does this cost far more than a single chatbot turn on the same model?

The 4,000-token stable prefix is resent as input on every step, so it is billed 8 times, and the replayed history grows each step on top of it, The model keeps no state between calls, so the runtime resends the stable prefix on every step. An 8-step task pays for that 4,000-token prefix 8 times, and it also replays a history that grows each step. Most of the input tokens on the bill are resent prefix and replayed history, not new text, which is the structural reason agent bills outrun chatbot bills on the same model.

You enable prompt caching but see almost no discount. Your prompt puts a per-request timestamp and session id on the very first line, above the system prompt. What is happening?

The timestamp changes every call, so the cached prefix never matches past the first token, and caching only works up to the first difference, so nothing below it is cached, Caching is prefix-based: it matches the leading tokens up to the first one that differs. A per-request value at the very top moves that first difference to the top of the prompt, so the entire stable block beneath it fails to match and is billed at full price every call. The fix is order: put the stable system prompt and tool schemas first, and the variable timestamp, session id, and user message last.

A task's sticker cost is 3 cents for one clean call. It runs with a reflection pass and up to 2 retries, and per-attempt success is 70 percent. Which statement about the real cost per successful task is correct?

It is meaningfully higher, roughly 2 to 3 times the sticker, because reflection multiplies each attempt, retries add attempts, and failed tasks still spent tokens that spread over the successes, Cost per successful task is the sticker times three multipliers: reflection (each attempt costs more), expected attempts per task from the retry math (1 minus (1 minus p) to the R plus 1, over p), and one over the success rate for the tokens burned on failed tasks. With reflection 1.8x, up to 3 attempts, and p of 0.7, the product is about 2.5x, taking 3 cents to roughly 7.7 cents. That is the number to budget on.

After enabling caching and slimming the prefix, the per-call breakdown shows the system prefix is only about 6 percent of the cost while history and output are the rest. What does this tell you to do next?

The prefix is now a small share, so the next levers are carrying less history forward and controlling output length, not further prefix cuts, Once caching and slimming have collapsed the prefix to a small share, more prefix cutting has little left to give. The breakdown is a map: the money has moved to history, which you attack by carrying less context forward, and output, which you attack with caps, terser formats, and gating chain-of-thought and reflection. Optimize the bucket that is now largest, not the one you already solved.

Token Cost Engineering: Where the LLM Bill Actually Goes

Name: System Design Masterclass
Availability: InStock

in Llm Genai Ops

This lesson is part of the ML Platform Engineering course. Unlock the full course, a separate one-time payment, for lifetime access.

One-time payment. Lifetime access. No subscription.

Global

$20/lifetime

One-time payment via PayPal

Get Lifetime Access

5 Foundation lessons free, try before you buy

India Special

India

₹999/lifetime

One-time via UPI, cards, or net banking

Get Lifetime Access

5 Foundation lessons free, try before you buy

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

Anish K., Senior Engineer at a Bengaluru startup

Every ML Platform lesson, Foundation to Advanced

Real architecture diagrams with real tool logos

MLOps, LLMOps, and data engineering in depth

Quizzes with instant feedback

Built and reviewed by senior engineers

Lifetime access, all future content included

The first 5 Foundation lessons of this course are free, no payment needed. Try them now