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 MCP (the Model Context Protocol)?

MCP is an open client-server protocol that standardizes how a model discovers and calls tools and reads resources. An AI application acts as a host that creates one client per server, and each server exposes tools, resources, and prompts over JSON-RPC. Its main value is turning N times M bespoke tool integrations across N agent surfaces and M capabilities into N plus M reusable interfaces.

What are the host, client, and server roles in MCP?

The host is the AI application that runs the agent loop and coordinates everything. For each server it wants to use, the host creates one client, and each client holds one dedicated connection to one server. The server is the program that exposes tools, resources, and prompts, and it can run locally as a subprocess or remotely as a hosted service usable by any host.

What is the token cost of attaching MCP servers?

Every attached server injects its tool descriptions into the context window on every request, because the model is stateless and the prompt is rebuilt each turn. A schema-heavy tool can consume on the order of a thousand tokens, so a couple dozen tools puts tens of thousands of tokens in the window before the user's message. In an agent loop that runs many turns per task, you pay that on every turn.

Does prompt caching remove the cost of MCP tool descriptions?

No. Prompt caching lowers the dollar cost, because a stable tool block sits at the front of the prompt and caches cleanly with cache reads at a fraction of input price, but it does not free the context window space the descriptions occupy, does not make the model's tool selection easier, and is invalidated if you change the tool set mid-session. Caching is a discount on a stable tool block, not a substitute for keeping the tool set small.

When should you use MCP versus a plain function call?

Use MCP when many agent surfaces share the same capability, so a server written once serves them all, or when a maintained server already exists that you can connect to instead of owning a function. Use a plain function call when it is one app with a few tools you own, because MCP would only add a network hop, per-request description tokens, and a version and auth surface with no reuse to amortize them.

Why does adding more tools make an agent less reliable?

Selecting the right tool is itself a step that can fail, and its reliability falls as the tool count grows. As descriptions overlap and similar tools blur, the model confuses them or fills the wrong one's arguments, with degradation widely reported to begin around ten to twenty active tools. The fix is keeping the tool set small, exposing fewer tools per server, and using routing or tool search so the model only sees the tools the current step needs.

What does MCP primarily reduce, and what does it not reduce?

It reduces integration effort (N times M glue becomes N plus M interfaces) but not the per-request cost, since each attached server still injects tool descriptions on every request, MCP's win is an integration-effort argument: N agent surfaces and M capabilities go from N times M bespoke adapters to N plus M protocol interfaces. That collapse is real. It says nothing about runtime cost. Because the model is stateless, every attached server's tool descriptions are re-injected into the context window on every request, and MCP adds a hop rather than removing one.

You attach a Postgres MCP server 'in case it is useful,' but a given task never queries the database. What is the cost on that task?

The Postgres tool descriptions are still injected into the window on every turn of that task, billed as input and competing for space, even though the tools are never called, The prompt is rebuilt on every turn, and the host injects every attached server's tool catalog whether or not the task uses it. So the Postgres descriptions cost input tokens and window space on every turn of that task despite never being called. The overhead is linear in the number of attached servers and paid per request, which is why 'attach it just in case' is an expensive default.

A team enables prompt caching on their MCP tool block to cut costs. What problem does this NOT solve?

The tool descriptions still occupying context window space and the model still having to read every tool to pick one, so selection difficulty from a large tool set is unchanged, Caching genuinely lowers the dollar cost, because the tool block is a stable prefix and cache reads are cheap. It does not shrink the window footprint (the descriptions still occupy space that task and history need) and it does not make selection easier (the model still reads every tool to choose one). It is also invalidated by changing the tool set mid-session. Caching is a discount, not a substitute for a small tool set.

You are building one internal app with three tools you wrote and control, used by no other surface. Should you expose them via MCP or call them directly?

A plain function call, because there is no reuse to amortize and MCP would only add a hop, per-request description tokens, and a version and auth surface, MCP's benefit is reuse across surfaces or connecting to an existing maintained server. Neither applies here: one app, a few tools you own, no second surface and no external server. A direct in-process function call, with arguments validated against a schema, has no transport hop, no extra per-request description tokens, and nothing new to version or authenticate. MCP would add cost and buy nothing, so 'MCP by default' is the trap.

MCP in Production: What the Protocol Buys and Costs

Name: System Design Masterclass
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