AI Systems Architecture — Mastery6 / 9
Cost Engineering — Token Budgets That Hold
An AI feature that delights at 100 users can bankrupt you at 100,000. Cost is an architectural constraint, designed in — not discovered on the invoice.
Traditional software gets cheaper per user as you scale. AI software gets more expensive — every request costs tokens. If unit economics aren't designed in, growth is the thing that kills you.
Budget per request
Decide, per feature, a token budget the way you'd cap DB queries. Know the input + output token cost of a typical request and the worst case. "Cost per request × requests/month" is a spreadsheet you can fix before it's an invoice you can't.
Model tiering
Not every step needs your best model. Use a cheap, fast model for routing, classification, query rewriting, and faithfulness checks; reserve the expensive model for the step where quality is the product. This is often a 2–5x cost cut at equal quality.
Cache everything cacheable
- Prompt/response cache for stable, repeated requests.
- Prompt caching (provider-side) for the large, unchanging prefix of a prompt.
- Retrieval cache so popular queries don't re-search.
A cache hit is a near-free request.
Trade quality for cost deliberately
Costs controlled. Next: making it fast — latency and throughput at scale.
Series — AI Systems Architecture — Mastery
- Part 01Architecting AI Products — First PrinciplesAI systems fail differently from normal software: they're non-deterministic, costly per call, and hard to test. The architecture has to account for all three.
- Part 02Single Agent vs. Multi-Agent — Choosing a TopologyMulti-agent is fashionable and usually premature. Here is how to decide honestly — and why most products should start with one well-equipped agent.
- Part 03Orchestration Patterns — Pipelines, Routers, SwarmsOnce you have multiple steps or agents, how they're wired together decides cost, latency and reliability. Four patterns cover almost everything.
- Part 04Context & Memory ArchitectureThe context window is your most expensive, most contested resource. What you put in it — and what you remember between calls — is an architectural decision.
- Part 05Evaluation Pipelines as InfrastructureIn AI systems, evaluation is not QA you do at the end — it's infrastructure you build first. Without it, every change is a prayer.
- Part 06Cost Engineering — Token Budgets That Hold — you are hereAn AI feature that delights at 100 users can bankrupt you at 100,000. Cost is an architectural constraint, designed in — not discovered on the invoice.
- Part 07Latency & Throughput at ScaleInference is slow and bursty. Streaming, parallelism, and the async boundary are what keep an AI product feeling fast under real load.
- Part 08Reliability — Retries, Fallbacks, GuardrailsModels return malformed output, providers go down, and outputs drift. A reliable AI system expects all three and keeps working anyway.
- Part 09The Reference Architecture in ProductionTopology, orchestration, memory, eval, cost, latency and reliability — composed into one blueprint for an AI system that survives real users.