AI Architecture and Costs: Design Systems That Scale Affordably
AI Systems Architecture & Economics: leave with a model-gateway design with routing, caching, fallbacks, per-tenant cost attribution and a latency budget.
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What this session would cover
Proposed outline — the mentor finalises the agenda once this topic is scheduled.
- 1Why ai systems architecture & economics matters — the common problem: AI bills grow unpredictably and nobody knows which feature or customer drives cost.
- 2Core concepts in plain language: Model selection, managed versus self-hosted inference, GPU capacity, multi-tenancy, permission-aware retrieval, latency budgets
- 3Going further: token budgets, caching, model gateways, fallback models, cost attribution, operating procedures
- 4Framework walkthrough: NIST AI Risk Management Framework, OWASP Top 10 for LLM Applications, Model Cards
- 5Practical workflow, built live: A model-gateway design with routing, caching, fallbacks, per-tenant cost attribution and a latency budget.
- 6How to measure it: Regression-eval pass rate, Drift alerts, Security findings, Incident count
- 7An illustrative case (a fictional example, not a client result), then live Q&A on your own situation
Who it's for
- • Students and freshers entering tech
- • Working developers and engineers
- • Tech leads and architects
You'd leave with
- A model-gateway design with routing, caching, fallbacks, per-tenant cost attribution and a latency budget.
- A working understanding of NIST AI Risk Management Framework and OWASP Top 10 for LLM Applications
- A short list of measures to track: Regression-eval pass rate, Drift alerts, Security findings
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