Architecture patterns and trade-offs for modern AI systems
A curated, evolving atlas of AI system design patterns—focused on how to think about architecture and trade-offs, not on implementation recipes or prescriptive solutions.
This repository is a thinking artifact.
It captures the system-level reasoning that shapes AI products long before code is written.
Author — Aditi Khare
Enterprise AI Product, Platform & Applied Research Leader —
Writing on AI research, product thinking, and system architecture
- 🌐 Website: aditikhare.com
- 🔗 GitHub Repository: AI System Design Atlas
- 🤗 Live Demo: View on Hugging Face
- 💼 LinkedIn: Aditi Khare
⭐ If this repository helps you reason more clearly about AI systems, consider starring it.
AI systems rarely fail because of models alone.
They fail because of:
- architectural mismatches
- misunderstood constraints
- unexamined trade-offs
- evaluation blind spots
Most public resources focus on how to build.
This atlas focuses on how to reason before building.
This repository provides:
- Conceptual AI system design patterns
- Common trade-offs across latency, cost, reliability, and evaluation
- A shared vocabulary for architecture-level thinking
It is intentionally:
- Descriptive, not prescriptive
- Pattern-based, not tool-based
- System-focused, not model-focused
This is not:
- A production framework
- A set of best practices
- A deployment guide
- A decision tree or playbook
No architectures are recommended.
No choices are made for you.
Use this atlas to:
- Frame design discussions
- Compare architectural patterns
- Surface trade-offs early
- Ask better system-level questions
It is best used before implementation begins.
Different AI problems impose different architectural pressures:
- Search & retrieval systems
- Conversational AI
- Agentic workflows
- Multimodal systems
Every AI system is shaped by constraints such as:
- Latency sensitivity
- Cost and scale
- Reliability expectations
- Observability needs
Patterns are presented conceptually, including:
- Retrieval-augmented systems
- Agent-orchestrated systems
- Pipeline-based inference
- Feedback-driven systems
Patterns describe structure, not implementation.
All patterns surface trade-offs, for example:
- Latency vs interpretability
- Cost vs robustness
- Flexibility vs control
Trade-offs are highlighted—not resolved.
atlas/ → Conceptual foundations and dimensions
patterns/ → Architecture patterns (descriptive)
examples/ → System reasoning walkthroughs
diagrams/ → High-level system flow illustrations
🧠 Final Note
AI systems are designed long before they are implemented.
© 2026 Aditi Khare. All rights reserved.
This atlas captures that pre-implementation thinking layer— where architecture, constraints, and trade-offs quietly determine outcomes.
⭐ If this repository helps you reason more clearly about AI systems, consider starring it.