My Approach to System Design and Architecture

Design choices, Scalability considerations and high-level architectures I prefer.

Introduction

Building AI and machine learning systems in production requires far more than model accuracy or algorithmic correctness. As systems scale, the primary challenges shift toward architecture, reliability, latency, cost control, and operational safety.

In this document, I describe how I design AI and ML systems for real-world use. The focus is on end-to-end architecture, system boundaries, scalability decisions, and trade-offs that arise when models interact with users, data, and infrastructure.

This is not a theoretical discussion of machine learning concepts. It is a practical overview of how I design AI systems that are deployable, maintainable, and resilient in production environments.

Why System Design Is Critical for AI and ML Systems

In production AI systems, the model is only one component of a larger system.

Effective system design determines:

  • Whether models can be served reliably under real traffic
  • How data flows are validated, secured, and monitored
  • How inference latency is controlled at scale
  • How failures are isolated and recovered
  • How costs are managed as usage grows

Poor system design can negate even the best-performing model. Strong system design ensures that AI capabilities translate into real business and user value.

Design Principles I Follow for AI Systems

When designing AI and ML architectures, I follow a small set of consistent principles:

  • Clear separation between data ingestion, inference, and user-facing paths
  • Stateless services wherever possible
  • Asynchronous processing for non-user-critical workloads
  • Explicit handling of trade-offs between accuracy, latency, and cost
  • Observability built into every critical path

These principles help ensure systems remain predictable as complexity increases.

Example 1: End-to-End Retrieval Augmented Generation System

Problem Context

The goal was to design a system capable of answering user queries using private, domain-specific documents while maintaining low latency, high answer quality, and strict data isolation across users or organizations.

System Architecture

The system consists of five major components:

  • A client-facing application
  • An API and orchestration layer
  • A document ingestion and preprocessing pipeline
  • A vector database for semantic retrieval
  • A language model inference service

Documents are ingested asynchronously, validated, chunked, embedded, and stored. User queries are embedded at request time, matched against stored vectors, and augmented context is provided to the language model for generation.

Key Architectural Decision

I separated document ingestion from query-time inference into independent pipelines.

This ensures that heavy ingestion workloads do not interfere with real-time query performance and allows both paths to scale independently.

Trade-off Consideration

This design introduces additional operational complexity and infrastructure components. I accepted this trade-off to achieve predictable latency, better fault isolation, and improved cost efficiency at scale.

Outcome

The system supports multi-tenant data isolation, scales to large document volumes, and delivers consistent, high-quality responses under real user traffic.

Example 2: Authentication and Rate Limiting for AI APIs

Problem Context

AI-driven APIs are particularly vulnerable to abuse due to high inference costs. The system needed to support multiple user tiers while preventing misuse and controlling operational expenses.

System Architecture

The architecture combines token-based authentication with a centralized rate limiting layer backed by an in-memory data store.

Each request passes through authentication, then rate limiting, before reaching model inference or application logic.

Key Architectural Decision

I enforced rate limiting at the gateway level rather than within individual services.

This ensures uniform enforcement across endpoints and avoids duplicating security logic across inference services.

Trade-off Consideration

Centralized rate limiting introduces a potential bottleneck. This was mitigated by using low-latency in-memory counters and short-lived rate windows.

Outcome

The system cleanly enforces usage tiers, protects expensive AI resources, and scales without embedding policy logic into model-serving code.

Example 3: Scalable Deployment Architecture for AI SaaS Platforms

Problem Context

The objective was to design a deployment architecture capable of supporting rapid iteration, global access, and independent scaling of AI workloads and user-facing components.

System Architecture

The system is composed of:

  • A globally distributed frontend deployed at the edge
  • Stateless backend services behind a load balancer
  • A relational database for transactional data
  • A vector database for semantic workloads
  • Dedicated inference services for model execution

Each layer scales independently based on workload characteristics.

Key Architectural Decision

I designed backend services to remain stateless and moved all persistent state into managed data stores.

This simplifies horizontal scaling, deployment automation, and failure recovery.

Trade-off Consideration

Stateless services increase reliance on external systems and network calls. I accepted this in exchange for improved scalability, simpler deployments, and clearer operational boundaries.

Outcome

The architecture supports real production traffic, enables continuous delivery, and provides a stable foundation for future AI feature expansion.

How I Evaluate Trade-offs in AI System Design

Every AI system involves trade-offs. There is no universally optimal architecture.

I explicitly evaluate decisions across dimensions such as:

  • Model accuracy versus inference latency
  • Cost per request versus response quality
  • System complexity versus operational reliability
  • Short-term delivery versus long-term maintainability

These trade-offs are documented and revisited as usage patterns and requirements evolve.

Conclusion

Effective AI and machine learning systems are defined by architecture as much as by models.

By designing systems with clear boundaries, scalable infrastructure, and explicit trade-off decisions, I ensure that AI capabilities remain reliable, secure, and valuable in real-world environments.

This approach treats AI systems as production software systems, not experiments, and ensures they can grow sustainably with user demand.

~ Vansh Garg