Beginner

Introduction to Microservices for AI

Understand why microservice architecture is essential for production AI systems, the trade-offs involved, and when to decompose ML monoliths.

Why Microservices for AI?

AI systems have unique requirements that make microservices particularly valuable:

Heterogeneous compute: Different models need different hardware (GPUs, TPUs, CPUs). Microservices let you allocate resources per model.
Independent scaling: A recommendation model may need 10x the capacity of a fraud detection model. Scale each independently.
Independent deployment: Update one model without redeploying the entire system. Ship model updates multiple times per day.
Technology diversity: Use Python for ML, Go for API gateways, and Rust for preprocessing - each service uses the best tool.
Team autonomy: Different ML teams can own, develop, and deploy their models independently.

💡

The AI monolith problem: Many organizations start with a single Flask/FastAPI server that loads all models into memory. This works until you have 5+ models, need different GPU types, or want to deploy updates without 30-minute cold starts.

Monolith vs Microservices for ML

Aspect	ML Monolith	ML Microservices
Deployment	All models deploy together	Each model deploys independently
Scaling	Scale everything or nothing	Scale each model separately
Hardware	One GPU type for all	Right GPU for each model
Failure blast radius	One model crash kills all	Failures are isolated
Complexity	Simple to start	Network, orchestration overhead
Latency	In-process calls (fast)	Network calls (slower)

When to Use Microservices

Multiple Models

When you have 3+ models in production that need different update frequencies, hardware, or scaling characteristics.

Multiple Teams

When separate ML teams need to develop, test, and deploy their models independently without coordination overhead.

Mixed Workloads

When you have batch and real-time inference, different GPU types, or models in different frameworks (PyTorch, TensorFlow).

High Availability

When you need fault isolation so that one model's failure does not affect others. Critical for production SLAs.

Typical AI Microservice Architecture

A production AI system typically decomposes into these services:

Service	Responsibility	Technology
API Gateway	Request routing, auth, rate limiting	Kong, Envoy, NGINX
Preprocessing	Input validation, feature extraction	FastAPI, Go
Model Serving	ML inference on GPU	Triton, TFServing, BentoML
Postprocessing	Format results, apply business rules	FastAPI, Node.js
Feature Store	Real-time feature retrieval	Feast, Redis
Model Registry	Model versioning, metadata	MLflow, W&B
Monitoring	Metrics, logs, alerts	Prometheus, Grafana

✅

Start with a modular monolith: Don't jump to microservices immediately. Start with a well-structured monolith with clear module boundaries. Extract services only when you have a concrete need (different scaling, different teams, different hardware).

Next → Architecture

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