microservices architecture

**Microservices architecture** is a software design approach that structures an application as a collection of **small, independent, loosely coupled services**, each responsible for a specific business capability, deployable independently, and communicating through well-defined APIs. **Microservices for AI/ML Systems** - **Model Service**: Hosts model inference, handles prediction requests, manages model loading and GPU allocation. - **Preprocessing Service**: Handles input validation, tokenization, prompt formatting, and data transformation. - **RAG Service**: Manages vector databases, document retrieval, and context assembly. - **Orchestration Service**: Coordinates multi-step workflows, agent chains, and tool calling. - **Gateway Service**: Handles authentication, rate limiting, request routing, and API versioning. - **Monitoring Service**: Collects metrics, logs, and traces across all other services. **Benefits** - **Independent Scaling**: Scale the inference service horizontally during peak demand without scaling the preprocessing service. - **Independent Deployment**: Update the RAG service without touching the model service — reduced deployment risk. - **Technology Flexibility**: Use Python for ML services, Go for the gateway, and Rust for performance-critical components. - **Fault Isolation**: If the RAG service crashes, the model can still serve requests (with degraded quality) rather than the entire system failing. - **Team Autonomy**: Different teams own different services and can develop, test, and deploy independently. **Challenges** - **Network Latency**: Inter-service communication adds latency compared to in-process function calls. - **Distributed Complexity**: Debugging issues that span multiple services requires distributed tracing (Jaeger, OpenTelemetry). - **Data Consistency**: Maintaining consistent state across services is complex. - **Operational Overhead**: Each service needs its own deployment pipeline, monitoring, and infrastructure. **When to Use Microservices vs. Monolith** - **Start Monolithic**: For early-stage projects, a monolith is simpler and faster to develop. - **Extract Services**: As the system grows, extract components that need independent scaling or deployment into services. Microservices architecture is the **standard pattern** for production AI systems at scale, enabling independent scaling of GPU-intensive inference from lightweight preprocessing and routing.