Integration
Microservices Architecture
Microservices architecture structures an application as a collection of small, independently deployable services that each own a single business capability and communicate over lightweight APIs. It contrasts with monolithic architectures, where all functions share a single deployable unit.
AI systems built on microservices isolate model inference, retrieval, guardrails, and business logic into separate services that can be scaled, updated, and monitored independently. The tradeoff is operational complexity: microservices require service discovery, distributed tracing, and resilient inter-service communication. For most enterprise AI deployments, a modular monolith is preferable to microservices until scale demands otherwise.
Microservices Architecture in practice
For a first AI feature, most teams add one service or module next to the existing application: it holds the prompts, calls the model provider and returns results through an internal API. Splitting further pays off once several applications share the same AI capabilities or need to scale differently. The choice comes up early in LLM API integration and in cloud AI deployments.
Related terms
- API Gateway (AI) · Integration
- LLM Integration · Integration
- Event Streaming · Integration