Monolithic AI applications become unmaintainable nightmares. Learn how microservices architecture enables scalable, resilient, and team-friendly ML systems.
Monolithic AI applications create unique scaling and maintenance challenges:
Data preprocessing needs CPU, model inference needs GPU, and feature storage needs memory. Monoliths force one-size-fits-all infrastructure.
Retraining one model forces deployment of entire application. A/B testing requires duplicating full infrastructure, not just changed models.
Multiple data scientists can't work independently when all models share one codebase. Conflicts, coordination overhead, and slow velocity.
One model's bug or performance issue brings down entire AI system. No isolation means every component is a single point of failure.
Decompose AI systems by function, not by model. Each service handles one clear responsibility.
Each service should be independently deployable, scalable, and maintainable by a single team. Loose coupling, high cohesion.
Choose synchronous or asynchronous communication based on latency requirements and failure tolerance.
Client → Gateway → Feature Service → Model Service → ResponseProducer → Kafka/SQS → Consumer (Model Service) → Result QueueEvent Bus ← Multiple Publishers → Multiple ML SubscribersEach microservice owns its data. Avoid shared databases that create tight coupling.
Services request data via APIs, not direct database queries. Use event sourcing or CDC to replicate data between services when needed.
Our architects have designed microservices for AI systems serving millions of predictions. Get expert guidance on service boundaries and deployment.
Kubernetes is the standard for managing microservices at scale, with special considerations for AI workloads.
Automated pipelines for testing, building, and deploying model services independently.
Track both infrastructure metrics and ML-specific signals across all services.
Begin with a monolithic prototype to validate ML approach. Extract microservices as you understand service boundaries and scaling needs. Premature decomposition wastes time.
Implement circuit breakers, retries with exponential backoff, and fallback strategies. One model service failure shouldn't cascade. Always have a plan B (cached predictions, simpler models).
Version APIs, models, features, and schemas. Use semantic versioning for backward compatibility. Support multiple versions simultaneously during transitions to prevent breaking clients.
Use API gateways for auth, rate limiting, and routing. Service mesh for observability and security. Don't duplicate logging, monitoring, or auth logic in every service.
Scale services based on their specific needs. Feature engineering on CPU, inference on GPU, monitoring on memory-optimized. Use horizontal pod autoscaling with custom metrics.
Test API contracts between services to catch breaking changes early. Use tools like Pact or Spring Cloud Contract. Validate schema compatibility before deployment.
Not necessarily. Group related models that share similar infrastructure needs and deployment cycles. For example, multiple text classification models can share one service. Separate them when they have different scaling needs, update frequencies, or resource requirements (CPU vs GPU).
Avoid distributed transactions - they're complex and fragile. Use eventual consistency with event sourcing or saga pattern. For critical workflows, implement compensating transactions to rollback on failure. Most AI use cases tolerate eventual consistency better than traditional transactional systems.
Network hops add 5-20ms per service call. Use service mesh with connection pooling to minimize overhead. For latency-critical paths, batch multiple predictions in one call or use gRPC instead of REST. Cache aggressively. The scalability and maintainability benefits usually outweigh minor latency costs.
Implement distributed tracing (Jaeger, Zipkin) to track requests across services. Add correlation IDs to all logs. Use centralized logging (ELK, Splunk) to query across services. Create dashboards showing request flow and timing. Invest heavily in observability from day one.
Yes, for simple models with infrequent traffic. Lambda/Cloud Functions work well for CPU-based inference. Challenges: cold start latency (2-5s), limited GPU support, 15-minute max runtime. Better for batch processing or low-QPS endpoints. Use containers (ECS, Cloud Run) for production inference workloads.
Our architects have designed microservices for enterprise AI platforms serving millions of users. Let us help you build maintainable, scalable ML systems.