Technical Problem
Monolithic AI applications collapse under heavy user load or sudden traffic spikes, cannot scale, and cause system-wide deadlocks.
ServicesII — Software Adaptation, Legacy Systems and Microservice Architecture
AI-BackedMicroservices
and Distributed System Architecture Design
Architectural solution
We design AI services as independent microservices on Docker/Kubernetes infrastructure. By separating model inference servers, vector databases and background job queues, we define auto-scaling rules. With load balancers and GPU cluster management we distribute resource use according to demand as it happens.
Operational outcome
An architecture where the part under load can grow on its own and updating one model does not stop the others. The availability target is written into the setup according to your requirement and then measured; we do not commit to a figure in advance, because that figure depends on hardware and operations as much as on design.
Starting Conditions
This service is needed when a single-piece AI application falls over entirely under load, or when updating one model means stopping the whole system. What has to be on your side is a sense of the real load — how many concurrent users, at which hours, doing what. An estimate will do, but an architecture cannot be built on a number that does not exist; if you cannot measure it, we measure it first.
How We Work
We start by finding where the current system actually blocks; the bottleneck is rarely where it is assumed to be. Then separation: inference, vector search and background queues become independently scalable parts. Each part becomes separately deployable, because the real gain is not scaling but being able to update one without stopping the others. Load testing runs in a copy of the environment, with real traffic patterns.
Out of Scope
Microservices are not the answer for every system: splitting an application that runs comfortably on one machine costs more in operations than it returns, and we say so. We do not run Kubernetes on your behalf — we set it up and hand it over; ongoing operation is a separate agreement. Hardware procurement and GPU capacity are also out of scope; the architecture uses that capacity efficiently, it does not create it.