Microservices Architecture for AI Systems

An AI system built as a monolith ships fast initially but becomes brittle under load, expensive to scale selectively, and risky to update. Decomposing AI systems into independent services applies the same reasoning that drove microservices adoption in backend engineering: isolate failure domains, scale hot components independently, and deploy without coordinating every team.

Service Decomposition for AI Pipelines

A typical AI pipeline can be decomposed along its functional seams:

Ingestion Service - Accepts raw documents, events, or data feeds. Validates format, deduplicates, normalises encoding. Publishes to a queue. Has no knowledge of downstream processing. Scaled by inbound volume.

Processing Service - Consumes from the ingestion queue. Performs chunking, cleaning, metadata extraction, embedding generation. Writes processed records to object storage or a database. Scaled by processing backlog depth.

Inference Service - Accepts structured requests (query + retrieved context, or structured input). Calls the foundation model API. Returns structured output. Does not know how context was retrieved. Scaled by request rate and latency targets.

Retrieval Service - Handles vector search, keyword search, and hybrid retrieval. Returns ranked document chunks given a query. Independently deployable so embedding models and search parameters can be tuned without touching inference.

Output Service - Takes inference results and writes to the destination: sends an email, posts to a Slack channel, writes a record to a database, returns an HTTP response. Handles formatting, delivery retry, and idempotency.

Service Boundaries

Good service boundaries follow the Single Responsibility Principle applied at the service level: each service owns one thing and exposes that thing through a stable API.

What belongs inside a service:

• Its own data store (not shared with other services)
• Its own configuration
• Its scaling policy
• Its failure mode (it can be down without taking others down)

What crosses service boundaries:

• Message queues (for async communication)
• HTTP/gRPC APIs (for synchronous communication)
• Shared schemas for events and contracts (versioned separately)

What should never cross boundaries:

• Database tables
• In-memory state
• Framework internals

API Design for AI Services

AI service APIs should be designed around the data shape, not the model implementation.

For an inference service:

POST /inference
{
  "request_id": "req-123",
  "context": [...],
  "instructions": "Summarise the following in 3 bullet points.",
  "model_config": {
    "max_tokens": 500,
    "temperature": 0.1
  }
}

The caller does not know which model is behind the endpoint. This allows swapping Claude for GPT-4 or switching from a hosted API to a fine-tuned model on SageMaker without changing any calling service.

For a retrieval service:

POST /retrieve
{
  "query": "What is our refund policy for digital products?",
  "top_k": 5,
  "filters": {"category": "policy"},
  "search_mode": "hybrid"
}

The caller does not know whether the backend is OpenSearch, pgvector, or Pinecone.

Deploying AI Microservices on AWS

Each service maps to a natural AWS deployment unit:

Step Functions provides orchestration when services need to be composed into a workflow with retry logic and error handling. EventBridge decouples services that produce and consume events without direct API calls.

When Microservices Are Not the Right Pattern

Microservices add operational complexity. For small AI applications with a single team and modest load, a well-structured monolith is usually the right starting point. Decompose when:

• Independent scaling is needed (inference costs are high; processing is cheap)
• Different teams own different components
• Component reliability requirements differ (output service can be eventually consistent; inference service must be synchronous)
• You need to deploy components on different cadences

Do not decompose prematurely. A clear modular monolith with explicit interfaces is easier to extract into services later than a tangled codebase that was decomposed too early.