I'm building a RAG app and want advice on infra layers I actually need

For a RAG app, you usually don’t need a huge infra stack at first. The simplest reliable setup is often enough:

Minimum viable infra

1. App API / backend

   • Your service that handles chat requests, retrieval, prompting, and response formatting.
   • Can be a single web app or serverless functions.

2. Document storage

   • Store raw docs in object storage or a DB.
   • Examples: S3, GCS, Azure Blob, Postgres.

3. Embedding + indexing pipeline

   • A job or worker that:
     • chunks documents
     • creates embeddings
     • writes them to a vector store
   • This can be async and doesn’t need to be real-time initially.

4. Vector store

   • Needed if you want semantic retrieval.
   • Could be:
     • Postgres + pgvector
     • a managed vector DB
     • or a search engine with vector support

5. LLM inference access

   • Usually just an API to a model provider at first.
   • You don’t need to host a model unless you have cost/privacy/latency reasons.

6. Basic observability

   • Logs, request tracing, and failure alerts.
   • This matters early because RAG failures are often silent quality issues.

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Infra layers you may think you need, but often don’t at first

1. Kubernetes

Only if you already have scale/ops complexity. Most RAG apps don’t need it early.

2. Message queues

Useful if ingestion is heavy, but not mandatory if your indexing pipeline is small.

3. Feature store / data lake / warehouse

Usually overkill unless your RAG app is tightly tied to analytics or ML training.

4. Separate cache layer

Not required initially. Add it only if repeated retrieval or generation is expensive.

5. Multi-region or active-active deployment

Only if you have strict availability or latency requirements.

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A practical “good enough” stack

A common early setup:

• Frontend: Next.js / React
• Backend: FastAPI / Node
• Doc storage: S3 or Postgres
• Vector storage: pgvector or managed vector DB
• Workers: one async job worker for ingestion
• LLM: external API
• Logging/monitoring: standard cloud logs + error tracking

This can get you surprisingly far.

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What matters most operationally

In RAG, the main infra pain points are usually:

• document ingestion reliability
• reindexing when data changes
• latency during retrieval
• cost control for embeddings/LLM calls
• evaluation and debugging of answer quality

So the infra you “need” is the stuff that helps with those.

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Rule of thumb

If you’re early stage:

• start with one app service
• one storage system
• one vector index
• one background worker
• one observability stack

Add complexity only when one of these becomes a bottleneck.

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If you want, I can also give you:

1. a minimal production architecture diagram
2. a recommended AWS/GCP/Azure stack
3. a “build vs buy” comparison for vector DBs
4. a cost-optimized stack for MVP vs scale

If you tell me your expected document volume, query volume, and cloud preference, I can suggest the exact layers to keep or skip.