I'm building a RAG app and want the simplest reliable infrastructure stack

If you want the simplest reliable RAG infrastructure stack, I’d optimize for:

• few moving parts
• managed services where possible
• easy observability
• something you can scale later without a rewrite

Recommended “simple but solid” stack

1) App/API

• Python + FastAPI
• Deploy on:
  • Render / Fly.io / Railway for simplicity, or
  • AWS ECS/Fargate if you want more “real production” control

Why: FastAPI is easy for RAG workflows, streaming responses, and tool integration.

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2) Document ingestion / background jobs

• Simple queue + worker
  • Celery + Redis if you want a common setup
  • or RQ + Redis if you want even simpler

If you want the absolute simplest ops:

• use one background worker process alongside the app initially

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3) Vector store

Pick one of these:

Best “simple and reliable” choice

• Postgres + pgvector

Why:

• one database for app data + vectors
• easy backups
• mature and familiar
• good enough for many RAG apps

Use managed Postgres:

• Supabase
• Neon
• RDS
• Crunchy Bridge

If you expect very large scale or want dedicated vector infra later, move to a vector DB later. But for a simplest reliable stack, pgvector is the default recommendation.

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4) Document storage

• Object storage
  • S3 / GCS / R2

Store:

• raw files
• parsed text
• chunk metadata
• ingestion artifacts

Why: don’t put documents directly in the app server filesystem.

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5) LLM provider

• OpenAI or Anthropic
• Use whichever matches your latency/cost/quality needs

For a simple stack:

• OpenAI for embeddings + generation is straightforward
• Anthropic is excellent for answer quality

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6) Embeddings

• Use a managed embedding model from your LLM provider
• Store embeddings in pgvector

Keep one embedding model initially to avoid complexity.

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7) Retrieval / ranking

Start with:

• basic semantic search
• optional hybrid search if needed later
• optional reranker once relevance matters

For simplicity:

1. chunk docs
2. embed chunks
3. vector search top-k
4. optionally rerank
5. pass context to LLM

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8) Caching

• Redis

Use it for:

• query/result caching
• rate limiting
• job queues
• session state if needed

If you’re keeping it minimal, Redis can also be omitted at first unless you need background jobs.

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9) Observability

Don’t skip this.

Minimal set:

• structured logs
• request IDs
• error tracking: Sentry
• basic metrics: provider dashboards or OpenTelemetry later

For RAG-specific visibility:

• log retrieved chunk IDs, scores, prompt version, model, latency, and token usage

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The simplest “production-ready” version

If I had to pick one stack for most small-to-mid RAG apps:

• Frontend: Next.js
• Backend: FastAPI
• DB: Managed Postgres + pgvector
• Storage: S3/R2
• Queue: Redis + RQ or Celery
• LLM/Embeddings: OpenAI or Anthropic
• Monitoring: Sentry + logs
• Deploy: Render/Fly/Railway or ECS/Fargate

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Even simpler MVP stack

If you want the absolute minimum to ship fast:

• Next.js
• Supabase Postgres + pgvector
• Supabase Storage or S3
• No queue initially; ingest synchronously for small volumes
• OpenAI embeddings + chat model
• Sentry
• Vercel or Render

This is often enough for:

• internal tools
• prototypes
• early customer demos
• low/medium traffic apps

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What I would avoid at first

To keep it simple and reliable, avoid initially:

• running your own vector database cluster
• multiple queues and workers
• overcomplicated microservices
• custom retrieval infrastructure before you need it
• complicated hybrid search pipelines on day 1
• self-hosted models unless you have a strong cost/privacy reason

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My default recommendation

If you want one concrete answer:

FastAPI + Postgres(pgvector) + S3 + Redis + OpenAI + Sentry

That’s the simplest stack I’d still call reliable.

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If you want, I can also give you:

1. a bare-minimum architecture diagram,
2. a cheap startup version, or
3. a more scalable AWS version.