I'm building a RAG product and need advice on the infrastructure stack

A good RAG infrastructure stack is usually built around four layers:

1. Ingestion / processing
2. Retrieval / storage
3. Generation / orchestration
4. Evaluation / observability

The best stack depends on your scale, latency target, budget, and whether you’re optimizing for fast MVP or production reliability. Here’s a practical breakdown.

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1) Start with the core architecture

A typical RAG setup looks like:

• Source systems: PDFs, docs, wikis, tickets, databases, web pages
• Ingestion pipeline: extract → clean → chunk → enrich metadata → embed
• Indexing layer: vector DB + optional keyword/BM25 index
• Retrieval layer: hybrid search + reranking
• LLM layer: prompt assembly + answer generation
• Monitoring: tracing, quality metrics, cost tracking, feedback loops

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2) Recommended stack by layer

A. Ingestion / ETL

Use this for document parsing, chunking, scheduling, and sync.

Common choices

• Python for pipeline logic
• Unstructured or Apache Tika for document parsing
• Airbyte or Fivetran for connectors if you need many sources
• Dagster or Prefect for workflow orchestration
• Celery / RQ / Temporal if you need asynchronous jobs

When to choose what

• Fast MVP: Python + Unstructured + simple cron jobs
• Production pipelines: Dagster/Prefect + queue + retries + idempotency
• Enterprise data integration: Airbyte/Fivetran

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B. Embeddings

This affects retrieval quality a lot.

Options

• Managed APIs: OpenAI, Cohere, Voyage, Gemini
• Self-hosted: bge, e5, jina embeddings

Advice

• If you’re early-stage, use a strong managed embedding API first.
• If cost/privacy becomes important, consider self-hosting later.
• Pick one embedding model and benchmark before changing often.

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C. Vector store / retrieval storage

This is one of the most important decisions.

Good managed options

• Pinecone: easy, scalable, production-friendly
• Weaviate Cloud
• Qdrant Cloud
• MongoDB Atlas Vector Search
• Elastic Cloud if you need hybrid search and enterprise search features

Self-hosted options

• Qdrant
• Weaviate
• Milvus
• Postgres + pgvector for simpler workloads

Guidance

• MVP / small-medium scale: Postgres + pgvector is often enough.
• Need hybrid search + simple ops: Elasticsearch/OpenSearch or Weaviate/Qdrant.
• High scale, low ops burden: Pinecone or managed Qdrant/Weaviate.
• If your product is already Postgres-centric: pgvector is a very practical choice.

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D. Keyword / hybrid retrieval

Pure vector search often isn’t enough.

Add:

• BM25 / lexical search
• Hybrid retrieval
• Metadata filters
• Reranking

Good options

• Elasticsearch/OpenSearch
• Postgres full-text search if small scale
• Vector DBs that support hybrid retrieval
• Separate reranker model: Cohere Rerank, Voyage rerank, bge-reranker, etc.

Best practice

• Use hybrid search for most real products.
• Add a reranker before sending context to the LLM.
• Use metadata filtering aggressively: tenant, source, date, ACL, doc type.

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E. LLM / generation layer

This is where you answer the user.

Common choices

• OpenAI
• Anthropic
• Google Gemini
• Azure OpenAI if you need enterprise procurement/security

Orchestration frameworks

• LlamaIndex: great for RAG-heavy apps
• LangChain: flexible, large ecosystem
• Haystack: solid retrieval-focused framework
• Custom orchestration: often best once you stabilize requirements

Advice

• Use a framework to prototype quickly, but don’t let it become a black box.
• Keep your prompt assembly and retrieval logic observable and testable.

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F. Caching

Very important for latency and cost.

Use:

• Redis for:
  • query result caching
  • embedding cache
  • session/state cache
  • rate limiting
• Optional CDN/cache if you serve static assets or files

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G. Metadata, app DB, and ACLs

You’ll almost always need a relational DB.

Use:

• Postgres for:
  • users
  • tenants
  • document metadata
  • access control lists
  • ingestion state
  • audit logs
  • evaluation records

Postgres often becomes the system of record, while vector DB handles similarity search.

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H. Eventing / queue

For scale and reliability:

• SQS / SNS
• Kafka
• RabbitMQ
• Redis queues for simpler setups

Use queues for:

• ingestion
• reindexing
• embedding generation
• background enrichment
• offline evaluations

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I. Observability / evaluation

This is crucial in RAG.

Track:

• retrieval precision/recall
• reranker quality
• groundedness
• citation correctness
• hallucination rate
• latency breakdown
• token/cost per query
• user feedback

Tools:

• OpenTelemetry
• LangSmith
• Arize Phoenix
• Helicone
• PromptLayer
• Datadog / Grafana / Prometheus
• custom eval harness in Python

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3) My practical recommendations by stage

If you’re building an MVP

Use:

• Backend: Python + FastAPI
• Orchestration: LlamaIndex or LangChain
• DB: Postgres
• Vector store: pgvector or managed Qdrant/Pinecone
• Embedding: managed API
• LLM: OpenAI/Anthropic
• Queue: Redis/RQ or Celery
• Observability: OpenTelemetry + basic logging

This gets you moving quickly without overengineering.

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If you’re building a production SaaS

Use:

• Backend: FastAPI / Node.js / Go depending on team
• DB: Postgres
• Vector store: Pinecone/Qdrant/Weaviate/Elastic depending on retrieval needs
• Search: hybrid retrieval + reranker
• Queue: SQS/Kafka
• Orchestration: Dagster/Temporal/Prefect for ingestion
• Cache: Redis
• Monitoring: OpenTelemetry + Datadog + Phoenix/LangSmith
• Auth/ACL: enforced at retrieval time

Key: make retrieval secure, observable, and testable.

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If you’re building for enterprise search

Prioritize:

• ACL-aware retrieval
• auditability
• data residency
• encryption
• hybrid search
• source citations
• incremental indexing
• admin tooling
• evaluation and approval workflows

Likely stack:

• Elastic/OpenSearch or Weaviate/Qdrant + BM25
• Postgres
• Temporal/Dagster
• Azure OpenAI / Anthropic / approved vendor
• Datadog + SIEM integration
• Private networking / VPC deployment

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4) The most common mistakes

• Using only vector search and ignoring keyword search
• Not reranking
• Ignoring ACLs and tenant isolation
• No evaluation harness
• No tracing of retrieval and prompts
• Over-chunking or under-chunking documents
• Re-embedding too often without versioning
• Building around framework defaults without understanding them
• Putting all metadata only in the vector DB
• No strategy for stale data or incremental updates

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5) A strong default stack

If you want a “safe default” stack that works for many products:

• API: FastAPI
• Workflow: Dagster or Prefect
• DB: Postgres
• Vector: Qdrant or pgvector
• Search: hybrid retrieval + reranker
• Embeddings: managed embedding API
• LLM: OpenAI or Anthropic
• Cache: Redis
• Queue: SQS or Celery
• Observability: OpenTelemetry + Phoenix/LangSmith
• Infra: Docker + Kubernetes or simpler container hosting early on

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6) If you want the shortest possible answer

For most RAG products:

• Postgres + vector DB + Redis + queue
• hybrid retrieval + reranking
• managed embeddings and LLMs initially
• strong observability and evals
• ACLs in the retrieval layer

That combination is usually the right starting point.

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If you want, I can also give you:

1. a cheap MVP stack
2. a scalable production stack
3. a multi-tenant enterprise stack
4. a reference architecture diagram
5. a make-vs-buy comparison of vector DBs