I'm building a RAG app for a mid-size company. Should I use a vector DB or search engine?

For a mid-size company building a RAG app, the default choice is often:

Use a search engine if your knowledge is mostly text and you need strong keyword/metadata filtering.
Use a vector DB if semantic similarity is the core requirement and you want the simplest path to embedding-based retrieval.
In many real RAG systems, the best answer is actually a hybrid search engine + vector retrieval setup.

Quick rule of thumb

Choose a search engine if you need:

• Great keyword/BM25 search
• Faceted filtering on metadata like department, date, product, access level
• Fast indexing and mature operations
• Logs, docs, tickets, web pages, PDFs, knowledge base content
• Search that users can understand and debug

Examples: Elasticsearch, OpenSearch, Solr, Azure Cognitive Search

Choose a vector DB if you need:

• Semantic retrieval over chunks of text
• Simpler setup for embeddings + ANN search
• High recall on vague/approximate queries
• Dedicated vector workflows and similarity search APIs

Examples: Pinecone, Weaviate, Milvus, pgvector, Qdrant

What matters most for RAG

For RAG, retrieval quality is usually driven by:

1. Chunking strategy
2. Embedding model quality
3. Hybrid retrieval
4. Filtering and reranking
5. Evaluation

The storage layer matters, but it’s not the whole game.

Practical recommendation for a mid-size company

If this is your first RAG app:

Start with a search engine that supports vectors or Postgres + pgvector if the scale is modest.

Why:

• Easier operationally
• Easier to combine keyword + vector retrieval
• Lower vendor sprawl
• Good enough for many mid-size use cases

If your corpus is large or retrieval is central:

Use a search engine for lexical + metadata filtering and add vector/hybrid support.

Why:

• Users often search with exact terms, names, IDs, product codes
• Semantic-only search misses these
• Hybrid tends to outperform pure vector search on enterprise content

If you expect heavy semantic workloads and less metadata complexity:

A dedicated vector DB can be a clean choice.

Why:

• Simple model for embeddings
• Often easier to optimize ANN retrieval
• Good if search is mostly “find similar stuff”

My default recommendation

For a mid-size company RAG app:

Pick a search engine with vector/hybrid support unless you have a strong reason to go vector-native.

That gives you:

• Lexical search for precision
• Vector search for semantic matching
• Filters for enterprise constraints
• One system to operate

Decision guide

Use search engine first if:

• You already have Elasticsearch/OpenSearch/Azure Search
• You need enterprise search features
• You care about auditability and filter-heavy queries
• Your users will search with names, codes, acronyms, and exact phrases

Use vector DB first if:

• Your corpus is mostly unstructured text
• Queries are conversational and fuzzy
• You want a simpler semantic retrieval layer
• You don’t need advanced text search features

Important warning

A pure vector approach often disappoints in enterprise RAG because:

• Exact terms matter a lot
• Acronyms and product names are critical
• Metadata filters are essential
• Users need deterministic retrieval in some cases

A common best architecture

1. Ingest docs
2. Chunk text
3. Store:
   • raw/chunk text
   • metadata
   • embeddings
4. Retrieve with:
   • keyword search
   • vector similarity
   • metadata filters
5. Rerank top results
6. Generate answer with citations

Bottom line

• Small to mid-size, first RAG app: search engine with vector support, or Postgres + pgvector
• Semantic-first workload: vector DB
• Enterprise production RAG: hybrid search is usually best

If you want, I can also give you:

1. a decision matrix by company size / scale / budget, or
2. a recommended stack for AWS / Azure / GCP / open-source.