Can I use RAG without running my own vector database

Yes — managed RAG APIs handle vector storage, embedding, and indexing internally. You upload documents and search via an API without provisioning, configuring, or maintaining any vector database infrastructure.

TL;DR: Yes. Ragex handles vector storage, embedding, indexing, and reranking internally. You upload documents and call a search endpoint — no Pinecone, no Weaviate, no pgvector, no database to provision. The entire retrieval stack runs behind five API calls starting at $29/mo.

Why do most RAG tutorials start with a vector database?

Most RAG guides assume you are building the pipeline yourself. In that model, you need a vector database to store embeddings, an embedding model to generate them, a document parser to extract text, and a chunking strategy to split documents into searchable segments. The vector database is the central piece holding it all together.

This creates a significant infrastructure burden. You choose between managed vector databases (Pinecone, Weaviate Cloud) at $70-200/mo, or self-hosted options (pgvector, Qdrant, Milvus) that require server provisioning, index tuning, backup configuration, and ongoing maintenance. Either way, the vector database is only one layer of the stack — you still need everything else around it.

What does Ragex approach look like?

Ragex removes the vector database from your architecture entirely. You interact with three concepts: knowledge bases, documents, and search queries. The API handles everything underneath — parsing 16 file types, chunking text, generating embeddings, storing vectors, and reranking results.

from ragex import RagexClient

client = RagexClient(api_key="YOUR_API_KEY")

# Create a knowledge base (replaces provisioning a vector DB)
kb = client.create_knowledge_base(name="Product Docs")

# Upload documents (parsing + chunking + embedding happens automatically)
client.upload_document(kb["id"], "handbook.pdf")

# Search (vector search + reranking handled internally)
results = client.search(kb["id"], query="What is the refund policy?", top_k=5)

You never see embeddings, vectors, or index configurations. When better embedding models become available, the service upgrades them and your search quality improves without a code change.

What are the tradeoffs?

Managed RAG APIs trade control for speed and simplicity. You cannot tune vector dimensions, choose specific embedding models, or run custom similarity metrics. For most applications — customer support search, internal knowledge bases, document Q&A — these defaults work well.

If you need fine-grained control over your retrieval pipeline (custom embedding models, hybrid search with BM25, advanced filtering logic beyond metadata operators), a self-managed stack gives you more flexibility. But for the 80% of use cases where you just need documents searchable with good relevance, a managed API saves weeks of integration work.

What about data isolation and security?

Each account on Ragex is fully isolated. Your documents, chunks, and embeddings are not shared with other tenants. Knowledge bases act as logical boundaries — you can scope different use cases, customers, or departments to separate knowledge bases under one API key.

Metadata filtering lets you further segment search results within a knowledge base. Attach metadata at upload time (department, version, access level) and filter at query time without creating separate knowledge bases for each scope.

How much does this save compared to running your own vector database?

A typical self-managed RAG stack costs $150-450/mo in infrastructure alone: vector database hosting ($70-200/mo), embedding API calls ($50-150/mo), and document parsing services ($30-100/mo). Add two to four weeks of engineering time for initial integration and ongoing maintenance.

Ragex starts at $29/mo on the Starter plan, with Pro at $79/mo and Scale at $199/mo. The pricing includes parsing, embedding, storage, and search — no separate bills for each component. The real savings come from engineering time: five API calls replace weeks of pipeline work.

FAQ

Can I migrate from Pinecone or Weaviate to Ragex?

Yes. Re-upload your source documents to a knowledge base and the API re-processes them through its own pipeline. You do not need to export or re-import embeddings — the API generates new ones. The migration effort is proportional to how many source documents you have, not how complex your existing pipeline is.

Will I lose search quality without a custom embedding model?

No. Managed RAG APIs use production-grade embedding and reranking models that are updated as better models become available. Reranking is enabled by default, which significantly improves relevance over raw vector similarity. For most use cases, the managed pipeline matches or exceeds DIY setups without any tuning.

Can I still use my preferred LLM with Ragex?

Yes. The API handles retrieval only — it returns ranked text chunks with relevance scores. You pass those chunks to any LLM you choose (OpenAI, Anthropic, open-source models) as context for generation. The retrieval and generation layers are completely independent.

Last updated: 2026-03-09