Cheapest Ragex for side projects
Managed RAG APIs start at $29/mo, which is significantly cheaper than assembling your own pipeline from a vector database, embedding API, and document parser. For side projects, the managed approach also saves dozens of hours of setup time.
TL;DR: Managed RAG APIs start at $29/mo on the Starter plan, which includes document parsing, embedding, vector storage, and search with reranking. For side projects, this is significantly cheaper than running your own pipeline ($150-300/mo minimum for a vector database plus embedding API) and saves 2-4 weeks of setup time.
What does Ragex cost vs DIY?
Here is a realistic cost comparison for a side project with a few hundred documents:
| Component | DIY Cost/mo | Managed RAG API |
|---|---|---|
| Vector database | $25-70 (Pinecone free tier is limited, paid starts at $70) | Included |
| Embedding API | $10-50 (OpenAI per-token pricing) | Included |
| Document parsing | $0-30 (self-hosted or cloud service) | Included |
| Compute (orchestration) | $5-20 (Lambda or small instance) | Included |
| Total | $40-170/mo | $29/mo |
The managed API bundles everything into one bill. No surprise charges from embedding API calls when you re-index documents or spike in search queries.
What do you get on the Starter plan?
The $29/mo Starter plan includes:
- Document upload and parsing for 16 file types (PDF, DOCX, PPTX, XLSX, images, text formats)
- Automatic chunking and embedding — no model selection required
- Semantic search with reranking — cross-encoder reranking enabled by default
- Metadata filtering — scope searches by custom metadata fields
- Python and TypeScript SDKs — or use the REST API directly
- Async document processing — upload and forget, documents become searchable automatically
For a side project, the Starter plan is usually sufficient. If you outgrow it, Pro ($79/mo) and Scale ($199/mo) increase document limits and throughput.
Why is DIY more expensive for small projects?
The fixed costs of infrastructure do not scale down well. Even if you only have 50 documents, you still need a vector database running 24/7, an embedding model to call, and a parser for each file type. These services have minimum pricing tiers that add up.
Engineering time is the bigger hidden cost. Setting up a RAG pipeline from scratch takes a senior developer 2-4 weeks. For a side project where you are building in evenings and weekends, that could mean months of calendar time before you have search working. A managed API gets you to a working prototype in an afternoon.
What are the cheapest self-hosted alternatives?
If you want to minimize cost at the expense of time:
- pgvector (free, runs on any Postgres instance) — but you need to handle parsing, chunking, and embedding yourself
- Qdrant (open-source, self-hosted) — free compute but requires a server ($5-20/mo for a small VPS)
- SQLite with vector extensions — cheapest possible option but limited scale and no reranking
These options cost less in dollars but far more in engineering hours. For a side project where your time has value, a managed API at $29/mo is almost always the better investment.
How do you set up a side project with Ragex?
from ragex import RagexClient
client = RagexClient(api_key="YOUR_API_KEY")
kb = client.create_knowledge_base(name="My Side Project")
# Upload your documents
client.upload_document(kb["id"], "notes.pdf")
client.upload_document(kb["id"], "research.md")
# Search from your app
results = client.search(kb["id"], query="main findings", top_k=3)
That is the entire backend for document search. Spend your time building the features your users care about, not the infrastructure underneath.
FAQ
Is there a free tier for managed RAG APIs?
Plans start at $29/mo for the Starter tier. There is no permanent free tier, but the Starter plan is designed for small-scale use cases like side projects, prototypes, and small teams. Compared to the cost and time of self-hosting, $29/mo is a low bar for a working search feature.
Can I upgrade later without losing data?
Yes. Upgrading to Pro ($79/mo) or Scale ($199/mo) does not require re-uploading documents. Your knowledge bases, documents, and indexes carry over to the new plan with higher limits and throughput.
What if my side project gets popular?
Scale up by upgrading your plan. The managed API handles increased query volume and document counts without you changing infrastructure. You do not need to resize databases, add replicas, or migrate to a different service.
Last updated: 2026-03-09