Build Your Own RAG vs Lore DB
You have engineers. You have a vector database. You could build your own RAG system. Should you?
What you’d need to build
A production-grade knowledge base with AI search requires:
| Component | What it takes | Lore DB status |
|---|---|---|
| Document storage | Database, CRUD API, Markdown rendering | Done |
| Chunking pipeline | Markdown-aware splitting, overlap strategy, header preservation | Done |
| Embedding pipeline | Batch embedding, re-indexing on edit, model management | Done |
| Hybrid search | Vector similarity + keyword matching + freshness decay | Done |
| Cross-encoder reranking | Second-pass scoring for precision | Done |
| Query expansion | LLM-powered query rewriting for recall | Done |
| RAG pipeline | Context assembly, prompt engineering, source citations | Done |
| Document linking | Wiki syntax, link resolution, knowledge graph | Done |
| MCP server | OAuth, tool registration, stateless HTTP transport | Done |
| Auth & permissions | Google OAuth, per-library roles, per-user scoping | Done |
| Team collaboration | Invite links, role management, library sharing | Done |
| Web UI | Document editor, search, Ask AI, admin dashboard | Done |
| Billing | Stripe subscriptions, usage tracking, plan limits | Done |
| Deployment | Serverless, CDN, auto-scaling | Done |
| Testing | Unit tests, E2E tests, screenshot tests | Done |
Estimated build time for all of the above: 3-6 months of engineering. Estimated ongoing maintenance: 1-2 days/month (dependency updates, model changes, bug fixes).
When to build your own
- You have unique requirements that a SaaS product can’t meet (custom embedding models, on-premise deployment, regulatory constraints)
- You need to integrate deeply with internal systems (custom data sources, proprietary auth)
- You have dedicated infrastructure and ML teams to maintain it
- Building RAG is a core competency of your business
When to use Lore DB
- You want AI-powered team knowledge this week, not in 3 months
- Your team’s time is better spent shipping product, not building infrastructure
- You need MCP compatibility with Claude, Cursor, VS Code, ChatGPT, and Gemini
- You want hybrid search, freshness ranking, knowledge graph, and team collaboration out of the box
- You’d rather pay SEK 79/month than maintain a custom system
The math
| Build your own | Lore DB | |
|---|---|---|
| Engineering cost | 3-6 months (~$50-150K) | $0 |
| Monthly maintenance | 1-2 days/month (~$2-4K) | $0 |
| Monthly subscription | $0 | ~$7.50/user/month |
| Time to first value | Weeks to months | Under 2 minutes |
| MCP support | You build it | Included |
| Updates & improvements | You build them | Included |
What about vendor risk?
Fair concern. Here’s how Lore DB mitigates it:
- Your documents are Markdown. Universal format, works everywhere.
- Full JSON export. Download your entire library — titles, content, metadata. Re-importable.
- MCP is a standard protocol. Not a proprietary API. Any MCP-compatible tool works.
- No data lock-in. Export and leave anytime. We don’t hold your data hostage.
See our Data Portability guide for details.
Many teams start with Lore DB for speed, and only build custom when they’ve validated that RAG is valuable for their workflow. Lore DB’s export means you can migrate your content to a custom system later if needed.