llm-app vs paradedb
Two of the top projects in RAG and search, side by side: who leads on each score part and who each one suits, then setup, license, activity and what each review found.
llm-app
YAML-configured RAG server that keeps a vector database synced with a document folder
paradedb
Postgres extension adding BM25 full-text, vector and hybrid search
Verdict
llm-app ranks higher, 82 to 76. Of the 4 score parts we measure for both, llm-app leads on 1, paradedb on 1 and 2 are even.
- Adoptionllm-app leads by 31 points, 98 to 67: a wide gap.
- FreshnessEven at 100.
- Maintenanceparadedb leads by 12 points, 87 to 75.
- Agent-readyEven at 0.
Pick llm-app if you are
Teams wanting live, cited Q&A over a document folder without writing code
- Edits, additions and deletions propagate to the vector store as real upserts and deletes
- 8 backends: DuckDB (default, embedded), pgvector, Qdrant, Milvus, ChromaDB, Weaviate, Pinecone, MongoDB Atlas
Pick paradedb if you are
Teams adding search and vector retrieval to an existing Postgres
- Search and application data share one Postgres with ACID transactions
- Covers BM25, vector and hybrid search, facets and aggregates in SQL
| What we compare | llm-app | paradedb |
|---|---|---|
| Score parts, out of 100 | ||
| Adoption | 98, widely used | 67, popular |
| Freshness | 100, active | 100, active |
| Maintenance | 75, fair | 87, healthy |
| Agent-ready | 0, none | 0, none |
| Facts from GitHub and the README | ||
| Stars | 58.7k | 9.5k |
| License | MIT (permissive) | AGPL-3.0 (copyleft) |
| Last commit | Nov 2026 | Nov 2026 |
| Last release | Nov 2026 | Nov 2026 |
| Language | Python | Rust |
| Docker | No | Yes |
| GPU | Optional | Not needed |
| arm64 or Apple Silicon | Not stated | Mentioned |
llm-app
serviette watches a folder or remote source (Google Drive, S3/MinIO, SharePoint, FTP/SFTP/WebDAV), parses and embeds documents with Pathway, and writes them to one of 8 vector databases. A separate stateless server hosts a web chat UI on / and a REST API under /api/v1 on port 8989, answering with cited sources. Edits and deletions in the source become upserts and deletes in the store within seconds.
Who it is for: Teams wanting live, cited Q&A over a document folder without writing code
Strengths
- Edits, additions and deletions propagate to the vector store as real upserts and deletes
- 8 backends: DuckDB (default, embedded), pgvector, Qdrant, Milvus, ChromaDB, Weaviate, Pinecone, MongoDB Atlas
- Indexer and server are separate processes sharing only the database; server scales horizontally
- Published benchmarks: 50 GB of text indexed at 14.65 GiB peak PSS, plus FRAMES retrieval results
Weaknesses
- Requires a Pathway license key, free but obtained via GitHub or LinkedIn sign-in
- No conversation memory or user accounts; answers one question at a time
- Default DuckDB backend is single-writer, so it suits laptops and demos only
- Local embeddings pull in PyTorch (about 0.8 GB for the CPU build)
- Python, OpenAI, LiteLLM, SentenceTransformers, Gemini
- Needs Pathway license key, Python with pip 23.1+, OpenAI API key (optional, for generated answers), Vector database (embedded DuckDB by default)
paradedb
ParadeDB is a Rust Postgres extension that adds a custom index for BM25 full-text search, vector search, filters, facets, aggregations and joins. Search runs inside Postgres with standard SQL and ACID transactions, so no separate search engine is needed. It builds on pgrx, Tantivy and Apache DataFusion, and offers ORM integrations plus agent skills and MCP docs.
Who it is for: Teams adding search and vector retrieval to an existing Postgres
Strengths
- Search and application data share one Postgres with ACID transactions
- Covers BM25, vector and hybrid search, facets and aggregates in SQL
- ORM integrations for Drizzle, Django, SQLAlchemy, Rails and EF Core
- Built on Tantivy and DataFusion rather than a custom engine
Weaknesses
- Community edition is AGPL-3.0; Enterprise needs a commercial license
- Self-hosting Enterprise requires contacting sales
- No embedding or document ingestion pipeline included
- Prisma integration is not yet available
- Rust
- Needs PostgreSQL
- Docker
More in RAG and search
Building with these?
We help teams pick an open-source AI stack and run it in production. Client work never changes a rank.