# Show HN: HRAG – Hybrid RAG on €116/month of Hetzner, officially benchmarked > Source: > Published: 2026-08-19 17:36:01+00:00 [Read Part 1 ↗](https://medium.com/@victormocioiu/production-grade-hybrid-search-rag-on-116-month-of-compute-part-1-the-pipeline-the-deede53d461d)· [GitHub](https://github.com/victormocioiu/rag-api) # hRAG Hybrid retrieval with receipts. **#9** [on EnterpriseRAG-Bench — officially scored](https://huggingface.co/spaces/onyx-dot-app/EnterpriseRAG-Bench-Leaderboard) **512K** docs benchmarked **88ms** BM25 at 2M chunks **€116** a month of Hetzner… yes, that is the whole cluster 00 · 01 ## The platform, answering actual screenshots of the live thing… the 512K-document playground, streamed answers, citations you can open. no mockups — mockups would have been easier. 01 · 02 ## What runs under the hood - BM25 inside PostgresNo search cluster to babysit. pg_textsearch gives real IDF ranking with Block-Max WAND: 88ms over 2 million chunks, where plain tsquery took 7.5 seconds. - Hybrid fusion, weightedFusion is not a democracy. Vector and lexical arms fuse with a measured 0.3 vector weight — equal weighting scored worse than turning one arm off. - Cross-encoder rerankerThe right eight chunks. A fourth service reads query and chunk together and reorders the window. Worth +3.9 on the public benchmark… and 16 seconds of latency on 2 vCPUs, which is why it ships as a checkbox and not a default. Both numbers are published. - Grounded answersRefusal as a feature. Empty retrieval refuses without calling the model. On the benchmark's info-not-found questions: 100% correct while others hallucinate. - Tenants by row-level securityIsolation is the floor, not a filter. Every query runs inside a tenant transaction Postgres enforces. The benchmark corpus and your sandbox are neighbors that cannot meet. - Streaming citations[n] means something. Sources arrive before the first token; every bracket in an answer opens to the exact chunk that backs it. 02 · 03 ## How the numbers stay honest - 1 **Measure before building** The reranker cost $1 to prototype offline before a line of service code existed. - 2 **Publish the noes** Multi-query rewriting, wider context, prompt-only fixes, HyDE — measured, failed, documented next to the wins. The noes cost real money; hiding them would waste it twice. - 3 **One database** Vectors, BM25, text, tenants, and usage all live in Postgres. One thing to operate, one thing to trust. - 4 **Small models, priced honestly** A 118M embedder and budget answerers, benchmarked against clouds that cost 100× more. 03 · 04 ## What it costs - Five-node Kubernetes cluster on Hetzner - Postgres with vectors, BM25, and row-level security - Four services: ingest, embed, rerank, answer - A public benchmark score you can reproduce - All code MIT, all numbers receipted €116 Answers cost tenths of a cent; the benchmark that proved them cost about $60 once. [Try it now — no login](/app) The 512K-document playground is open to everyone, no login. Sign in with Google or GitHub only for your own private sandbox. per month — the entire five-node cluster 04 · 05 ## Fair questions - What am I actually chatting with? - EnterpriseRAG-Bench: 512,000 simulated company documents — Slack threads, emails, wikis, tickets… the same corpus the platform is publicly scored on. what you see is exactly what the benchmark saw. - Can I bring my own documents? - Yes — sign in with Google or GitHub and you get a private sandbox: 10 documents, 20 pages each, a daily token budget, and meters that show exactly what's left. A page is counted in tokens, so no… one huge .txt file is not "one page", we checked. Row-level security keeps your docs yours. - How good is it, honestly? - Officially scored on EnterpriseRAG-Bench: **#9**, overall 44.74 (correctness 52.6, completeness 54.38, document recall 69.65) — ahead of Vertex AI Search and NVIDIA, one spot below Azure. On document recall we beat both Azure (64.25) and Vertex (61.76): the retrieval competes with the managed clouds outright, on a €116/month box. The[live leaderboard is on Hugging Face](https://huggingface.co/spaces/onyx-dot-app/EnterpriseRAG-Bench-Leaderboard); per-category tables are in the repo. - Why should I trust these numbers? - You shouldn't — not on our word alone. Every run's raw results are committed to the repo, and when the benchmark team re-scored our submission with their own judge, their recall landed within 0.05 points of ours (69.65 vs 69.6). Retrieval validated to a rounding error. Verify, then trust. - Can I run this myself? - That is the whole point… the articles walk through every deployment click and every benchmark, and the repos build the platform from scratch. Copy freely, measure everything.