Building an Ultra-High Throughput AI-SQL Engine
Back to blog Building an Ultra-High Throughput AI-SQL Engine Sep 24, 2026 Shreya Shankar, Charles Frye, Fergus Finn, Arnav Dhariya, Joseph Barrow, Meryem Arik TL;DR: AI functions in SQL, and fast LLM-powered classifiers in general, are having their day in the sun. But they typically rely on costly, closed LLM APIs. We’re building Quail, the QUery-Aware Inference Layer, to jointly optimize query planning and model inference for open-weight models.
- ▪Back to blog Building an Ultra-High Throughput AI-SQL Engine Sep 24, 2026 Shreya Shankar, Charles Frye, Fergus Finn, Arnav Dhariya, Joseph Barrow, Meryem Arik TL;DR: AI functions in SQL, and fast LLM-powered classifiers in general, are havi
- ▪But they typically rely on costly, closed LLM APIs.
- ▪We’re building Quail, the QUery-Aware Inference Layer, to jointly optimize query planning and model inference for open-weight models.
Hacker News (AI / LLM) files mainly under ai. We currently carry 6,375 of its stories.
Story provenance
Source · retrieval · rights · ranking — open for full record
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Story provenance
Attribution is not the same as permission. This drawer separates discovery metadata, excerpts, WeSearch-generated summaries, reuse status, and whether the publisher receives the visit. Nothing here claims a legal grant the publisher has not made.
Record
| Original publisher | Full Stack Data Lab |
| Canonical URL | https://fsdatalab.github.io/blog/introducing-quail/ |
| Publication time | Thu, 24 Sep 2026 22:56:37 +0000 |
| Retrieval time | 2026-09-24T23:10:27.182Z |
| Last seen | 2026-09-24T23:10:27.182Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | H-Jcey1IejFZ · 1 stories |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
WeSearch handling by dimension
| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
Back to blog Building an Ultra-High Throughput AI-SQL Engine Sep 24, 2026 Shreya Shankar, Charles Frye, Fergus Finn, Arnav Dhariya, Joseph Barrow, Meryem Arik TL;DR: AI functions in SQL, and fast LLM-powered classifiers in general, are having their day in the sun. But they typically rely on costly, closed LLM APIs. We’re building Quail, the QUery-Aware Inference Layer, to jointly optimize query planning and model inference for open-weight models. Across 29 QUAIL-B queries, Quail is 1.84x faster on average than well-tuned vLLM baselines — up to 14x! Star us on GitHub, and try it out in our live demo! Contents AI-SQL makes unstructured data useful, but it is expensive. Key Idea: Query plans should control LLM inference! We built Quail to run AI-SQL queries faster.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at Full Stack Data Lab.
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