Article URL: https://neon.com/blog/how-castform-neon-beats-frontier-models-on-price-and-efficiency Comments URL: https://news.ycombinator.com/item?id=49186762 Points: 106 # Comment…

A 4B open-source model post-trained with Castform retrieved search results as accurately as GPT-5.6 Sol, while costing 100x less “Most teams' best training data is just sitting in their databases. The problem is that turning raw data into something usable is hard, and letting agents read, search, and mutate data cheaply at scale requires advanced infra. Pointing Castform at Neon skips both.” Neon (Lakebase Postgres) and their new Search extensions solve the first; Castform solves the second. In ~2022, the industry was going all in on embedding search. Every database provider added one, and pgvector was Neon's most downloaded extension. To provide context to LLMs, engineers handcrafted RAG pipelines, which in essence, is some form of embedding similarity search. In ~2025, agents started to gain more traction. Developers started creating multi-hop search workflows, decomposing big problems into smaller ones. Retrieval has shifted from the one-shot search systems to agentic retrieval. Instead of issuing a single query, models plan and search multiple times in a loop. Every loop iteration meant another call to the frontier model, increasing the overall cost and latency per user request. Concretely, a typical multi-turn search request with gpt-5.6-sol takes >10s and costs ~$0.03 end-to-end, making it prohibitively slow and expensive. Meanwhile, small open-weights models are 100x cheaper. But, out of the box, their capabilities lag behind closed api models. RL post-training helps bridge this gap. On specific tasks like search, post-trained open-source models can match & beat frontier models while costing orders of magnitude less per request. That is why we built Castform: to enable developers to RL post-train models without having to deal with machine learning & gpu internals. The goal's to make post-training as approachable as prompt engineering.