Category
生成AI導入の意思決定・コスト（API vs 自前ホスティング／RAG vs ファインチューニング）ガイド
生成AIの導入は、モデルの賢さより『どこに・いくら投資するか』の意思決定で成否が分かれます。本クラスタは、API利用と自前ホスティングの損益分岐（稼働率が鍵）、RAGとファインチューニングの費用対効果（多くはまずRAG）、本番RAGが失敗する落とし穴（検索の質・アクセス制御・評価）、音声合成のコストとデータ主権——というAI導入の『買い手意図』の問いに答えます。自前GPUで量子化オープンモデルを本番運用し、RAGで専門商材の誤答を構造的に排除し、商用・自前の両TTSを運用した実績を根拠に、速く・安く・安全にAIを業務へ載せる判断材料を提供します。
4 articles in total
Foundational guide
Foundational guide (start here)
The cost and break-even of generative AI: a decision guide for API usage vs self-hosting
Should you use generative AI (LLM, voice, image) via a cloud API, or host an open model on your own GPU? From the buyer's perspective, it explains a decision framework that discerns the break-even point from usage volume, data sovereignty, regulation, and operating cost — from the real example of running a self-hosted GPU inference pipeline in production and estimation code with stated assumptions.
Related practical articles
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Why production RAG fails: the design that raises accuracy to practical quality, and what buyers should demand
Why does RAG (retrieval-augmented generation) that worked in a demo 'answer wrong, run slow, leak information' in production? It explains the typical pitfalls where naive RAG fails (search accuracy, chunking, reranking, evaluation, access control) and the design that raises it to practical quality, from the real example of a RAG voice-concierge that structurally eliminated wrong answers about specialized products and a hybrid-search implementation.
9 min read - 生成AIRAGファインチューニングコスト最適化発注
RAG vs fine-tuning: the cost-effectiveness of which to invest in, and the decision
When adapting generative AI to your business, which should you invest in — RAG (retrieval-augmented generation) or fine-tuning (additional training)? From the buyer's perspective, it explains the difference in the problems they solve, the cost-effectiveness, and the reasoning behind the conclusion 'RAG first in most cases,' from the real example of a RAG voice-concierge that structurally eliminated wrong answers about specialized products.
9 min read - 音声AI生成AIコスト最適化セルフホスト発注
Self-hosting speech synthesis (TTS) vs ElevenLabs: choose by cost, data sovereignty, and lock-in
Should you use speech synthesis (TTS) via a commercial API like ElevenLabs, or self-host an open model like Qwen3-TTS? It explains a decision framework based on per-character price, data sovereignty (on-prem requirements), voice-cloning consent, and vendor lock-in, from the real example of running both commercial and self-hosted TTS in production and a swappable provider-abstraction implementation.
9 min read