WhatA tool that estimates the true monthly cost of running open-source models locally versus cloud APIs, factoring in hardware, energy, ops labor, and quality tradeoffs for specific workloads.
SignalThere is a growing sentiment that frontier model pricing will keep rising as subsidies end and providers seek profitability, pushing serious users to evaluate self-hosted open-source alternatives, but nobody can easily calculate whether local deployment actually saves money for their use case.
Why NowOpen-source models from DeepSeek, Alibaba, and Meta are approaching frontier quality while cloud providers are simultaneously raising prices and hiding chain-of-thought tokens, creating a concrete inflection point for build-vs-buy decisions.
MarketCTOs and platform engineering teams at companies spending $100K+/year on LLM APIs; TAM is the broader FinOps market for AI ($3B+); no competitor provides an apples-to-apples local-vs-cloud comparison.
MoatContinuously updated hardware pricing data and real-world inference benchmarks across model families create a trusted reference dataset.
Anonymous request-token comparisons from Opus 4.6 and Opus 4.7View discussion ↗ · Article ↗ · 559 pts · April 18, 2026
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