WhatA platform that helps engineering teams right-size their local vs. cloud LLM usage by analyzing actual workload patterns, token volumes, and privacy requirements to minimize total cost of inference.
SignalThe debate over local vs. cloud LLM economics is riddled with flawed analyses — people are comparing apples to oranges by not accounting for amortized hardware costs, actual utilization patterns, or the difference between input and output token economics.
Why NowLocal inference on Apple Silicon and consumer GPUs has just become viable enough that enterprises face a real hybrid decision for the first time, but lack tooling to make it data-driven.
MarketEngineering teams and enterprises running LLM workloads; TAM overlaps with the $5B+ AI infrastructure management market; competitors like Portkey and Helicone focus on cloud observability but ignore the local inference side entirely.
MoatAccumulating real-world cost and performance benchmarks across hardware configurations creates a proprietary dataset that improves recommendations over time.
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