WhatA developer tool that continuously monitors and reports real LLM inference performance broken down by prefill speed, decode speed, and context-length degradation curves — the metrics that actually determine user experience.
SignalExperienced practitioners are frustrated that headline token-per-second numbers are misleading because they hide critical distinctions between prefill and decode speeds, and performance degrades significantly as context windows grow — yet no tooling surfaces this clearly.
Why NowLong-context models (100K+ tokens) and thinking/reasoning models that generate thousands of internal tokens before responding have made the gap between advertised and real-world speed painfully obvious.
MarketAI engineering teams and companies running LLM workloads in production; TAM tied to the observability-for-AI market ($1B+ and growing fast); competitors like LangSmith and Helicone track latency but don't decompose it into prefill/decode/context-degradation curves.
MoatDeep integration into inference pipelines creates switching costs, and accumulated performance profiles across models and hardware become a proprietary benchmarking dataset.
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