YP

YPerf – Monitor LLM Inference API Performance

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YPerf – Monitor LLM Inference API Performance

Our team operates several real-time AI applications, where both latency(TTFT) and throughput(TPS) are critical to most of our users. Unfortunately, nearly all of the major LLM APIs lack consistent stability. To address this, I developed YPerf—a simple webpage designed to monitor the performance of inference APIs. I hope it helps you select better models and discover new trending ones as well. The data is sourced from OpenRouter, an excellent provider that aggregates LLM API services.

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Product HuntOn track for Day 1 leaderboard · Strong signals: model, user, new · Missing: mac, agents, macos
90%90% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released
57%57% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRFits verified-revenue profile · Strong signals: users · Missing: mobile apps, ios, personal
55%55% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting
48%48% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: users · Missing: plus, platform, intuitive
33%33% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
18%18% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
7%7% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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