Introduction
Building Tensormesh
In this video, Tensormesh CEO and Co-Founder Junchen Jiang explains how model-level caching reduces repeated work, lowers AI costs, and creates a foundation for context-heavy production workloads.
Tensormesh was founded by the creators of LMCache to bring production-grade context caching to AI teams. We help developers reduce repeated model work, lower costs, and scale workloads that reuse context across requests.
In this video, Tensormesh CEO and Co-Founder Junchen Jiang explains how model-level caching reduces repeated work, lowers AI costs, and creates a foundation for context-heavy production workloads.
AI should scale by reusing work, not repeating it. We help teams spend less on redundant context and more on building better products.
We turn research into infrastructure teams can actually use. Tensormesh builds on LMCache to bring production-grade context caching to real AI workloads.
Customers should know exactly what they pay for and what they save. We make caching, pricing, and performance measurable.
Context-heavy AI is a systems problem. We design for the full path from request to cache to model response.
