Tensormesh provides an AI‑native caching layer that stores large language model KV‑cache entries outside GPU memory and replays them for inference requests with identical prefixes, cutting latency and GPU utilization. The service integrates via REST API, Python/Go SDKs, and CLI, and can be deployed on public GPU clouds or on‑prem Kubernetes clusters with real‑time observability and enterprise‑grade security controls.
Funding
$4.5M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Enterprises deploying large language models face high GPU consumption and latency because each inference request recomputes identical token‑level activations. Redundant computation inflates cloud‑GPU bills and limits the throughput of production LLM services.
Solution
Tensormesh delivers an AI‑native caching layer built on the LMCache engine that intercepts LLM inference calls, stores KV‑cache entries outside GPU VRAM, and replays them for subsequent requests with identical prefixes. By sharing these cached activations across queries, the platform cuts time‑to‑first‑token and overall latency by up to tenfold while halving GPU utilization. The service can be provisioned on public GPU clouds or on‑prem Kubernetes clusters in under five minutes and integrates through REST APIs, CLI tools, and language‑specific SDKs. A unified observability console reports cache‑hit rates, throughput, and cost‑savings, enabling operators to monitor performance in real time. The solution also includes cache‑aware request routing and optional SOC 2‑ready audit logging for enterprise compliance.
Target Audience
The primary customers are AI platform teams, enterprise SaaS providers, and large‑scale LLM application developers that run inference workloads on cloud or private GPU clusters and need to optimize cost and latency.
Features
- Distributed KV‑cache storage that persists outside GPU memory and is re‑hydrated on demand, eliminating redundant token computations.
- Cache‑aware routing engine that directs incoming prompts to the node holding the most relevant cache entries, improving hit probability.
- Full‑stack integration: REST API, Python/Go SDKs, and a CLI that mirror the web UI functionality for automated pipelines.
- Compatibility layer for leading inference servers (e.g., vLLM) and plug‑in hooks for custom model stacks.
- Real‑time metrics dashboard exposing cache‑hit ratio, latency reduction, GPU‑hour savings, and per‑model cost analytics.
- Enterprise deployment packages with Helm charts, K8s operators, and on‑prem isolation options, supporting SSO/RBAC and audit logs.
- Savings‑based billing engine that calculates charges from raw GPU usage plus a configurable share of realized cost reductions.
- Security controls including encrypted data transit, role‑based access, and optional SOC 2 audit logging.