Tensormesh helps developers build faster, lower-cost AI applications by caching and reusing repeated context across requests. For agents, RAG systems, copilots, and multi-turn assistants, the same instructions, documents, tools, and conversation history often get processed again and again. Tensormesh uses KV caching to reuse that work automatically, reducing redundant computation and improving response latency.Documentation Index
Fetch the complete documentation index at: https://docs.tensormesh.ai/llms.txt
Use this file to discover all available pages before exploring further.
What You Can Do
Reduce API Costs
Avoid paying to process the same context again and again. Tensormesh reuses cached context across repeated requests — cached tokens are always $0.
Speed Up Agent Responses
Skip redundant prefill work so agents, copilots, and RAG apps can respond faster. Cached prefixes return instantly with no recomputation.
Build With Less Infrastructure
Start with serverless APIs for instant access with no setup. Move to reserved GPU clusters when you need dedicated capacity or compliance requirements.
Core Technology: KV Caching
Tensormesh is built on KV caching. Instead of recomputing repeated prompts, documents, tools, and conversation history, Tensormesh stores reusable model state and serves it again when similar context appears. That means less GPU waste, faster responses, and more efficient AI applications at scale.| What happens | What you pay | |
|---|---|---|
| First request | Input tokens are computed and stored in the KV cache | Standard input token rate |
| Subsequent requests | Matching tokens are served from cache, no recomputation | $0.00 per cached token |
Coding Agents
Tensormesh powers some of the most capable open-weight coding agents available. Use any serverless model as the backend for Claude Code or Codex CLI:Claude Code
Run Claude Code backed by Tensormesh serverless inference. Point it at any supported open-weight model and start building — no changes to your existing Claude Code workflow.
Codex CLI
Drive open-weight models with OpenAI Codex CLI using Tensormesh as the inference provider. OpenAI-compatible API means zero configuration changes.
Explore the Docs
Serverless Inference
Model catalog, per-token pricing, and quickstart examples.
Reserved Deployments
When to use dedicated capacity and how to request a cluster.
External Storage
Plan tiers, usage monitoring, and how persistent caching affects your costs.
Pricing Overview
How token pricing works and how to structure prompts to maximize cache hits.
Serverless Usage
Per-model token counts, cache hit rates, and cost breakdown.
Cache Savings
Dollar value of your KV cache hits over time.