Cached tokens

1,000,000

Cost per request

$0.85

Stop paying models to reprocess the same context

Tensormesh caches repeated prompts, documents, tools, and workflow context, helping your AI apps run faster and cheaper with $0 cached tokens.

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Trusted by teams building AI at scale
Benefits

Scale applications without repeating the same work

Lower cost per request

Reduce the cost of context-heavy requests by turning repeated inputs into cached tokens your app can reuse for free.

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Faster recurring workflows

Accelerate recurring workflows that reuse the same context across documents, agents, and long-running tasks.

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Plug into your existing stack

Add caching to your AI app without changing the user experience or rebuilding around a new framework.

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Deployment

Deploy your way

Serverless
Reserved

Serverless

Serverless Inference
The fastest way to deploy open-source models with no clusters, no capacity planning, and no long-term commitments
Free Cached Context
Reuse repeated context without paying for cached input tokens.
OpenAI-Compatible API
Connect existing apps with minimal code changes.
Serverless Inference
The fastest way to deploy open-source models on demand. No clusters, no capacity planning, and no long-term commitments.
Free Cached Context
Reuse repeated context without paying for cached input tokens.
OpenAI-Compatible API
Connect existing apps with minimal code changes.

Reserved

Reserved Model Inference
Run production models on reserved capacity with predictable performance and throughput.
Dedicated Container Inference
Bring custom inference stacks for agents, RAG, and long-context AI applications.
Accelerated Compute
Get dedicated capacity for high-volume workloads that need reliability, control, and support.
Dedicated Model Inference
Run models on dedicated capacity with greater control.
Dedicated Container Inference
Bring custom containers and inference stacks to production.
Accelerated Compute
Scale GPU infrastructure for demanding AI workloads, from single deployments to high-performance clusters.
Integrations

Works with the models you already use

Tensormesh supports open-weight models and popular inference engines, allowing teams to cache repeated context where their workloads already run.

Browse our models

How it works

Tensormesh caches context your app sends repeatedly, then reuses it on future requests.

See how context caching works

Reviews

Trusted by teams scaling AI workloads

Enterprises everywhere are wrestling with the huge costs of AI inference, Tensormesh’s approach delivers a fundamental breakthrough in efficiency and is poised to become essential infrastructure for any company betting on AI.

Ion Stoica

Co-Founder, Databricks

Tensormesh enabled distributed KV-cache sharing across servers—delivering performance that exceeded expectations.

Rowan T.

CEO

The LMCache team rapidly adapts and delivers results that stabilize and optimize model hosting. It’s a major step forward for enterprise LLM performance.

Prashant P.

Software Engineer

Our collaboration with LMCache accelerated our GDS open-source release and achieved a 41× reduction in time-to-first-token—transforming large-scale AI economics.

Callan F.

Product Lead

We’ve seen major LLM efficiency and cost savings using the vLLM Production Stack from Tensormesh’s founders.

Ido B.

CEO
Blog & News

Explore latest news & insights

News
Articles
July 23, 2026

Tensormesh and AMD Collaborate to Empower Fewer GPUs to Serve More Models

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May 27, 2026

Tensormesh Raises $20M from Investors Including AMD Ventures, CoreWeave, NVentures, Launches Tensormesh Inference to Fix AI’s Most Expensive Problem

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October 23, 2025

Tensormesh Emerges From Stealth to Slash AI Inference Costs and Latency by up to 10x

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July 30, 2026

Why LLM Inference Is a Data Problem, Not Just a Compute Problem

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July 1, 2026

Designing AI Infrastructure Products for Developers

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June 24, 2026

Persistent KV Cache: Own Your Context Caching Lifecycle

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Make repeated context work for you

Test your workload, measure the savings, and see how much cached-token pricing can reduce your bill.

Talk to an engineer

Have questions about our billing formula?

Read the Docs

*Results may vary based on specific client infrastructure, workload characteristics, and hardware configuration. The 10x performance claim represents peak optimization in controlled environments.
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