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Enterprise Layer Intelligence
Categories/AI Hosting & Inference/Tinfoil
Tinfoil

Tinfoil

Founded by Tanya Verma; Jules Drean; Sacha Servan-Schreiber; Nate Sales in 2024

Run AI models and containers privately inside secure hardware enclaves

Pricing model

Paid

Rating

★ People love it

Time to value

Quick Setup (< 1 hour for basic chat or inference API)

You can use Tinfoil to run AI models and workloads inside secure hardware enclaves that keep your data private. It provides an OpenAI-compatible inference API, a private AI chat client for browser and iOS, and container hosting for running custom Docker images. Data is encrypted directly to the hardware enclave, preventing third parties and hosting servers from viewing prompt inputs or outputs. Hardware attestation cryptographically verifies the environment, allowing developers and enterprises in regulated industries to process sensitive data without compromising confidentiality.

What Tinfoil does

Deploy Docker containers into confidential hardware enclavesQuery open-source AI models using an OpenAI-compatible endpointVerify enclave hardware attestation before sending sensitive dataChat with AI assistants on iOS and web without saving unencrypted logsProcess confidential enterprise workloads in isolated GPU memoryAudit infrastructure security using open-source attestation toolsHardware attestation to cryptographically verify isolated compute environmentsDirect client data encryption directly into secure hardware enclavesOpenAI-compatible private inference API for quick drops into existing codebasesPrivate container execution for any custom Docker imageBrowser and iOS private AI chat interfaceMulti-GPU confidential computing support for open models

Pricing breakdown

Pricing model: Paid

PlanList price
Private Chat$20 / mo

Tutorials & Demos

Frequently asked

DockerDockerOpenAIOpenAI

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Tinfoil, private AI, confidential computing, secure enclaves, private inference API, hardware attestation, Docker containers, confidential GPU, encrypted cloud compute, OpenAI compatible, LLM privacy, data security, Llama, confidential inference