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NEAR AI

NEAR AI Cloud is private AI inference you can verify. Open-weight models run inside hardware-secured trusted execution environments (TEEs) built on Intel and NVIDIA confidential computing. Every response is signed inside the enclave and backed by a hardware attestation, so a caller can check what served their prompt rather than trusting a policy promise. The API is OpenAI-compatible. Pricing is per token with no minimums or subscriptions. NEAR AI is operated by Jasnah Inc., a Delaware corporatio

For agents: NEAR AI Cloud: OpenAI-compatible inference for open-weight models in TEEs with hardware attestation and signed responses; per-token pricing, no subscriptions.

private ai inferencetrusted execution environmentopen-weight modelsopenai-compatible apiattestationper-token pricing

Their llms.txt

The beginning of the file, from our validated copy saved 2026-10-23T08:53:15.213Z. Cached copy · Source on near.ai

# NEAR AI

> NEAR AI Cloud is private AI inference you can verify. Open-weight models run inside hardware-secured trusted execution environments (TEEs) built on Intel and NVIDIA confidential computing. Every response is signed inside the enclave and backed by a hardware attestation, so a caller can check what served their prompt rather than trusting a policy promise. The API is OpenAI-compatible. Pricing is per token with no minimums or subscriptions. NEAR AI is operated by Jasnah Inc., a Delaware corporation doing business as NEAR AI.

## What it is

- Private AI inference: open-weight models served inside Intel TDX and NVIDIA confidential-GPU enclaves, so prompts and completions are not readable by the host, the operator, or NEAR AI.
- Attestation: every response is signed inside the enclave and backed by a hardware attestation of the code and model that ran. Verification is documented and can be performed independently.
- OpenAI-compatible API: point an existing OpenAI client at the NEAR AI base URL. No new SDK.
- Not the same entity as NEAR Protocol, the layer-1 blockchain. NEAR AI is an AI infrastructure company.

## Pricing

- Per token, priced separately for input and output, quoted per 1,000,000 tokens. The rate depends on the model.
- No minimums, no subscriptions, no seat licences on pay-as-you-go.
- Private execution and attestation are included in the rate, not billed as an add-on.
- Enterprise agreements cover reserved GPU capacity, private models, volume rates and SLAs.
- Machine-readable rates: https://near.ai/pricing.md

## Key pages

- [Pricing](https://near.ai/pricing): per-model rates, generated from the live catalog.
- [Pricing in plain text](https://near.ai/pricing.md): the same rates without HTML.
- [Models](https://near.ai/models): the catalog, with context windows and pricing.
- [Blog](https://near.ai/blog): launches, engineering notes and customer stories.
- [Company](https://near.ai/company): who builds it, and open roles.
- [Brand](https://near.ai/brand): logos, colour and usage guidelines.

## Product and developer resources

- [Documentation](https://docs.near.ai/cloud/introduction): API reference and guides.
- [Attestation and verification](https://docs.near.ai/cloud/verification): how to check a response yourself.
- [Console](https://cloud.near.ai): create an account and an API key.

## Research

- [Research papers](https://near.ai/papers): the papers behind the platform, by NEAR AI.
- [Decentralized Confidential Machine Learning](https://near.ai/papers/decentralized-confidential-machine-learning.pdf) (Illia Polosukhin, Alex Skidanov and Pierre Le Guen, February 2025): Private and verifiable inference inside CPU and GPU trusted execution environments, and a way to make open-source models pay their developers.
- [Proof of Response](https://near.ai/papers/proof-of-response.pdf) (Illia Polosukhin and Alex Skidanov, February 2025, arXiv:2502.10637): Request data from a service in a network and get a signed answer within
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Machine-readable: GET /v1/resolve?q=near.ai · GET /v1/domain/near.ai · this page as Accept: application/json or text/markdown · MCP lookup_domain

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Claim my listing on Agent Web Map for near.ai:
1. POST https://agentwebmap.com/v1/sites/near.ai/claims and keep claim_id and claim_secret private.
2. Publish the proof: add the line "AgentWebMap ownership: <token>" (token from step 1) to https://near.ai/llms.txt, or use the DNS, meta-tag or well-known method the response describes.
3. POST https://agentwebmap.com/v1/sites/near.ai/verify with {"claim_id": "...", "claim_secret": "..."}.
4. Save the api_key from the answer; it manages the listing (PATCH https://agentwebmap.com/v1/sites/near.ai).
MCP equivalent: tools begin_ownership_claim, then verify_domain, at https://agentwebmap.com/mcp. Docs: https://agentwebmap.com/docs#verify

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