Ollm
Freemium

What is Ollm?

Ollm, by Orgn, is the world's first confidential agentic stack designed for defense, finance, and regulated industries. It provides a private LLM gateway, AI IDE, agents, attestation, and sandboxes, ensuring verifiable execution across all workflows. Built by a team formerly at Platform 01/04β€”CDE, Ollm leverages TEE (Trusted Execution Environment) hardware-backed isolation for sensitive coding sessions, guaranteeing that your code is never used to train models. The stack includes four core components: Gateway, which offers over 250 LLMs through a single governed interface with policy-based model access control; Studio, providing policy gates for tools, agents, and users with traceable runs that map cleanly to operators and evidence; Scanner, delivering full-chain evidence across inference, agents, sandbox, and compute; and the confidential development environment itself, which ensures verifiable confidentiality as proof, not just a policy statement.

Key features include zero data retention (0 training), TEE and ZDR (Zero Data Retention) models routable through one confidential Gateway API, and an integrated CDE, Gateway, Studio, and Scanner in a single attested stack. Ollm is ideal for defense, government, and regulated teams that need to bring AI into sensitive environments without compromising security or compliance. Use cases range from factory floors to front lines, enabling secure AI development, deployment, and monitoring.

Technical details: Ollm uses prepaid credits with transparent markup, with every deduction itemized in real time by task and model. Pricing starts at $20 to begin, with a predictable $2,150/seat per month for agentic runtime based on hours, not token consumption. Each seat includes non-stop agentic runtime every month. Last updated in May 2026, Ollm is proudly made in the USA and built for the highest security standards.

Who is it for?

defense organizations, financial institutions, regulated industries, security teams, AI developers, compliance officers

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