OLLM.COM
Orgn (orgn.dev, Inc.) · Coding · Other
OLLM.COM is a confidential AI gateway from Orgn built for teams that can't afford to leak code, prompts, or task context. It's a private LLM gateway that puts hundreds of large language models behind one governed API. Then it wraps that access in confidential computing: trusted execution environments, encrypted sandboxes, and attestation that proves where each workload ran. Defense contractors, banks, healthcare groups, and other regulated teams reach for this kind of secure AI gateway when public model endpoints are simply off the table.
About OLLM.COM
What Is OLLM.COM
OLLM.COM is an enterprise AI gateway that connects your applications to hundreds of models through a single interface. The difference is what surrounds that access. This is confidential computing AI in practice. Every request runs inside isolated, hardware-protected environments, so prompts, code, and task context stay contained instead of passing through a shared public endpoint. It's an AI gateway for regulated teams first and a convenience product second.
Orgn built the platform around a specific worry. Regulated teams want modern AI models, but they can't send sensitive data to a vendor that might log it, train on it, or hand it over on request. The company states plainly that your code is never used to train any model. Workloads can also run in private or air-gapped setups.
That promise has a cost. This isn't a weekend tool for a solo developer. Pricing is enterprise-shaped, the strongest controls (SSO, RBAC, attestation exports) sit behind an annual seat plan, and self-serve users pay a markup on every model call. You're buying containment, not the cheapest token on the market. Is that trade worth it? For a team that can't legally touch a public endpoint, yes.
Getting Started
- Request a demo or start self-serve with prepaid credits on the Orgn site.
- Pick the surface you need: Gateway for model access, CDE for AI-assisted coding, or the Agents SDK for custom agents.
- Connect your models and providers, then set which models, users, and workflows each environment is allowed to touch.
- Run a task in a sandbox and pull the attestation record to confirm where it executed.
- Wire the gateway into your app and monitor usage, cost, and audit logs from the admin controls.
Product Information
A quick look at OLLM.COM's pricing, supported platforms, and performance.
Best for
The users, tasks, and scenarios where this tool fits best.
Users
- Security and platform engineers at regulated companies
- Defense, finance, and healthcare teams
- Developers who want AI coding help on sensitive repositories
Tasks
- Centralizing model access under one policy
- Running untrusted or experimental code
- Proving where a workload ran
Scenarios
- A bank piloting an AI assistant over internal documents without exposing them to a third party.
- A government contractor summarizing classified-adjacent material in a restricted or air-gapped deployment.
- A healthcare team testing an agent that touches patient-adjacent data under strict policy controls.
- A security team that has to answer "did our data train someone's model?" with a documented no.
Key features
Confidential AI Gateway
Gateway is the core of the platform. It gives one interface to hundreds of models with routing, observability, and policy controls baked in. For sensitive workloads, the point isn't raw model choice. It's that the access itself is governed and contained.
Confidential AI IDE (CDE)
CDE is an AI coding environment that keeps repositories, prompts, terminals, and execution context inside protected spaces. Developers get the usual AI assistance. The assistant never sees more than the environment allows. It's aimed at teams that block cloud coding tools outright.
Agents SDK
The Agents SDK lets you build agents with private execution, controlled tool access, secure data flows, and auditability. You decide what each agent can reach. The platform keeps a record of what it did along the way.
Sandboxed Execution
Every task can run in its own isolated environment. That matters most when you're running generated code or connecting an agent to operations you don't fully trust. A bad run stays boxed in. It can't reach everything else.
Attestation and Audit Trails
Scanner produces attestation and audit records across inference, agents, sandboxed compute, and gateway traffic. Teams get evidence of where a workload executed, which is often the difference between passing a compliance review and repeating it.
Confidential Computing Under the Hood
The platform leans on trusted execution environments, confidential GPU infrastructure, and encrypted sandboxes. This is the machinery behind the privacy claims. It's why the offering reads as infrastructure rather than a thin API wrapper.
Enterprise Controls
Admins manage team membership, usage policy, model access, deployment configuration, and audit requirements from one place. Enterprise deployments can also support private, restricted, and air-gapped setups for classified or sovereign needs.
Pros and cons
Pros
- One governed API for hundreds of models cuts the sprawl of separate vendor keys and billing.
- Confidential computing and encrypted sandboxes give regulated teams a way to use modern models without shipping data out.
- Attestation and audit trails provide concrete evidence of where workloads ran, which helps with compliance.
- Private and air-gapped deployment options cover genuinely sensitive environments.
- Transparent 10% markup with itemized deductions per task and model makes costs easier to justify.
Cons
- There's no free tier, so you can't test the platform without committing credits.
- The strongest admin and attestation features sit behind a $2,400 per-seat annual plan, which rules out small teams.
- Self-serve pricing adds a markup on every model call, so it won't be the cheapest way to run high-volume inference.
Frequently asked questions
No. The platform is designed so code, prompts, and task context stay inside isolated, hardware-protected environments, and the company states your code is never used to train any model.
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