Your AI runs on your hardware. Not ours. Not anyone's.
- Open-weight models on your own servers.
- No data leaves your building.
- No usage-based vendor bill.
Self-hosted deployment. Your hardware, your network, your data.
- HIPAA
- PHIPA
- PIPEDA
- OSFI B-10/B-13
- SOC 2
- PCI DSS
Designed to support these obligations. Validate with your own auditor.
Perplexity runs DeepSeek R1 on its own servers in US and EU data centres. User data never leaves Western servers.
Source as of January 2025
Pinterest cut AI costs roughly 90 percent and raised accuracy roughly 30 percent by running and customising the open-weight Qwen3-VL model on its own infrastructure.
Results reflect Pinterest's specific implementation. Your outcomes will depend on your hardware, model choice, and workload.
Source as of June 2026
A data-protection promise is only as strong as the jurisdiction that can override it.
Every cloud AI provider will tell you your data is protected, and most of them mean it. The limit is structural, not a question of intent. Any provider is subject to the laws of the jurisdiction it answers to, so a contractual promise can be overridden by a lawful order from that jurisdiction. This is true of every cloud, in every country. It is the nature of handing data to a third party.
Hive changes the mechanism rather than the promise. The open-weight model runs on hardware you control. Your prompts, your documents, and the model's responses stay inside your own network boundary. On the on-prem deployment, inference runs locally and the request never calls an external API, so there is no third-party endpoint to subpoena and no external data path to audit. The data does not move, so there is nothing to compel.
That is what we mean by sovereignty, scoped precisely: the on-prem Hive deployment keeps every inference inside your own infrastructure, under your physical and legal control.
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You hold the hardware.
The box sits on your premises, or in an in-region datacentre you select, under your own jurisdiction. You hold the keys, the logs, and the machine.
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No request leaves the boundary.
On the on-prem deployment, inference runs locally and never calls an external API. There is no provider in the loop, so there is no provider to be ordered to produce anything.
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One vendor relationship, no metered data path.
You license software and support from us. Your data never reaches us, because there is no place in the architecture for it to go.
Three exposures come bundled with every cloud AI subscription.
When your team puts a contract, a patient record, or a quarter's unpublished numbers into a third-party AI tool, three things follow that most buyers never priced in. None of them is about a bad provider. All three are properties of sending data outside your own control.
- 01
Reach that follows the provider, not the data. Any cloud provider can be compelled, under the law of the jurisdiction it answers to, to produce customer data regardless of where that data physically sits. The US CLOUD Act is one well-known example of this general principle: it lets a US-controlled provider be ordered to hand over data even when that data lives in another country. The point is not specific to one law or one country. Residency is where the bytes live. Sovereignty is who can be ordered to produce them. They are not the same thing, in any jurisdiction.
- 02
Your prompts become records someone else holds. A US court has already ordered a major AI provider to preserve chat logs (source). Prompts and responses sent to a third-party AI service are electronically stored information held by another party. They can be subpoenaed, preserved by order, and pulled into discovery (source), and a "we do not retain" promise gives way the moment a preservation order lands. The data you cannot see is the data you cannot govern.
- 03
A bill that grows with use. A third-party AI API meters you per token. Light, occasional usage stays cheap. But once AI runs inside long-running agents, automations, and sustained daily work, the meter never stops and the bill grows with every win. The more value you get, the more it costs to keep getting it.
The first two exposures are about who can reach your data. The third is about who controls your cost. Running the model yourself addresses all three. With Hive on-prem, your data stays inside your network boundary, so the jurisdiction that governs it stays only yours.
Run Hive at the level of control your data demands.
The same Hive software runs three ways, from maximum control to lowest cost. You choose the point that fits your data, your team, and your budget. Every hosted option runs only on infrastructure that sits in-region and under your own jurisdiction, because hosting you under a jurisdiction other than your own would reintroduce the exact exposure you came here to remove.
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Own it (on-prem).
A Hive deployment on your premises, run by your team. Data never leaves the building. The strongest position on the spectrum.
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Colocate it.
A dedicated Hive box, hosted by us in-region, on infrastructure under your own jurisdiction. We manage the hardware. Residency stays in-region.
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Shared cloud.
A region-locked shared instance with a full audit trail. Lowest cost, fastest start. Honestly the weakest tier on the spectrum, and we grade it that way.
An open-weight model you own costs a fraction of the API you rent.
Open-weight models have closed much of the quality gap with the large proprietary APIs, and they run on hardware you can buy. Run the same workload on a model you own and the cost changes shape. There is a mostly fixed cost up front for the box, and after that inference is roughly the cost of the electricity it draws.
Measured on output, open-weight inference runs roughly 10x to 30x cheaper than the proprietary APIs. For an entry-tier box handling sustained daily work, the box pays for itself in about seven months, then keeps saving every month after. For light, occasional use, a public API is a reasonable choice. For long-running agents, automations, and heavy daily usage, ownership is the cheaper position over time.
Rates as of June 2026. Hosted-API pricing, self-hosting economics differ.
Pinterest cut AI costs roughly 90 percent and raised accuracy roughly 30 percent by rebuilding the open-weight Qwen3-VL vision model on its own infrastructure with proprietary embeddings.
as of June 2026
Results reflect Pinterest's specific implementation. Your outcomes will depend on your hardware, model choice, and workload. Source
If your data cannot go to a third-party AI service, Hive runs in your own.
Hive is built for organisations whose data is the one thing they cannot put at risk. That covers a wide range of work, in many jurisdictions.
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Financial institutions.
Banks, credit unions, insurers, and FinTech firms handling material non-public information, internal agents, and recordkeeping obligations that an external endpoint cannot satisfy.
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Healthcare and health-data custodians.
Personal health information stays under the custodian's direct control, with no external service provider in the data path.
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Government and defence.
Public-sector bodies and military programmes, including air-gapped and offline deployments where no outbound network path is permitted.
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Legal.
Privilege does not survive a third-party processor. Client matters and document review stay inside the firm.
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Any sensitive-data organisation.
If "do not send our data to a third party" is a hard rule, on-prem inference is the posture that keeps it. Trade secrets, source code, R&D, and competitive plans included.
The box you buy keeps getting better.
Owning your hardware does not mean owning a snapshot. Under the annual licence, the Hive software and the supported open-weight models keep improving. New and stronger open models are validated and made available as they ship, so your deployment tracks the state of the art instead of falling behind it. When a workload outgrows a single box, capacity is added: one box, then multiple GPUs, then multiple nodes. You are buying a platform that keeps improving, on hardware you can extend.
Glean, Writer, Cohere North, and Copilot route every query through their cloud. Hive runs in yours.
What is actually true here.
- Architecture
Open-weight model weights run on your hardware. Every inference executes locally. No prompt, no response, no document calls an external API or leaves your network boundary.
- Perplexity
Perplexity runs DeepSeek R1 on its own servers in US data centres, with EU to follow. User data never leaves Western servers. Source (as of January 2025)
- Regulatory category
Finance, healthcare, and legal teams often cannot route sensitive data through an external AI endpoint. Self-hosted open weights keep every inference inside the network boundary. This is general information, not legal advice.
Run your first AI inference on hardware you control.
Book a short demo and we will show Hive running locally, with no data leaving the box.