Open-Source AI Hosting
Stop sending your confidential data to OpenAI. We deploy and scale open-source AI models securely on your own cloud infrastructure.
Organisations handling regulated or commercially sensitive data often cannot send it to a third-party API, regardless of the contractual assurances offered. That constraint tends to stop AI adoption entirely, even where the underlying use case is straightforward and valuable.
Impact
Teams either forgo the capability, or staff quietly use consumer AI tools with company data — which is considerably worse, because it happens outside any policy or audit. The absence of a sanctioned option does not remove the demand.
We deploy open-weight models inside your own infrastructure, so inference happens within your security boundary. That covers model selection against your actual latency and quality requirements, serving infrastructure, access control, and the internal tooling your teams will use.
Technical Approach
Serving runs on vLLM or a comparable inference server, deployed to your cloud account or on-premises hardware with GPU capacity sized to real concurrency rather than theoretical peak. Access is authenticated and logged, and the deployment integrates with internal portals or retrieval systems as required.
Sensitive workloads are excluded from AI tooling entirely on policy grounds, while staff informally use external services for work the organisation never sanctioned.
A sanctioned internal capability running inside your security boundary, with authentication, logging, and no data leaving infrastructure you control.
Strict adherence to global data privacy laws. We never train public AI models on your proprietary data.
Architecture designed to meet rigorous healthcare and enterprise security compliance standards natively.
Scalable cloud-native deployments via AWS and Vercel Edge networks ensuring 99.99% uptime.
Everything you need to know about our Open-Source AI Hosting process.
GPU cloud instances (like AWS p4d) can be costly. However, we use 'Quantization' and high-throughput servers like vLLM to dramatically reduce VRAM requirements, often making self-hosting cheaper than OpenAI APIs at enterprise scale.
Yes, we deploy modern, sleek chat interfaces (like LibreChat or custom Next.js apps) that connect to your private model, giving your team the exact same user experience as ChatGPT.
For a wide range of tasks, yes. The gap has narrowed considerably, and current open-weight models handle summarisation, extraction, classification, and retrieval-augmented question answering well. Frontier commercial models still lead on the hardest reasoning tasks. The honest framing is that model choice should follow your actual workload — we benchmark candidates against your real inputs rather than relying on published leaderboards.
It depends on model size and concurrency, and this is where costs are most often misjudged. A quantised mid-size model can serve a department on a single modern GPU; organisation-wide deployment with high concurrency needs considerably more. We size against your realistic concurrent usage rather than peak theoretical load, because over-provisioning GPUs is expensive and easy to do by accident.
Yes. Air-gapped deployment is well supported — weights are downloaded once, and inference requires no outbound connectivity. This is a common requirement in defence, healthcare, and financial contexts. Model updates then become a deliberate, scheduled process rather than something a provider does to you without notice.
Whoever you choose. We build with standard, documented tooling so your existing infrastructure team can operate it, and we hand over runbooks covering monitoring, scaling, and model updates. Many clients retain us for ongoing support, but the deployment is deliberately not dependent on us being available.
Deploy a private AI on your own servers.
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