Self-hosted open-LLM hub
Deploy selected open-source models and orchestration inside your own infrastructure, with a governed gateway and routing.
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For regulated and data-sensitive organisations: keep AI inside your own trust boundary with self-hosted open-LLM hubs, on-premises RAG and private-cloud deployments. You get full data residency and sovereignty, model-neutral and governed.
Model-neutral and deployment-flexible, so data sensitivity and residency drive the architecture.
Deploy selected open-source models and orchestration inside your own infrastructure, with a governed gateway and routing.
Permission-aware retrieval over your documents and systems without sensitive data leaving your boundary.
Dedicated, isolated environments with controlled network integration for stronger separation.
Policy testing, red-team scenarios, monitoring and release gates around every deployment.
Task agents that use only approved tools, with human approval for high-impact actions and auditable state.
Trust boundaries, identity and access controls defined per workload and per data source.
Most organisations use more than one model, classified by data sensitivity, residency requirements and latency. The diagram above shows what sits inside the boundary in each case.
Best when data and model weights must never leave your infrastructure. Everything runs in your own environment, in the region you choose.
Best when you want a dedicated, isolated environment in your own cloud account, with managed operations and optional governed access to commercial models.
Best when speed matters and your data can stay in your tenancy. The platform is managed, and only redacted, non-sensitive prompts reach external models.
Open-weight LLMs let you self-host, fine-tune and avoid per-token lock-in. We wrap them in the same control plane as commercial models: identity, routing policy, guardrails, evaluation and audit, so “open” never means “ungoverned”.
From assessment to operation, with named owners and decision gates at every step.
Data sensitivity, residency requirements, use cases, risk and the right deployment boundary.
Deployment model, network and identity boundaries, model selection and the control-plane design.
Models, RAG and the control plane stood up inside your environment, integrated with your systems.
Evaluation, monitoring, guardrails and audit, with updates as the open-model ecosystem evolves.
A private deployment needs infrastructure, networking, data pipelines and integration. Ledger Bytes Technology delivers that engineering around the AI, under one coordinated proposal.
Explore technology deliveryBegin with an AI assessment to map use cases, data readiness and the right deployment boundary before you build.
See AI consultingStraightforward answers to common questions.
With self-hosted and private-cloud deployments, your source data, documents, vector index and model files remain in your own cloud account or on-premises, in the region you choose. Nothing is sent to an external model provider unless you explicitly enable it.
We are model-neutral and work with leading open-weight model families, selected per use case on quality, licence, latency and cost. The control plane lets you adopt new open models as the ecosystem evolves, without re-architecting.
Yes. Private deployments run in your existing AWS, Azure or Google Cloud account, or on-premises, alongside your current networking and controls.
A commercial API is fast to start but sends your prompts and data to an external provider. A private deployment keeps data and models inside your boundary, gives you control over model versions and cost, and avoids per-token lock-in. The trade-off is running more of the stack, which we manage for you.
Yes. Selected open-source models, the control plane and RAG components can run in your cloud account or on-premises, with no dependency on a single external model provider.
Where it improves outcomes and licensing permits, yes, alongside retrieval (RAG), which is often the faster, safer route to grounded answers.
The same control plane applies to open and commercial models: identity, routing policy, guardrails, evaluation and full audit.
Tell us your data sensitivity, infrastructure and use cases; we will propose a self-hosted or private-cloud architecture.