Cloud-Dog Private LLM

Executive Overview

Cloud-Dog Private LLM enables organisations to deploy and operate modern large language models entirely within their own controlled environment, delivering the power of advanced AI without exposing sensitive data, intellectual property, prompts or outputs to external providers. It is designed for enterprises that require confidentiality, sovereignty, predictable performance and strict governance while still benefiting from the latest advances in LLM capability.

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Summary

Cloud-Dog Private LLM enables organisations to deploy and operate large language models entirely within their own controlled environment. Using Ollama or vLLM runtimes with GPU acceleration, it delivers confidential AI inference without exposing data to external providers. Supports private, sovereign, hybrid and fully offline deployments with complete data sovereignty.

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Features and Benefits

Feature	Benefit
Private AI model hosting with complete data control	Protects confidential data by keeping AI fully in-house
Secure confidential processing for sensitive workloads	Enables safe AI adoption without external dependence
Consistent performance for mission-critical AI operations	Guarantees compliance with sovereignty requirements
Unified interface for all local AI applications	Reduces AI operating cost through local inference
Enterprise governance across all AI interactions	Improves workforce productivity with fast AI responses
Compliant deployment for regulated sovereign environments	Increases trust through governed auditable AI behaviour
Flexible model choices for varied business needs	Supports innovation without exposing intellectual property
Scalable design supporting team-to-enterprise growth	Provides resilience through offline controlled deployments
Reliable offline operation for high-assurance scenarios	Accelerates AI deployment across business functions
Easy integration with existing enterprise workflows	Enhances decision-making with reliable private intelligence

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Product Overview

Cloud-Dog Private LLM provides organisations with a secure, self-contained large language model environment that can be deployed in private cloud, sovereign cloud, data-centre, on-premise or fully offline infrastructures. Rather than relying on external AI providers or exposing sensitive data to public APIs, Cloud-Dog Private LLM enables organisations to operate their own high-performance model stack using open, transparent and locally hosted components.

At the core of the service is a choice of Ollama or vLLM, or both when deployed across multiple compute nodes. Ollama offers a simple, lightweight and highly accessible runtime optimised for rapid model loading and diverse model experimentation. It excels in development, prototyping, team-level use and scenarios where multiple compact or mid-sized models are required concurrently. In contrast, vLLM provides an advanced, deeply-optimised inference engine capable of driving high-throughput workloads, larger models and production-grade applications with strict latency and concurrency requirements.

Cloud-Dog Private LLM includes OpenWebUI, providing a user-friendly interface for model testing, prompt engineering, evaluation and interactive use. For integration with other Cloud-Dog agents, workflow tools and internal applications, the service optionally incorporates LiteLLM as an on-premise OpenAI-compatible gateway. LiteLLM enables full API abstraction, usage metering, project-level cost controls, request routing and model policies — all without data ever leaving the private environment.

The platform is fully configurable to meet specific operational demands. It supports a wide range of models available through Ollama and Hugging Face, including compact local models, mixture-of-experts architectures, multilingual models, domain-specific models and high-parameter LLMs deployable through vLLM. Deployments are optimised for Linux, CUDA and NVIDIA GPU architectures using containerised delivery with Docker.

Cloud-Dog Private LLM forms a natural extension of your agentic ecosystem. When combined with the Cloud-Dog RAG Agent, SQL Agent or Data Agent, it provides the foundational reasoning engine over private knowledge, structured data and application context. Workflows that require deterministic governance, local execution or strict confidentiality benefit from having a fully isolated LLM runtime that never transmits prompts, embeddings or tokens to third-party systems.

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Architecture

Cloud-Dog Private LLM is built on a secure, modular architecture designed to deliver high-performance local large language model inference while maintaining full control over data, workloads and operational boundaries.

At the foundation is the Execution Layer, which hosts either Ollama or vLLM — two complementary inference runtimes optimised for different operational objectives. When hardware permits, both engines can operate side-by-side, allowing different agents or workloads to route requests to the runtime best suited to their performance and cost profiles.

Above this is the Interface and Access Plane, including an optional LiteLLM gateway exposing a local OpenAI-compatible API endpoint. OpenWebUI forms the user interaction surface for experimentation, evaluation, model comparisons and development use.

The Governance and Security Layer enforces isolation, policy, auditability and compliance. It ensures that prompts, responses, embeddings and logs remain entirely within the deployment boundary. This layer integrates with SSO and enterprise identity systems and applies role-based access controls.

The Deployment Layer provides flexibility for enterprise operations. Built on Docker-based containerisation, it can run on single GPU nodes, multi-GPU clusters, air-gapped servers or private cloud infrastructure. Optimised for Linux, CUDA and NVIDIA platforms.

The Observability and Lifecycle Management Plane ensures predictable operation through logging, metrics, model updates, patching and performance tuning.

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Key Capabilities

Private, Sovereign and Offline LLM Deployment — Deploy modern large language models entirely within your own boundary. All prompts, responses, logs and embeddings stay fully in-house, ensuring confidentiality and compliance with data residency requirements.

Flexible Model Execution Using Ollama or vLLM — Choose the execution engine best suited to the workload. Ollama excels at rapid switching and experimentation while vLLM provides superior throughput, batching and tensor parallelism for demanding workloads.

Unified Access Through OpenAI-Compatible APIs — LiteLLM offers an OpenAI-compatible access layer covering completion, chat, embeddings and model routing. Applications and agents integrate without modification while maintaining internal control.

Enterprise-Grade Governance and Security — Every request is subject to policy controls, identity validation and isolation. The system prevents unauthorised external access and enforces security boundaries with logging for auditability.

High-Performance GPU-Optimised Inference — Built for predictable latency and sustained throughput across NVIDIA GPUs. Optimises model loading, batching and token generation for efficient hardware resource use.

Integration with the Agent Ecosystem — Integrates naturally with Cloud-Dog RAG Agent, SQL Agent and Data Agent, serving as the reasoning engine for secure, grounded multi-agent workflows.

Scalable Deployment Options — From single-node test environments to multi-node resilient clusters, the platform supports predictable scaling from evaluation to production.

Full Lifecycle Management — Includes patching, model updates, security improvements and performance optimisation with visibility into model behaviour, hardware usage and system health.

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Use Cases

1. Confidential AI Processing — Run sensitive workloads entirely within your own security boundary.
2. Sovereign AI Deployment — Meet data residency and sovereignty requirements with fully local inference.
3. Agent Reasoning Engine — Provide the foundational LLM for RAG, SQL and Data Agent workflows.
4. Model Evaluation and Testing — Evaluate and compare models using OpenWebUI before production deployment.
5. Air-Gapped AI Operations — Operate LLMs in fully offline, disconnected or classified environments.