Private LLMs and RAG for the enterprise.
Architecture patterns, deployment guides, and cost-compliance trade-offs for private LLM hosting, retrieval-augmented generation, and enterprise knowledge systems.
Enterprise teams need LLM capabilities without sending sensitive data to third-party APIs. Private LLM deployment, whether through cloud-hosted private endpoints, self-hosted models, or hybrid architectures. Gives organisations control over data residency, access patterns, and cost.
Retrieval-augmented generation (RAG) is the bridge between LLMs and your private data. Instead of fine-tuning models on proprietary content, RAG retrieves relevant documents at query time and injects them into the model's context. The architecture choices. Chunking strategies, embedding models, retrieval methods, vector stores. Determine whether the system is useful or frustrating.
We design and deploy private LLM and RAG architectures that balance performance, cost, and compliance requirements. Every deployment is different. The right architecture depends on your data volumes, query patterns, security constraints, and operational capacity.
Private LLM hosting patterns: Azure, Anthropic, and self-hosted compared
Architecture patterns and trade-offs for deploying LLMs without exposing sensitive data to public endpoints. Covers cost modelling, compliance implications, and operational complexity for each approach.
ReadHow we structure RAG pipelines for enterprise knowledge retrieval
A technical walkthrough of retrieval-augmented generation architecture: chunking strategies, embedding selection, retrieval scoring, and the trade-offs we make in production deployments.
ReadEnterprise AI Platform
A large enterprise needed a secure, governed way for employees to use LLMs internally without exposing sensitive information or relying on uncontrolled public tools.
Read case studyDomain-specific conversational AIScientific product platformLabCaddy
The client needed a more intelligent way for users to discover science-related products and interact with product information through conversation, not just keyword filtering.
Read case studyWhat is a private LLM deployment?
A private LLM deployment gives your organisation access to large language model capabilities without sending data to shared, public API endpoints. Options include Azure OpenAI (dedicated instances within your Azure tenant), Anthropic's enterprise offerings with data isolation, and fully self-hosted open-source models (Llama, Mistral) running on your own infrastructure.
What is retrieval-augmented generation (RAG)?
RAG connects LLMs to your private data by retrieving relevant documents at query time and injecting them into the model's prompt. Instead of the model relying on its training data, it answers based on your specific documents. Internal wikis, policy documents, codebases, customer data. The key architecture decisions are how to chunk documents, which embedding model to use, and how to score retrieval relevance.
Self-hosted vs cloud-hosted LLMs: how do you choose?
Cloud-hosted private endpoints (Azure OpenAI, Anthropic Enterprise) give you data isolation with minimal operational overhead. Self-hosted models (via Ollama, vLLM, or similar) give you complete control but require GPU infrastructure, model serving, and ongoing maintenance. The decision depends on data sensitivity, cost tolerance, latency requirements, and your team's infrastructure capability.
How much does a private LLM deployment cost?
Costs vary significantly. Azure OpenAI provisioned throughput starts at approximately $2/hr per deployment unit. Self-hosted models require GPU infrastructure ($2-8K/month for a production-grade single-node setup). RAG infrastructure adds vector database costs ($100-500/month for managed services) plus embedding compute. We help teams model costs based on their specific usage patterns before committing.