Deploy powerful AI within your secure environment
Private LLM deployment gives your organization the power of modern AI while keeping sensitive data within your control. Whether deployed in your VPC, on-premises, or in an air-gapped environment, private LLMs enable compliant AI adoption for regulated industries.
Your data never leaves your environment. No training on your data by third parties.
Meet HIPAA, SOC 2, PCI DSS, and other regulatory requirements with proper controls
Fixed infrastructure costs vs. unpredictable per-token API charges at scale
Real-world applications delivering measurable results.
Process patient records and clinical notes with HIPAA-compliant AI that keeps PHI internal
Analyze contracts, reports, and customer data with AI that meets regulatory requirements
Review privileged documents with AI that maintains attorney-client confidentiality
Build company-specific AI assistants trained on proprietary documentation
Deploy AI for classified or sensitive government use cases in isolated environments
A proven approach to delivering successful solutions.
Assess compliance needs, use cases, data sensitivity, and infrastructure options
Choose the right model size and architecture for your performance and cost requirements
Design deployment architecture with proper security controls and scalability
Deploy models and integrate with your applications and workflows
Implement logging, access controls, and ongoing model governance
Public APIs send your data to third-party servers where it may be logged, stored, or used for training. Private LLMs run entirely within your infrastructure. Your data never leaves your control, which is essential for regulated industries and sensitive applications.
Open-source models like Llama 3, Mistral, and others have closed much of the gap. For many enterprise use cases, especially domain-specific applications, fine-tuned private models can match or exceed public API performance while keeping data internal.
Requirements vary by model size. Smaller models (7B-13B parameters) can run on standard GPU instances. Larger models may need multiple GPUs or specialized hardware. We help right-size infrastructure to balance performance and cost.
We implement comprehensive controls: audit logging of all AI interactions, access controls, data encryption, model versioning, and documentation for auditors. Our deployments are designed to meet SOC 2, HIPAA, PCI DSS, and other framework requirements.
We provide model lifecycle management including security updates, performance monitoring, and controlled model updates. You maintain full control over when and how models are updated in your environment.
Let's discuss how private llm deployment can transform your operations and deliver measurable results.