LLMDeploy helps Enterprise AI Teams achieve complete data sovereignty while deploying custom LLMs on-premises in days instead of weeks. Get a custom solution tailored to your enterprise needs.
100%
Enterprise Focused
72hrs
Deployment Time
10x
Lower Latency
100%
Data Sovereignty
Enterprise AI teams face critical challenges when deploying LLMs
Sensitive data leaving controlled environments when using cloud APIs poses compliance and security risks
Unpredictable cloud API costs and usage spikes affecting budget planning
3-5 weeks initial deployment requiring 3-5 senior engineers and extensive DevOps resources
A turnkey on-premises LLM platform that delivers enterprise-grade AI capabilities
Pre-configured Docker containers with optimized open-source LLMs. Deploy in 72 hours vs. 3-5 weeks.
Pre-certified for HIPAA, SOC2, and FedRAMP. Real-time compliance monitoring and audit trails.
Automated tenant isolation with role-based access control. Manage separate model instances securely.
Interface for training models with your proprietary data. A/B testing and automated model updates.
Built-in load balancing, auto-scaling, and GPU resource management. 10x lower latency than cloud APIs.
Dedicated success manager, quarterly business reviews, and 1-hour SLA for critical issues.
LLMDeploy is designed specifically for organizations with stringent data sovereignty requirements
Financial Services
Banks, insurance companies, and investment firms with AI/ML teams of 10-20 engineers
Healthcare Organizations
Hospital systems and pharmaceutical companies handling sensitive patient data
Government Contractors
Defense and federal contractors requiring air-gapped deployments
Fortune 500 Manufacturing
Companies with proprietary IP and trade secrets needing secure AI
You'll get the most value from LLMDeploy if you:
Need complete control over their AI data
Your data can't leave your infrastructure for legal or competitive reasons
Are serious about implementing AI
You have real use cases and budget, not just exploring
Have technical resources available
Either in-house IT team or external partners who can manage infrastructure
Value long-term cost efficiency
Looking to reduce ongoing AI costs, not just quick experiments