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Private LLM Deployment Services
Plavno helps businesses deploy large language models in private cloud, hybrid cloud, and on-premise environments. We design secure LLM infrastructure for companies that need stronger control over data privacy, model access, compliance, performance, and enterprise AI operations.
Private LLM Deployment Solutions
Deploy LLMs in controlled environments designed around your security, compliance, performance, and infrastructure requirements.
Private LLM hosting
Secure model deployment
Enterprise AI infrastructure
Controlled inference pipelines
Run large language models inside your own infrastructure for maximum data control and reduced external exposure.
On-premise model hosting
Local inference setup
GPU infrastructure planning
Network-isolated AI systems
Deploy LLMs in dedicated private cloud environments with secure APIs, access control, monitoring, and scalable architecture.
Private cloud AI architecture
Secure API gateway setup
Model serving infrastructure
Cloud-based private inference
Build hybrid AI infrastructure that combines private environments with selected cloud services where appropriate.
Hybrid AI deployment
Secure data routing
Cloud and on-prem integration
Scalable enterprise architecture
Connect privately deployed LLMs to internal documents, databases, and knowledge bases using secure retrieval-augmented generation.
Private enterprise knowledge search
Internal document Q&A
Secure vector database setup
Access-aware retrieval
Deploy private AI assistants for teams that work with sensitive company, customer, legal, healthcare, or financial information.
Employee knowledge assistants
Legal AI assistants
Healthcare AI assistants
Financial operations copilots
Optimize private LLM infrastructure for performance, latency, cost, scalability, and reliability.
GPU utilization optimization
Model serving optimization
Latency reduction
Usage and cost monitoring
Add enterprise controls to manage model access, usage, performance, outputs, logs, and security events.
Role-based access control
Usage analytics
AI audit logs
Security monitoring
Eugene Katovich
Sales Manager
Whether you need private LLM hosting, on-premise LLM deployment, private RAG architecture, a secure AI assistant, or enterprise AI infrastructure, Plavno helps you move from idea to secure production-ready implementation. Our team combines AI engineering, cloud infrastructure, DevOps, security, and business process automation to deploy private LLM systems that work in real business environments.
Case Study
Plavno developed a secure Real-Time EHR Summarization Agent that automates clinical documentation, reduces physician administrative burden, and enhances medical record accuracy using advanced EHR summarization AI.
improvement in documentation accuracy
Plavno, an international AI development company, delivered a secure AI-based data anonymization system for a California law enforcement agency, automating document redaction while meeting strict US compliance and data residency requirements. This project demonstrates how enterprise-grade AI solutions and AI agents can safely operate within sensitive government environments.
document processing time
Whom We Serve
Benefits
Stronger Data Control:Private LLM deployment keeps sensitive workflows inside controlled infrastructure, helping businesses manage how data is accessed, processed, and stored.
Better Compliance Readiness:Private AI infrastructure can support stricter governance, auditability, access control, and data handling requirements.
Reduced External Dependency:Private LLM systems reduce reliance on external AI APIs for critical workflows and give businesses more control over AI operations.
Custom Performance Optimization:Private deployment allows teams to optimize model serving, latency, cost, availability, and scalability based on actual business usage.
Secure Enterprise AI Adoption:Plavno builds private LLM systems with security, monitoring, access control, and operational visibility from the beginning.
Security Measures
We design private LLM environments that reduce external exposure and keep sensitive AI workflows inside controlled infrastructure.
We implement authentication, permissions, role-based access control, and secure administrative access for private LLM systems.
We configure monitoring for model usage, system performance, latency, errors, security events, and infrastructure health.
We test private LLM deployments for infrastructure risks, API vulnerabilities, prompt injection, data leakage, and business-rule violations before production launch.
Advanced Tech
Private model serving infrastructure allows businesses to run LLM inference in controlled cloud, hybrid, or on-premise environments.
GPU and compute optimization help reduce infrastructure waste, improve latency, and support scalable private AI workloads.
Private vector databases enable secure semantic search, document retrieval, knowledge indexing, and source-grounded AI responses.
Secure API gateways manage access between users, applications, business systems, and privately deployed LLMs.
Observability and LLMOps support monitoring, logging, model evaluation, cost tracking, versioning, and continuous improvement.
Frequently Asked Questions
Find answers to your common concerns
Private LLM deployment services include the setup, hosting, integration, monitoring, and optimization of large language models in private cloud, hybrid cloud, or on-premise environments.
Private LLM deployment is useful for businesses that need stronger data control, security, compliance readiness, predictable infrastructure, and reduced dependency on external AI APIs.
Yes. Plavno can support on-premise LLM deployment, including infrastructure planning, model serving, secure access, monitoring, and integration with internal systems.
Yes. Plavno can connect private LLMs to internal documents, databases, knowledge bases, cloud storage, CRMs, ERPs, and enterprise tools using secure RAG architecture and API integrations.
It depends on your business case. Commercial APIs can be faster for MVPs, while private LLM deployment can be better for sensitive data, high-volume usage, compliance requirements, and infrastructure control.
Infrastructure depends on model size, expected traffic, latency requirements, security needs, and deployment type. It may include GPUs, model serving tools, vector databases, API gateways, monitoring, and secure networking.
The cost depends on model choice, infrastructure, GPU requirements, expected usage, data complexity, integrations, security requirements, and whether you need private cloud, hybrid, or on-premise deployment.
Yes. Plavno can build private RAG systems that connect privately deployed LLMs to internal documents and enterprise data while maintaining access control and secure retrieval.
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Vitaly Kovalev
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