Deploy large language models on your own infrastructure — full data privacy, regulatory compliance, zero data leaving your network.
Private LLM deployment means running large language models like Llama, Mistral, or fine-tuned models on your own servers or private cloud — not sending data to OpenAI or Google. This is critical for organizations bound by RBI data localization rules, HIPAA compliance, DPDP Act requirements, or internal data governance policies. Your prompts, documents, and responses never leave your infrastructure. Boolean & Beyond builds private AI deployments on AWS, Azure, GCP private cloud, or bare-metal servers. We handle model selection, infrastructure sizing, fine-tuning on your domain data, and production deployment with monitoring. Typical inference costs drop 60-80% compared to API-based LLMs at scale.
Our implementation approach covers the full spectrum of private llm & on-premise ai deployment.
On-premise LLM deployment (Llama 3, Mistral, Phi, Gemma)
Private cloud AI on AWS/Azure/GCP (VPC-isolated)
Domain-specific fine-tuning on your data
RAG systems with private vector databases
GPU infrastructure sizing and optimization
Model quantization for cost-efficient inference
Kubernetes-based scaling and monitoring
Air-gapped deployment for classified environments
DPDP Act and RBI compliance architecture
Deep-dive articles on building production private llm & on-premise ai deployment systems.
Common questions about private llm & on-premise ai deployment.
A private LLM deployment typically costs Rs 20-50 lakhs for initial setup including infrastructure, model fine-tuning, and production deployment. Ongoing GPU infrastructure costs Rs 2-8 lakhs/month depending on usage. At scale (10,000+ daily queries), private deployment costs 60-80% less than API-based solutions like OpenAI — while keeping all data within your network.
The best open-source LLMs for on-premise deployment in 2025-2026 are: Llama 3.1 (405B, 70B, 8B variants by Meta), Mistral Large and Mixtral, Microsoft Phi-3, Google Gemma 2, and DeepSeek-V3. For Indian language support, Sarvam AI and AI4Bharat models work well. Model choice depends on your use case, hardware, and latency requirements.
RBI's data localization rules require that financial data of Indian customers is stored and processed within India. Sending customer queries containing financial data to OpenAI's US servers potentially violates these rules. Private LLM deployment on Indian data centres (AWS Mumbai, Azure Pune) ensures full compliance while enabling AI capabilities for banking, insurance, and fintech applications.
For domain-specific tasks, yes — often exceeding it. A Llama 70B model fine-tuned on your industry data typically outperforms GPT-4 on your specific use cases while being 10x cheaper to run. For general knowledge tasks, GPT-4/Claude remain stronger. The optimal approach is often hybrid: private LLM for sensitive data tasks, API-based LLM for general tasks.
Boolean & Beyond is a software engineering company in Bangalore (Bengaluru) specializing in private LLM deployment for enterprises. We handle model selection, infrastructure setup, fine-tuning, and production deployment on AWS, Azure, GCP, or bare-metal servers. We serve BFSI, healthcare, and government clients in Bengaluru, Coimbatore, and across India.
We build production-ready private llm & on-premise ai deployment systems designed to scale.
We approach every project with production readiness in mind—proper error handling, monitoring, and scalability from day one.
We help you decide what to build custom and what to integrate. Not every problem needs a custom solution.
Our team brings deep experience in building similar systems, reducing risk and accelerating delivery.
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Boolean and Beyond
825/90, 13th Cross, 3rd Main
Mahalaxmi Layout, Bengaluru - 560086
590, Diwan Bahadur Rd
Near Savitha Hall, R.S. Puram
Coimbatore, Tamil Nadu 641002