Why Enterprises Are Moving to Private LLM Deployments
Public AI APIs served as a useful starting point, but enterprises are increasingly recognizing that production-scale AI demands more control, security, and cost predictability.
Complete Data Sovereignty
When you use public AI APIs, every prompt and response transits through third-party infrastructure. With a private LLM deployment, your data never leaves your security perimeter — not for inference, not for training, not for logging. This is not just a security preference; for many industries it is a regulatory requirement. Financial services firms handling material non-public information, healthcare organizations processing PHI, government agencies with classified data, and legal firms managing privileged communications all require data sovereignty that public APIs fundamentally cannot provide.
Zero Vendor Lock-in
Public AI APIs create deep vendor dependencies. Pricing changes, usage policy updates, model deprecations, and capability modifications are entirely outside your control. Private deployments use open-source models — Llama, Mistral, Mixtral, and others — that you own and operate. You can switch models, run multiple models simultaneously, fine-tune for your domain, and evolve your AI capabilities on your own timeline. Your AI infrastructure becomes a strategic asset you control rather than a subscription service that someone else controls.
Regulatory Compliance
Emerging regulations like the EU AI Act, combined with existing frameworks including HIPAA, SOC 2, GDPR, and industry-specific standards, create complex compliance requirements for AI systems. Private deployments give you full auditability: you know exactly which model processed which data, you control data retention and deletion, and you can demonstrate compliance to regulators with complete transparency. This level of control is difficult or impossible to achieve with third-party API services where the model is a black box operated on someone else's infrastructure.
Predictable Cost at Scale
API-based pricing can be unpredictable and expensive at enterprise scale. As your AI usage grows across the organization, per-token costs compound rapidly. Private deployments have a higher upfront investment but deliver dramatically lower per-inference costs at scale. Organizations processing millions of requests per month often find that private deployment pays for itself within months. More importantly, costs are predictable and under your control — no surprise bills when a new application drives unexpected usage spikes.
Choose the Right Deployment Model
Every organization has different security requirements, infrastructure capabilities, and scalability needs. We design deployments that match your specific environment.
On-Premise Deployment
Maximum ControlFull LLM deployment on your own hardware within your own data center. This is the gold standard for data sovereignty. Your models, your hardware, your network — no external dependencies whatsoever. On-premise deployment is ideal for organizations with existing GPU infrastructure, air-gapped security requirements, or regulatory mandates that prohibit data from leaving physical premises. We handle the full deployment: GPU cluster configuration, model optimization and quantization, inference server setup, load balancing, monitoring, and operational documentation.
- Complete air-gap support for classified environments
- Zero external network dependencies
- Full hardware control and optimization
- Maximum security posture
- No recurring cloud compute costs
Private Cloud Deployment
Cloud FlexibilityLLMs deployed in your own VPC on AWS, Azure, or GCP with private endpoints and network isolation. You get the operational benefits of cloud infrastructure — elastic scaling, managed services, geographic distribution — while maintaining data sovereignty through VPC isolation and private networking. No data traverses the public internet. This model works well for organizations that already have cloud-first infrastructure strategies and want to avoid managing physical GPU hardware while maintaining control over their AI models and data.
- VPC-isolated with private endpoints only
- Elastic scaling for variable workloads
- No public internet data exposure
- Managed infrastructure reduces operations burden
- Multi-region deployment options
Hybrid Deployment
Best of BothA combined approach where sensitive workloads run on-premise while less sensitive or burst-capacity workloads run in a private cloud environment. Hybrid deployments let you optimize for both security and cost: critical applications processing sensitive data stay on your premises, while less sensitive applications benefit from cloud elasticity. A unified management plane provides consistent monitoring, governance, and operational control across both environments. This architecture is increasingly popular with large enterprises that have diverse workload profiles.
- Sensitive data stays on-premise automatically
- Cloud burst capacity for peak demand
- Intelligent routing based on data classification
- Unified monitoring and management
- Gradual migration path between deployment modes
Models We Deploy
We work with the leading open-source and open-weight models, selecting the best fit for your use case, performance requirements, and infrastructure constraints.
Llama 3 / 3.1
Meta
Industry-leading open-weight models ranging from 8B to 405B parameters. Excellent general-purpose performance with strong reasoning and instruction-following capabilities. Available for commercial use and fine-tuning.
Mistral / Mixtral
Mistral AI
High-performance models with efficient architectures. Mixtral uses a mixture-of-experts approach that delivers strong performance with lower compute requirements. Excellent for cost-effective deployments at scale.
Qwen 2.5
Alibaba
Strong multilingual capabilities with competitive performance across benchmarks. Available in multiple sizes for different deployment scenarios and resource constraints.
Custom Fine-Tuned Models
Your Data
We fine-tune base models on your domain-specific data to create specialized models that outperform general-purpose alternatives for your particular use cases. Fine-tuning can dramatically improve accuracy for domain-specific tasks while reducing inference costs through smaller, more focused models.
RAG Implementation for Private LLMs
Retrieval-Augmented Generation lets your private LLMs answer questions using your enterprise data — without that data ever leaving your infrastructure.
How Enterprise RAG Works
RAG bridges the gap between general-purpose LLMs and your organization's proprietary knowledge. Instead of fine-tuning a model on all your data — which is expensive and inflexible — RAG retrieves relevant documents at query time and includes them as context for the LLM to generate grounded, accurate responses with citations to source materials.
This approach works with any enterprise data source: internal wikis, policy documents, technical documentation, customer records, contracts, research papers, email archives, and more. New documents are automatically ingested and indexed, so the system stays current without manual intervention. And because the entire pipeline runs on your infrastructure, your proprietary data maintains full sovereignty throughout the process.
RAG Pipeline Components
- Document IngestionAutomated connectors for SharePoint, Confluence, S3, databases, APIs, and file systems
- Chunking & ProcessingIntelligent document parsing, splitting, and metadata extraction
- Vector EmbeddingsDense vector representations generated with enterprise-grade embedding models
- Vector DatabaseEfficient storage and retrieval with Milvus, Qdrant, Weaviate, or pgvector
- Semantic SearchHybrid search combining dense vectors with BM25 sparse retrieval and reranking
- Grounded GenerationContext-aware prompting with source citation and hallucination guardrails
Frequently Asked Questions
Common questions about private LLM deployments, infrastructure requirements, and enterprise AI sovereignty.
How much does a private LLM deployment typically cost?+
Cost varies significantly based on deployment model, scale, and performance requirements. A basic private cloud deployment for a single model serving moderate traffic can start in the range of initial setup costs plus monthly cloud compute. Larger on-premise deployments with multiple models and high throughput require more substantial infrastructure investment. However, the total cost of ownership for private deployments is often lower than API services at enterprise scale. We provide detailed cost modeling during our discovery phase, comparing private deployment TCO against projected API costs over one, three, and five year horizons so you can make an informed decision.
What GPU infrastructure do I need for on-premise deployment?+
Infrastructure requirements depend on the model size, required throughput, and latency targets. Smaller models (7B-13B parameters) can run effectively on a single NVIDIA A100 or H100 GPU. Larger models (70B+ parameters) typically require multi-GPU configurations. We optimize aggressively using quantization techniques (GPTQ, AWQ, GGUF) that can reduce memory requirements by fifty to seventy-five percent while maintaining quality. We provide detailed hardware specifications and can work with your procurement team or cloud provider to size infrastructure appropriately. If you already have GPU infrastructure, we assess what you have and recommend targeted additions.
How does RAG work with private LLM deployments?+
Retrieval-Augmented Generation connects your private LLM to your enterprise data so it can answer questions grounded in your proprietary information. We build the complete RAG pipeline: document ingestion (PDFs, Word docs, databases, APIs, SharePoint, Confluence, and other sources), chunking and preprocessing, vector embedding generation, vector database indexing, semantic retrieval with reranking, and prompt construction that feeds retrieved context to the LLM for grounded generation. The entire pipeline runs within your infrastructure, so your proprietary data never leaves your security perimeter. We also implement citation tracking so users can verify the sources behind any AI-generated response.
Can you migrate us from public APIs to private LLMs?+
Yes, API-to-private migration is a common engagement for us. We assess your current API usage patterns, evaluate which applications are suitable for private model alternatives, plan the migration sequence, and execute the transition with minimal disruption. Not every application needs to move: some low-sensitivity, low-volume use cases may be better served by APIs. We help you make pragmatic decisions about what to migrate and what to leave, then execute the migration with parallel running, performance comparison, and gradual cutover. The result is typically lower costs, better data sovereignty, and more control — without losing capability.
What about model updates and keeping up with the latest models?+
The open-source model landscape evolves rapidly, and private deployments need to keep pace. We design your infrastructure to support model updates as a routine operational process, not a one-time deployment event. When new model versions release that offer meaningful improvements for your use cases, we evaluate them against your benchmarks, test them with your data, and manage the upgrade process. For managed service clients, model evaluation and updates are included in the ongoing retainer. We also monitor the model landscape proactively and recommend updates when new releases offer significant performance, efficiency, or capability improvements.