AI that never leaves your infrastructure. Full control. Full ownership. Zero data exposure.
For businesses in regulated industries — legal, healthcare, finance — sending data to external AI providers is not an option. DeployLabs deploys open-source models (Llama, Mistral, Gemma, Qwen) on your own infrastructure, giving you AI capabilities with complete data sovereignty.
What Private / Self-Hosted AI brings to your operations.
On-Premise Deployment
Models run on your servers, your cloud, or your private VPC. No data leaves your infrastructure. No API calls to external providers. Complete network isolation for sensitive operations — document analysis, client communications, financial modeling.
Model Selection and Optimization
Open-source models range from 7B to 405B parameters. DeployLabs selects and optimizes the right model for your use case — balancing capability, latency, and hardware cost. Smaller models run on modest hardware. Larger models handle complex reasoning.
Fine-Tuning on Your Data
Unlike cloud AI services, self-hosted models can be fine-tuned on your proprietary data — case history, client communications, internal documentation. The model learns your domain terminology, your processes, and your standards.
Cost Predictability
No per-token API charges. No usage-based billing surprises. After the initial hardware and deployment investment, your AI runs at a fixed infrastructure cost regardless of usage volume. High-volume operations see costs drop to a fraction of API-based alternatives.
The platform gives you the foundation. We build the engine.
Private / Self-Hosted AI provides
- Open-source model weights (Llama, Mistral, Gemma, Qwen)
- Community inference engines (vLLM, Ollama, TGI)
- Fine-tuning frameworks (LoRA, QLoRA)
- Model evaluation benchmarks
- Hardware compatibility documentation
- Community support and updates
DeployLabs adds
- Infrastructure sizing, procurement, and configuration
- Model selection and optimization for your use case
- Fine-tuning pipeline design and data preparation
- Application layer connecting models to business workflows
- Monitoring, scaling, and model update procedures
- Security hardening and access control configuration
Three phases. No templates. No shortcuts.
Assess
We evaluate your compliance requirements, data sensitivity classification, existing infrastructure, and target workflows. The assessment produces a hardware specification, model recommendation, and deployment architecture tailored to your regulatory environment.
2–3 weeksBuild
Infrastructure is provisioned, models are deployed and optimized, fine-tuning pipelines are configured, and application layers connect the AI to your business workflows. Every deployment is tested against your actual data and compliance requirements.
6–12 weeksOperate
Ongoing monitoring covers model performance, infrastructure health, and security posture. When new model versions release, we evaluate and deploy upgrades. You own the entire stack — hardware, models, data, and application code.
OngoingPrivate / Self-Hosted AI engines built for your industry.
Measured results from Private / Self-Hosted AI engine deployments.
Enterprise-grade security built into every deployment.
Complete Data Sovereignty
No data leaves your infrastructure. No API calls to external providers. No third-party data processing agreements needed. Your data stays on your hardware, in your network, under your control.
PIPEDA · PHIPA · Provincial Privacy ActsNetwork Isolation
Models operate within your private network or VPC. Air-gapped deployments available for the highest-sensitivity environments. No internet connectivity required for inference.
Zero Trust · Network Segmentation · Air GapRegulatory Compliance
Self-hosted deployments meet the strictest regulatory requirements — HIPAA for healthcare, solicitor-client privilege for law firms, OSFI guidelines for financial services. The compliance perimeter is your infrastructure, not a vendor's.
HIPAA · OSFI · Law Society Rules · SOC 2Model Transparency
Open-source models provide full visibility into architecture, training data composition, and behavior. No black-box vendor models. You can audit exactly what the model knows and how it reasons.
Open Source Licenses · Model Cards · Audit RightsWhat clients ask about Private / Self-Hosted AI.
What hardware do we need for self-hosted AI?
It depends on the model size and throughput requirements. Smaller models (7B–13B parameters) run on a single GPU server ($5,000–$15,000). Mid-range models (30B–70B) require multi-GPU setups ($20,000–$50,000). Large models (70B–405B) need GPU clusters or cloud GPU instances. The assessment determines the right specification for your use case.
Are open-source models as capable as Claude or GPT-4?
For general reasoning, frontier models (Claude, GPT-4) still lead. But for domain-specific tasks — especially after fine-tuning on your data — open-source models frequently match or exceed them. A Llama 70B fine-tuned on your legal documents will outperform GPT-4 on your specific legal workflows. The right answer is often a hybrid: self-hosted for sensitive operations, cloud AI for general tasks.
What does a self-hosted deployment cost?
The AI Readiness Assessment is $2,500. Infrastructure design and deployment starts at $7,500 for the build phase. Hardware costs vary by specification. Monthly management retainers range from $2,000 to $5,000. The total investment is higher upfront than cloud AI but significantly lower in ongoing costs for high-volume operations.
How do we handle model updates?
Open-source models release updates regularly. DeployLabs evaluates new versions against your workflows, runs comparison benchmarks, and deploys upgrades when they improve performance. Your fine-tuning data and application code carry forward — you do not start over with each update.
Can we combine self-hosted with cloud AI?
Yes. Hybrid architectures are common. Sensitive operations (client data processing, privileged communications, financial analysis) run on self-hosted models. General tasks (content drafting, research, scheduling) use cloud AI. DeployLabs designs the routing logic that directs each task to the right environment.
Ready to build your Private / Self-Hosted AI engine?
Start with a free discovery call. We map your operations and show you exactly where Private / Self-Hosted AI creates the most leverage for your business.