Private AI Infrastructure
Deploy AI capabilities within your own infrastructure. We build secure, self-hosted AI platforms that give you full control over your data, models, and compute.
What We Deliver
- On-premise and VPC-hosted LLM deployment and serving
- GPU cluster provisioning, orchestration, and auto-scaling
- Model serving infrastructure with load balancing and failover
- Data privacy frameworks aligned to SOC 2, HIPAA, and GDPR
- Hybrid cloud AI architectures bridging private and public compute
- Cost modeling and capacity planning for GPU workloads
Typical Use Cases
Regulated industries
Healthcare, financial services, and government organizations that cannot send data to third-party APIs due to compliance requirements.
Data sovereignty
Organizations operating across jurisdictions that need AI workloads to remain within specific geographic boundaries.
High-volume inference
Workloads where API costs become prohibitive at scale — processing millions of documents, serving thousands of concurrent users, or running continuous analysis.
Proprietary model deployment
Teams with fine-tuned or custom-trained models that need secure, performant serving infrastructure without vendor lock-in.
How We Work
Infrastructure Assessment
We audit your current compute, networking, and security posture to determine the optimal deployment architecture — on-premise, private cloud, or hybrid.
Architecture & Cost Modeling
Detailed architecture design with GPU sizing, networking topology, and total cost of ownership analysis comparing self-hosted vs. API approaches.
Build & Deploy
Infrastructure as Code deployment with automated provisioning, model serving setup, monitoring, and security hardening. Everything reproducible.
Optimize & Transfer
Performance tuning, cost optimization, and knowledge transfer to your operations team with comprehensive runbooks and documentation.
Technology Expertise
Frequently Asked Questions
When does self-hosted AI make more sense than API-based?
When you have regulatory constraints on data movement, when API costs exceed $10-15K/month and are growing, when you need sub-20ms latency, or when you need to run models that aren't available via APIs. We help you model the break-even point.
What GPU infrastructure do you recommend?
It depends on workload. For inference-heavy use cases, we often start with NVIDIA A10G or L4 instances. For training or large model serving, A100 or H100 clusters. We always right-size first — over-provisioning GPUs is the most common mistake we see.
How do you handle model updates in a self-hosted environment?
We build blue-green deployment pipelines for models — new versions are deployed alongside existing ones, traffic is shifted gradually based on evaluation metrics, and rollback is automatic if quality degrades.
Can you integrate private AI with our existing cloud infrastructure?
Yes. Most deployments are hybrid — private inference for sensitive workloads, cloud APIs for non-sensitive tasks. We design the routing layer and security boundaries between them.
What about ongoing operational support?
We build systems to be operated by your team. That means comprehensive monitoring dashboards, automated alerting, capacity planning tools, and detailed runbooks. We can provide ongoing advisory support if needed.
Ready to build with us?
Tell us about your project and we'll scope a pragmatic path forward.