Case Study
Secure Gen-AI: Local LLM Hosting Platform
An on-prem enterprise LLM hosting platform that empowers teams to build, test, and deploy local models securely, bringing structure and control to 8,000+ users.
100%
HIPAA and GDPR compliance
0%
of data leaves the company's infrastructure
Customer
Employees count
8000+
Industry
Digital media, marketing services, and software
Our services
AI & MLOps; Production-ready AI & MLOps; Rapid product development
Technologies
LiteLLM
Nvidia
Kubernetes
Llama
Docker
INTRODUCTION
Global digital enterprise spanning healthcare, legal, and automotive sectors was facing an internal pressure: growing AI interest had led to teams sharing a limited pool of GPU resources in an unmanaged environment. Projects interfered with one another, performance was unpredictable, and scaling innovation became nearly impossible.
Recognizing that the issue was more operational than technical, Client partnered with Tight Line to design a foundational platform that could bring structure, visibility, and control to how GPU resources and AI models were managed across the organization.
OBJECTIVE
The goal was to eliminate resource contention, streamline team collaboration, and bring order to a chaotic environment where GPU servers were shared without governance.
Unable to share sensitive data with commercial AI services, the Client needed a secure, self-hosted platform that kept all data in-house and properly governed, while still supporting scalable innovation.
Challenges
High risk of data leaving the company
LLM providers with unacceptable or unclear compliance standards
Teams competing for shared GPU resources
No centralized visibility into resource usage
OUR APPROACH
We recommended an open-source–first architecture centered on a LiteLLM gateway fronting Kubernetes model pools.
The gateway provides authentication, rate-limiting, and model routing; the pools place models on high-compute A100s for large LLMs and lower-compute nodes for embeddings and rerankers. Chatbots, assistants, and agent apps all connect over streaming HTTP to a single endpoint. This standardizes access, removes noisy-neighbor effects, and keeps all data in-house while remaining provider-agnostic and future-proof.
Benefits
One OpenAI-compatible endpoint for all uses backed by virtual keys, RBAC, and per-team quotas.
Kubernetes model pools map workloads to appropriate hardware: large LLMs on high-memory GPUs, utility models (embeddings/rerankers) on commodity GPUs.
Right-sized GPU placement, rate limiting, and shared low-compute pools help reduce spend.
Authentication, rate limiting, and centralized audit provide governance. Namespaces, network policies, and secrets management keep sensitive data in-house and compliant.
Provider-neutral routing and compatibility with evolving Kubernetes inference specifications keep the platform adaptable as models and runtimes change.
Results
On-premises data retention
No data or models leave the organization
Transparency and accountability
Deliver GPU usage across the organization
Faster and safer LLM experimentation
Empowered without relying on cloud infrastructure
Scalable, reproducible AI System
Built to align with enterprise-grade processes
Conclusion
By tackling disorganization, resource contention, and governance concerns head-on, the client turned local LLM hosting into a repeatable system for innovation at scale which is secure, compliant, and built for growth.
AI enablement is no longer just about powerful models, it’s about creating the environment where people can build with confidence.
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