Enterprise AI Infrastructure at Scale
Building a unified AI gateway to enable thousands of users across a Fortune 500 organization
The Challenge
A Fortune 500 consumer goods company was experiencing rapid, ungoverned adoption of AI tools across the organization. Teams were managing their own API keys, using different models with no standardization, and costs were impossible to track. Security and compliance teams were concerned, and new teams faced a steep learning curve to even get started with AI. The company needed infrastructure that could enable innovation while maintaining control.
The Problem
Ungoverned AI usage, scattered API keys, zero cost visibility, and long onboarding times
The Goal
Enable enterprise-wide AI adoption with governance, cost control, and flexibility
The Scale
Support thousands of users across diverse use cases and teams
What We Built
As part of the engineering team, we built a unified AI gateway that became the central infrastructure for all LLM usage across the enterprise. The system needed to be flexible, performant, and scalable enough to handle the demands of thousands of users with diverse needs.
1. Unified Gateway Architecture
We built a centralized AI gateway that routed requests to multiple LLM providers—including OpenAI, Claude, and internally developed models. Teams got a single, consistent interface regardless of which model they were using, dramatically simplifying development and reducing onboarding time.
2. Enterprise-Scale Infrastructure
Using LiteLLM and Kubernetes, we built infrastructure that could handle enterprise-scale load with high availability and performance. The system needed to be reliable enough to power mission-critical applications across the organization.
We also integrated Nvidia Triton to serve internally developed models, ensuring the company could deploy proprietary AI capabilities alongside commercial offerings—all through the same unified interface.
3. Governance and Cost Control
The gateway provided centralized visibility into usage patterns, costs, and model selection across the organization. Security and compliance teams could ensure proper governance while teams retained the flexibility to experiment and innovate.
The Results
The AI gateway transformed how the organization approached AI adoption—moving from chaos to control while accelerating innovation:
- Thousands of users gained access to multiple AI models through a single, unified interface
- Dramatically reduced onboarding time for teams wanting to use AI, lowering from weeks of setup to immediate access
- Full cost visibility and control across all AI usage, enabling data-driven decisions about model selection and spend
- Seamless integration of internal models alongside commercial providers, giving the organization maximum flexibility
- Continues to power enterprise-wide AI workloads today, handling mission-critical applications at scale
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