Tollbooth AI — Multi-Tenant LLM Gateway & AI Governance Platform
A multi-tenant LLM gateway that provides secure AI access control, usage tracking, cost management, and governance features for AI applications.
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Overview
Project context and problem focus.
Tollbooth AI is a backend-focused AI infrastructure platform designed to manage and govern LLM usage across multiple tenants. The platform provides secure API access, tenant isolation, role-based permissions, provider routing, usage metering, and cost attribution. The system was designed around production backend concepts including asynchronous processing, event-driven architecture, database modeling, authentication, authorization, and scalable API design. Kafka-based event processing handles usage tracking and audit workflows separately from the main request lifecycle.
Purpose
To simplify the management of AI infrastructure by providing developers and organizations with secure, observable, and cost-controlled access to large language models.
Target Users
AI application developers, SaaS companies Engineering teams integrating LLM APIs Organizations requiring AI usage governance
Case Study Deep-Dive
The Problem & The Architectural Solution
Addressing real-world performance, concurrency, and reliability constraints.
Problem Statement
Implemented Solution
Impact & Outcomes
Measurable Results & Benchmarks
Reduced API response errors
Fixed bugs
Improved feature delivery speed
Supported modules/features
Technologies used in this project.
A clear technical stack overview for recruiters, clients, and engineering reviewers.
Project Technology Stack
Core features and implementation highlights.
This section explains the important functional parts of the project and what was implemented.
Multi-tenant architecture
API key management
JWT authentication
Role-based access control
Rate limiting
Budget enforcement
Provider routing
Usage metering
Audit logging
PostgreSQL schema design
Prisma ORM
Relational data modeling
Usage tracking tables
Tenant isolation
Docker containerization
Environment-based configuration
Service deployment workflow
Problems faced and how they were solved.
A project becomes stronger when the implementation process shows real problem-solving decisions.
Challenge
Managing usage tracking without slowing down LLM requests.
Solution
Implemented Kafka-based asynchronous processing to move metering and audit operations outside the main request flow.
Challenge
Supporting multiple tenants securely.
Solution
Designed tenant-aware authentication, authorization, API keys, and access-control layers.
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