AI / Developer Tools Production System

FLock AI Platform

AI Gateway Management Platform for Multi-Provider LLM Orchestration

LLM Providers

~99%

Uptime

100K+

Daily Logs

Serverless

Deployment

Executive Summary

We built FLock AI Platform, an enterprise-grade AI gateway management platform that enables organizations to access multiple LLM providers through a unified interface. Using a decoupled gateway-platform model with webhook-first synchronization, selective caching, and a custom PostgreSQL-based queue system, we achieved ~99% uptime and supported global enterprise users on minimal infrastructure.

The Problem

FLock AI Platform required building a highly reliable system that maintains accurate synchronization between the API gateway (LiteLLM) and the platform under real-world usage conditions. Key challenges included ensuring financial-like precision for balance tracking with micro-usage units, handling hundreds of thousands of usage logs daily without degrading gateway performance, avoiding frequent reads that would slow the gateway, building fault-tolerant synchronization mechanisms, maintaining secure internal communication, and providing a centralized control plane — all while operating on serverless infrastructure without native queue support.

LLM Providers

100K+

Daily Logs

Global

Enterprise Users

Services Delivered

AI Integration Dedicated Team

AI Gateway Management Platform for Multi-Provider LLM Orchestration

Architecture Overview

Data Layer

LiteLLM PostgreSQL Supabase

Backend & Orchestration

Next.js Custom Queue

Frontend

Next.js shadcn

Infrastructure

Vercel Supabase

Key Technical Decisions

System Design

The architecture is centered around a decoupled gateway-platform model. LiteLLM acts as the API gateway handling all model requests. The platform, built as a Next.js fullstack application, manages users, balances, API keys, and orchestration. Communication between gateway and platform uses API calls for real-time interactions and webhooks for asynchronous usage reporting. Selective caching is applied to frequently accessed gateway data. A daily settlement job reconciles failed or missed webhook events. A parallel processing pipeline handles large-scale log reconciliation. PostgreSQL via Supabase serves as the primary data store, with a custom PostgreSQL-based queue system managing background processing on Vercel.

Key Decisions

Vercel and Supabase were chosen to simplify deployment and enable high availability with minimal infrastructure overhead. Caching was introduced for gateway-dependent endpoints to reduce load on LiteLLM at the cost of slight data staleness. Webhook-first synchronization with a fallback settlement job ensures eventual consistency even under webhook failure scenarios. Queue-based processing for settlement jobs prevents database overload and enables controlled parallel execution. Tradeoffs included accepting that cached endpoints are not fully real-time, settlement jobs introduce delayed consistency for edge cases, and a PostgreSQL-based queue required custom implementation due to serverless constraints.

Implementation Highlights

A caching layer was applied to endpoints retrieving gateway state, reducing repeated calls to LiteLLM under high load. Database optimization included indexing hot paths for API usage logs and balance lookups, with tuned queries for high-frequency reads and writes. A custom queue system built on PostgreSQL enabled parallel processing of large log batches during settlement. Real-time streaming was implemented in the chat playground, allowing users to test models with low latency. A daily settlement job reconciles failed webhook events, ensuring eventual consistency in financial and usage data.

Results & Validation

Successfully supported enterprise users globally on minimal infrastructure (Vercel + Supabase + LiteLLM).

Achieved ~99.0% uptime.

Maintained high performance under heavy API usage through caching and indexing.

Ensured data accuracy via reconciliation pipelines and settlement jobs.

Key Insights

Ability to design high-throughput, financially sensitive systems.

Strong handling of eventual consistency in distributed systems.

Practical use of serverless infrastructure under non-trivial workloads.

Efficient queue and parallel processing design without dedicated queue infrastructure.

A key insight was that webhook systems must always be paired with reconciliation mechanisms, gateway-dependent systems require strategic caching boundaries (not blanket caching), and PostgreSQL can effectively act as a lightweight queue system when infrastructure is constrained.

Who This Applies To

This architecture is directly applicable to companies building AI platforms requiring multi-provider model orchestration, usage-based billing systems with high-frequency event tracking, and developer platforms that need API gateway abstraction without vendor lock-in. It is particularly relevant for teams seeking to operate globally with lean infrastructure while maintaining reliability and performance.

AI Gateway Multi-Provider LLM Usage-Based Billing Serverless Architecture Eventual Consistency

Technologies Used

Backend

Next.js shadcn

Frontend

Vercel

Infrastructure

Supabase PostgreSQL LiteLLM

Data & Integrations

Custom Queue Webhooks Caching Layer

Patterns & Techniques

Streaming Settlement Jobs Parallel Processing GitHub

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