Multi-Provider AI Gateway
We built an AI gateway management platform for multiple LLM providers. Its gateway-platform split uses webhook-first synchronization, selective caching, and a PostgreSQL queue. The system supports global enterprise users on minimal infrastructure with about 99% uptime.
FLock needed reliable synchronization between LiteLLM and its platform. It had to track micro-usage balances precisely, process hundreds of thousands of daily usage logs, avoid slowing the gateway, and run fault-tolerant internal communication on serverless infrastructure without a native queue.
Multi-Provider AI Gateway
LiteLLM is the API gateway for model requests. A Next.js platform manages users, balances, API keys, and orchestration. API calls cover real-time interactions. Webhooks report usage asynchronously. Caching covers frequent gateway data. A daily settlement job repairs missed webhooks, and a parallel pipeline reconciles large log volumes. Supabase PostgreSQL stores data and runs a custom background queue on Vercel.
Vercel and Supabase minimized deployment overhead while supporting high availability. Caching reduces LiteLLM load by trading a little freshness. Webhook-first sync uses a settlement job to recover from webhook failures. Queue processing protects the database and controls parallelism. The PostgreSQL queue required custom serverless work.
Gateway-state caching reduces repeated LiteLLM calls under load. Hot usage-log and balance paths are indexed and tuned. A PostgreSQL queue processes settlement batches in parallel. The chat playground streams responses for low-latency model tests. Daily settlement reconciles failed webhooks and keeps usage data consistent.
Supported global enterprise users on Vercel, Supabase, and LiteLLM.
Achieved about 99% uptime.
Maintained performance under heavy API usage through caching and indexing.
Protected data accuracy with reconciliation and settlement jobs.
High-throughput system design for financially sensitive usage data.
Deliberate eventual-consistency handling in distributed systems.
Serverless infrastructure under non-trivial workloads.
Queue and parallel processing without dedicated queue infrastructure.
Webhooks need reconciliation. Selective cache boundaries work better than blanket caching. PostgreSQL can provide a lightweight queue when infrastructure is constrained.
Applicable to AI platforms with multiple model providers and usage-based billing. It also fits developer platforms needing gateway abstraction without vendor lock-in on lean infrastructure.
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