Why We Migrated From a Self-Built Gateway to LiteLLM

2026-07-09 · Source: LLM on Medium · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering, Cloud Computing & IT Infrastructure · Depth: Intermediate, medium

Summary

A team migrated its internal AI platform from a self-built FastAPI-based OpenAI-compatible REST API gateway to the open-source LiteLLM proxy due to growing usage complexity and maintenance challenges. The original gateway, built as a single Docker container, required code changes and server restarts for every model update, leading to downtime. Its fixed API format also struggled with diversifying usage patterns, including Claude Code and Codex, and lacked features like hierarchical key management and usage logging. LiteLLM resolved these issues by enabling model changes via an Admin API without redeployment or downtime, supporting continuous serving and per-model fallback. While the migration introduced trade-offs like limited key visibility and reduced customization control, practical deployment on a GPU server (for consolidation) and API-driven model management streamlined operations. The team also navigated key migration, Claude Code compatibility, and a security patch that served as a rolling-update stress test, highlighting both the benefits and dependencies of open-source solutions.

Key takeaway

For MLOps Engineers managing internal AI platforms, if you are considering a custom LLM gateway, evaluate the long-term maintenance burden against open-source alternatives. Your custom solution may quickly become unstable or inflexible as usage diversifies, requiring significant effort for model changes and feature additions. Opting for a robust open-source proxy like LiteLLM can streamline operations, reduce downtime, and provide essential features like key management and access control, even with minor trade-offs in customization. Plan for key migration and specific model configurations during rollout.

Key insights

Migrating from custom LLM gateways to open-source proxies like LiteLLM improves scalability and maintainability for diverse AI platform usage.

Principles

• Custom solutions incur high maintenance costs with evolving requirements.
• Open-source tools offer rapid feature development and community support.
• Centralize infrastructure for monitoring and management efficiency.

Method

Migrate LLM gateway by first defining models via API, then reissuing keys, and finally configuring specific model compatibilities like Claude Code.

In practice

• Use Admin API for dynamic model management to avoid redeployments.
• Plan for key migration as existing and new keys differ.
• Test specific model compatibilities (e.g., Claude Code) post-migration.

Topics

• LLM Gateway
• LiteLLM
• API Management
• Model Management
• Kubernetes
• Open-Source Software

Best for: AI Engineer, MLOps Engineer, Software Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by LLM on Medium.