Run AI models locally with edge inference
Set up local-first AI inference on your own hardware — no cloud, no API keys, full privacy.
Dev Tools · Project · Intermediate · 90 min · Ollama, llama.cpp, vLLM
Dev ToolsIntermediateProject
Build OpenAI-compatible API proxies
Set up proxy servers that provide a unified OpenAI-compatible interface to multiple AI providers — cost optimization and failover included.
75 minLiteLLM, OpenRouter, Docker, Python10xCareer Team
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$99
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What you'll learn
- Deploy an OpenAI-compatible proxy server for multi-provider access
- Implement cost-based routing across AI providers
- Build failover and rate limit handling for production reliability
- Track and optimize AI API spend across teams
Tags
proxyapiopenai-compatiblecost-optimizationinfrastructuretrending
Overview
Subscription proxying to OpenAI-compatible APIs (65K+ stars) lets you route AI requests across providers through a single interface. This is essential for cost management, failover, and team access control.
What you'll build
- A proxy server that routes requests to OpenAI, Anthropic, and local models
- A cost-tracking dashboard for monitoring API spend
- A failover system that auto-switches providers on errors
Tools covered
- LiteLLM — Unified API proxy for 100+ LLM providers
- OpenRouter — Multi-provider routing service
- LocalAI — Self-hosted OpenAI-compatible server
What you'll learn
- The OpenAI API specification and compatibility layers
- Setting up LiteLLM as a proxy gateway
- Cost optimization: routing cheap tasks to cheap models
- Failover and rate limit handling across providers
Why this matters
No one should be locked into a single AI provider. API proxying gives you flexibility, cost control, and resilience.
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