Step-by-step guide

LLM Proxy: One Endpoint, Every AI Provider

An LLM proxy sits between your app and model providers, giving you a single integration point. Here is how to set one up and why it matters for production AI.

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Start on cheap auto-routed models first, then move up only when your workload truly needs premium manual control.

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1

Why you need an LLM proxy

Without a proxy, you integrate each provider separately - different SDKs, authentication schemes, error formats, and billing. When OpenAI goes down at 2am, your app goes down too. A proxy layer decouples your application from any single provider: one endpoint, one key, one error format, automatic failover.

2

Choose your proxy approach

You have three options: (1) Self-host an open-source proxy like LiteLLM - maximum control, you own uptime. (2) Use a managed proxy like LLMWise - zero infrastructure, built-in routing and failover. (3) Build your own - maximum customization, maximum maintenance burden. For most teams, managed is the right starting point. You can always move to self-hosted later.

3

Set up LLMWise as your LLM proxy

Sign up at llmwise.ai and grab your API key. Point your application to https://llmwise.ai/api/v1/chat instead of your current provider endpoint. Use the same role/content message format you already use. Set the model parameter to 'auto' for intelligent routing, or specify a model like 'claude-sonnet-4.5' for direct access.

4

Configure failover chains

Enable Mesh mode to get automatic failover. LLMWise monitors each provider's health in real time: when a model starts returning errors, traffic shifts to the next healthy model within seconds. For OpenRouter specifically, sustained rate-limit responses trigger a brief cooldown before retrying. Your app stays online regardless of which provider has issues.

5

Add cost controls

Set up credit-based budgeting to prevent runaway costs. LLMWise reserves credits before each call and settles to actual usage after the response completes. The auto-router automatically picks cheaper models for simple queries - classification, extraction, and Q&A go to budget models while complex reasoning stays on frontier models.

6

Monitor and optimize

Use the LLMWise dashboard to track per-model latency, cost, and error rates. The optimization engine analyzes your historical request data and recommends routing changes - primary model selection, fallback chains, and model-task assignments based on your actual usage patterns.

Evidence snapshot

LLM Proxy: One Endpoint, Every AI Provider execution map

Operational checklist coverage for teams implementing this workflow in production.

Steps
6
ordered implementation actions
Takeaways
4
core principles to retain
FAQs
4
execution concerns answered
Read time
12 min
estimated skim time
Key takeaways
✓An LLM proxy eliminates vendor lock-in and gives you automatic failover across providers
✓Managed proxies like LLMWise add cost optimization and orchestration without infrastructure overhead
✓Automatic failover keeps your app online when individual providers go down
✓Cost savings of 25-40% are typical when smart routing replaces hard-coded model selection

Common questions

What is an LLM proxy?
An LLM proxy is a forwarding layer between your application and LLM providers. It translates your API calls into provider-specific formats, handles authentication, and can add features like failover, caching, and cost tracking. Think of it as a reverse proxy specifically designed for AI model APIs.
What is the difference between an LLM proxy and an LLM gateway?
A proxy focuses on forwarding and format translation. A gateway adds intelligence: routing decisions, failover logic, cost optimization, and multi-model orchestration. LLMWise is a gateway - it proxies your requests but also routes them intelligently, handles failover, and offers compare/blend/judge modes.
Can I use an LLM proxy with my existing API keys?
Yes. LLMWise supports BYOK (Bring Your Own Key) - pass your existing OpenAI, Anthropic, or Google API keys and route through LLMWise for failover and analytics while paying the provider directly. No credit charges when using BYOK.
Does an LLM proxy add latency?
A well-implemented proxy adds 5-20ms of overhead - negligible compared to the 200-2000ms of actual LLM inference time. LLMWise's routing decision adds microseconds because it uses regex-based heuristics, not an ML classifier.

Start on Auto, move up only when you need it

Free preview, Starter for the Auto lane, Teams for manual GPT, Claude, and Gemini Pro access. Add-on credits kick in after included plan tokens are used.

Start on cheap auto-routed models first, then move up only when your workload truly needs premium manual control.

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