LLM Proxy vs LLM Gateway: What's the Difference and Which Do You Need?

You've got an app hitting the OpenAI API. Costs are climbing, you have no idea which model is burning the budget, and a single rogue loop could run up a $50 charge before you notice. You start searching for a solution and land on two terms: LLM proxy and LLM gateway.

They sound interchangeable. They're not.

This article breaks down what each one actually does, where the line between them falls, and how to figure out which you need without overbuilding.

What Is an LLM Proxy?

An LLM proxy is a local intermediary that sits between your application and an AI provider. Your code sends requests to the proxy instead of directly to OpenAI, Anthropic, or whatever provider you're using. The proxy forwards those requests, intercepts the responses, and gives you visibility and control you'd otherwise have to build yourself.

Think of it as a transparent layer. From your app's perspective, nothing changes — you're still making standard API calls. From the proxy's perspective, it sees everything: which model you called, how many tokens you used, what it cost, and whether the provider responded.

The core value proposition is observability and cost control without changing your application logic.

What a proxy actually does

A well-implemented LLM proxy handles:

• Cost tracking — per-request token counts x model pricing, accumulated over time
• Response caching — exact-match cache to avoid paying for repeat queries
• Fallback routing — if your primary provider fails, fail over to a secondary
• Budget enforcement — hard caps that block or downgrade requests when spend exceeds a threshold
• Anomaly detection — alerting when costs spike, loops run away, or token usage explodes

Code example

Pointing your existing OpenAI client at a local LLM proxy is a one-line change:

import OpenAI from "openai";

const openai = new OpenAI({
  apiKey: process.env.OPENAI_API_KEY,
  baseURL: "http://localhost:4100/openai", // point at the proxy
});

const response = await openai.chat.completions.create({
  model: "gpt-4o",
  messages: [{ role: "user", content: "Summarize this contract." }],
});

Your provider key stays on your machine. The proxy adds tracking, caching, and fallback — and your application code is unchanged.

Installing an LLM proxy

The fastest way to run an AI proxy locally is RelayPlane:

npm install -g @relayplane/proxy
relayplane init
relayplane start

That's it. Dashboard opens at http://localhost:4100. No Docker, no YAML files, no cloud account required. It installs as a global npm package and runs as a local service — or as a persistent system daemon via relayplane autostart.

What Is an LLM Gateway?

An LLM gateway is an enterprise infrastructure component. Where a proxy focuses on developer-side observability, a gateway is designed to govern AI access across an organization — multiple teams, multiple applications, multiple users — with centralized control.

Gateways typically live in your cloud infrastructure (Kubernetes, a managed service, etc.) and add a layer of policy enforcement on top of the routing.

What a gateway adds

Enterprise gateways are built around:

• Multi-tenant access control — different teams get different rate limits, model permissions, and cost budgets
• Centralized policy enforcement — a single admin surface that applies rules across every application in the org
• Audit logging at scale — every request logged to a SIEM or data warehouse for compliance
• SSO and identity integration — tying AI usage to organizational identities
• Approval workflows — certain model calls require human review before execution
• Network-level deployment — runs as a service mesh component or sidecar, not a developer tool

The tradeoff is complexity. Setting up an enterprise gateway correctly involves infrastructure, IAM policies, network configuration, and ongoing ops. It's the right choice when the problem is organizational governance, not individual developer cost visibility.

Popular gateway products in this space include Kong AI Gateway, Apigee, and AWS Bedrock's native controls. LiteLLM and Portkey occupy a middle tier, offering gateway-like features with varying degrees of cloud dependency.

Key Differences: LLM Proxy vs LLM Gateway

The core distinction: a proxy solves problems you have right now on your machine. A gateway solves problems that emerge when AI usage scales across an organization.

When You Need a Proxy

You need an LLM proxy if any of these describe your situation:

You don't know what you're spending. API costs accumulate invisibly. A proxy gives you per-request cost data — which model, how many tokens, what it cost — without requiring any changes to your application.

You want fallback without writing it yourself. When OpenAI goes down, do you want your app to hard-fail or quietly route to a backup? A proxy handles that transparently.

You're running local models alongside cloud APIs. If you're mixing Ollama or other local models with hosted providers, a proxy gives you a single interface with consistent routing.

You want to cache expensive responses. Exact-match caching means identical queries don't hit the API twice. For workflows with repeated or similar prompts, this compounds quickly.

You want anomaly detection without building it. A budget cap that blocks requests when daily spend hits $50 is genuinely useful. A proxy enforces this at the infrastructure level so a runaway agent loop doesn't become a billing problem.

You're working solo or on a small team. All of this is achievable with minimal config. You don't need Kubernetes for cost tracking.

When You Need a Gateway

A gateway becomes necessary when the problem is organizational, not individual:

Multiple teams share the same API keys. You need attribution, rate limiting per team, and the ability to revoke access for a specific group without affecting everyone else.

Compliance requires full audit trails. Healthcare, finance, and legal applications may require every AI request to be logged and attributed to an identity. That's gateway territory.

You need to enforce model policies at the org level. "No GPT-4 for interns" or "all legal team queries must use our approved system prompt" requires centralized policy enforcement, not per-developer config.

You're running AI at scale across many services. When you have dozens of microservices all calling LLMs, a gateway in the service mesh is cleaner than each service running its own proxy.

If none of those apply to you, a gateway will add infrastructure burden without proportional benefit.

Where RelayPlane Fits

RelayPlane is an npm-native LLM proxy built for developers who want real cost control without the ops overhead of an enterprise gateway.

npm install -g @relayplane/proxy
relayplane init
relayplane start

It covers the 90% case: cost tracking, caching, fallback routing, budget enforcement, and anomaly detection — running locally, persisting as a system service, with a dashboard at http://localhost:4100.

What it does well:

• 11 direct providers — Anthropic, OpenAI, Google Gemini, xAI/Grok, OpenRouter, DeepSeek, Groq, Mistral, Together, Fireworks, Perplexity, each with native API routing
• Task-aware routing — map simple/medium/complex tasks to different models; use cascade mode to try cheap first and fall back to expensive
• Budget enforcement — daily, hourly, and per-request limits with configurable actions: block, warn, downgrade, or alert
• Anomaly detection — runaway loop detection, cost spike alerts, token explosion warnings
• Aggressive caching — gzipped disk cache with relayplane cache CLI for inspection and management
• Circuit breaker — transparent failover if the proxy itself encounters an error
• System service install — relayplane autostart installs as a systemd/launchd daemon

The free tier includes the full proxy, unlimited requests, and a local dashboard with 7-day history. No credit card, no cloud dependency, no container runtime.

For teams that need a cloud dashboard, cost digests, and routing recommendations, the Starter plan is $9/mo. Governance features and team access come in at higher tiers — but most developers never need that far.

RelayPlane is not trying to be a gateway. It's a proxy that runs where you work, gives you the data you need, and stays out of the way.

Summary

The distinction is scope:

• An LLM proxy intercepts your requests, tracks costs, adds caching and fallback, and enforces budget limits. It solves the observability and cost control problem for a developer or small team in minutes.
• An LLM gateway governs AI access across an organization — multi-tenant access control, compliance logging, centralized policy. It solves the organizational governance problem at the cost of significant infrastructure complexity.

If you're a developer with an app that calls Claude or GPT-4 and you want to stop flying blind on costs, start with a proxy. You can set one up before you finish your coffee.

npm install -g @relayplane/proxy && relayplane init && relayplane start

If you later need to coordinate AI access across twenty teams with SSO integration and compliance audit trails, that's the moment to evaluate gateways. Most projects never get there.

RelayPlane is an open-source LLM proxy. Install it at relayplane.com or via npm: npm install -g @relayplane/proxy.