---
id: blog-cut-llm-costs-with-request-routing
slug: cut-llm-costs-with-request-routing
date: 2026-02-14
title: "How We Cut Our LLM Costs 60% With Request Routing"
summary: "A practical breakdown of how intelligent routing, caching, and model selection through an LLM gateway can dramatically reduce your AI infrastructure costs."
categories: ["Product"]
image:
  src: "/blog/cut-llm-costs-with-request-routing.png"
  alt: "How We Cut Our LLM Costs 60% With Request Routing"
  width: 1408
  height: 768
---

Most teams start their AI journey the same way: pick a flagship model, point all requests at it, and watch the bill climb. It works — until you're spending thousands per month and realize that 70% of your requests didn't need a $10/M-token model in the first place.

Here's how a gateway-based approach to LLM routing can cut costs by 60% or more without sacrificing quality where it matters.

## The Problem: One Model for Everything

Consider a typical AI-powered SaaS application that handles 10,000 requests per day with a mix of tasks:

- Classifying support tickets
- Generating email drafts
- Summarizing documents
- Answering complex technical questions
- Extracting structured data from forms

If you run everything through GPT-5 ($1.25 input / $10.00 output per 1M tokens), you're paying flagship prices for tasks that a model 10x cheaper could handle just as well.

**Monthly cost with GPT-5 only:** ~$1,875/month (assuming 1K input + 500 output tokens average)

## Strategy 1: Route by Complexity

Not every request needs your best model. By categorizing requests and routing them to appropriate model tiers, you immediately cut costs on the majority of your traffic.

| Request Type                        | % of Traffic | Model            | Cost per 1M Output |
| ----------------------------------- | ------------ | ---------------- | ------------------ |
| Simple (classification, extraction) | 70%          | GPT-4.1 Nano     | $0.40              |
| Moderate (summarization, drafts)    | 20%          | Gemini 2.5 Flash | $2.50              |
| Complex (reasoning, analysis)       | 10%          | GPT-5            | $10.00             |

**Monthly cost with routing:** ~$270/month

That's an **85% reduction** from using GPT-5 for everything — and users won't notice the difference on simple tasks.

## Strategy 2: Response Caching

Many LLM requests are repetitive. Support ticket classifiers, FAQ responses, and template-based generations often produce identical or near-identical outputs for similar inputs.

With gateway-level caching:

- Identical requests return cached responses instantly
- Cache hit rates of 15-30% are common for production apps
- Cached responses have zero token cost and near-zero latency

A 20% cache hit rate on our 10,000 daily requests means 2,000 fewer billable requests per day.

**Additional savings from caching:** ~15-20% on top of routing savings

## Strategy 3: Provider Arbitrage

The same model quality tier is priced differently across providers. An LLM gateway lets you compare and switch without code changes:

| Tier     | Option A                      | Option B                       | Savings |
| -------- | ----------------------------- | ------------------------------ | ------- |
| Flagship | Claude Opus 4.6 ($25/M out)   | GPT-5 ($10/M out)              | 60%     |
| Mid-tier | Claude Sonnet 4.5 ($15/M out) | Gemini 2.5 Flash ($2.50/M out) | 83%     |
| Budget   | Claude Haiku 4.5 ($5/M out)   | GPT-4.1 Nano ($0.40/M out)     | 92%     |

When you're locked into a single provider, you can't take advantage of pricing differences. A gateway gives you the flexibility to pick the best price-to-quality ratio for each use case.

## Strategy 4: Automatic Fallback

Provider outages happen. Without a fallback strategy, an outage means downtime — and downtime means lost revenue that dwarfs any LLM cost savings.

With gateway-level fallback:

1. Primary request goes to your preferred provider
2. If it fails, the gateway automatically retries with an alternative provider
3. Your application stays up, and users never notice

This isn't just a cost strategy — it's a reliability strategy that happens to also give you pricing flexibility.

## The Combined Effect

Putting it all together for our 10,000 requests/day scenario:

| Strategy                 | Monthly Cost | Savings vs. Baseline |
| ------------------------ | ------------ | -------------------- |
| Baseline (GPT-5 only)    | $1,875       | —                    |
| + Complexity routing     | $270         | 85%                  |
| + Response caching (20%) | $216         | 88%                  |
| + Provider arbitrage     | ~$180        | **90%**              |

The exact numbers depend on your traffic patterns, but the principle holds: most teams are dramatically overspending on LLM costs because they're using a single expensive model for everything.

## Getting Started

LLM Gateway handles all of this out of the box:

- **Smart routing** across 200+ models from every major provider
- **Response caching** with Redis for instant repeated queries
- **Automatic fallback** when providers go down
- **Cost tracking** so you can see exactly where your money goes

```bash
curl https://api.llmgateway.io/v1/chat/completions \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "gpt-4.1-nano",
    "messages": [{"role": "user", "content": "Classify this ticket: My password reset email never arrived"}]
  }'
```

Switch models by changing a single string. No SDK changes, no code rewrites.

**[Start saving on LLM costs](/signup)** | **[Estimate savings with the Token Cost Calculator](/token-cost-calculator)** | **[Compare model pricing](/models)** | **[Read the docs](https://docs.llmgateway.io)**
