How Can I Reduce My AI API Costs?

How Can I Reduce My AI API Costs?

Your AI feature launched six months ago. Users love it. The product team is celebrating another milestone. Then finance sends you the latest bill: $50,000 this month. Up 30% from last month. Again.

You open the API dashboard and stare at the numbers. Hundreds of thousands of requests flowing through GPT-5 and Claude Sonnet 4.5. Every single one. The junior developer who built the prototype defaulted to the premium models. Nobody questioned it. Now you are here, trying to figure out how to explain this to your CFO.

If you are spending more than $1,000 monthly on LLM APIs, you are probably overspending by 60-90%. There are a lot of savings to be unlocked by carefully selecting your models.

Why Your Bill Is So High

Walk through a typical day for your AI features. A user asks your chatbot about their order status. Your system sends the question to GPT-5 at $1.25 per million input tokens and $10 per million output tokens. The model processes it, generates a response, and sends it back. Total cost for that single interaction: about $0.02.

That seems reasonable until you multiply it by 100,000 requests per day. Suddenly you are spending $2,000 daily, or $60,000 monthly, on conversations that a cheaper model could handle for $6,000.

The math gets worse when you realize what you are actually paying for. GPT-5 can write complex code, solve advanced math problems, and reason through multi-step challenges. It achieves 74.9% on SWE-bench, handling real-world software engineering tasks. Classification tasks, sentiment analysis, and simple Q&A do not need GPT-5's capabilities.

Processing 1 million tokens in 2025 costs:

• GPT-5: $1.25 input / $10.00 output
• GPT-5-mini: $0.25 input / $2.00 output
• GPT-5-nano: $0.05 input / $0.40 output
• Claude Sonnet 4.5: $3.00 input / $15.00 output
• Claude Haiku 3.5: $0.80 input / $4.00 output

Most applications send roughly equal amounts of input and output tokens. So for a typical workload, you are looking at around $11 per million tokens with GPT-5, or $2.50 with Haiku 3.5. That is a 4.4x difference for tasks that often produce identical quality results.

Start By Understanding Where Money Goes

You cannot fix what you cannot see. The first step is figuring out which parts of your application are burning through tokens.

Many teams discover something surprising when they finally audit their usage: 20% of their features account for 80% of their costs. Maybe it is the document analysis feature that loads entire PDFs into context. Or the customer support bot that defaults to GPT-5 for every query, even "What are your hours?"

Set up logging that captures the basics. Which endpoint made the request. Which model processed it. How many tokens were used. What customer or feature triggered it. You need this data to make intelligent decisions.

Give yourself two weeks to implement comprehensive logging. The investment pays off immediately when you can point to specific features and say "this costs us $15,000 monthly and we can cut it by 70%."

Without this visibility, you are optimizing blind. With it, you know exactly where to focus.

The Manual Approach: Match Models to Tasks

Once you know where money is going, you can start making smarter decisions about model selection. Some tasks genuinely need GPT-5's reasoning capabilities. Most do not.

Think about the queries your application handles. Order status checks, simple FAQs, basic product recommendations. These need fast, accurate responses, not deep reasoning. GPT-5-nano at $0.05 per million input tokens handles them perfectly. Your users cannot tell the difference.

Then you have the medium complexity stuff. Multi-turn support conversations, basic code generation, content writing. GPT-5-mini at $0.25 per million input tokens or Claude Haiku 3.5 at $0.80 per million input tokens work great here. Still 5-10x cheaper than your current setup.

Reserve the premium models for where they matter. Complex bug analysis, multi-step reasoning tasks, sophisticated code generation. These justify paying $3-15 per million tokens because getting them wrong is expensive.

The challenge is implementing this manually. You write routing logic that checks the request type and selects the appropriate model. Maybe something like this in your codebase:

function selectModel(requestType: string) {
  if (requestType === 'faq' || requestType === 'simple_query') {
    return 'gpt-5-nano';
  }
  if (requestType === 'support_chat' || requestType === 'content_generation') {
    return 'gpt-5-mini';
  }
  return 'gpt-5'; // complex tasks
}

This works. Companies doing manual routing can save 50-70% on their API bills. But it comes with hidden costs.

Every time OpenAI releases a new model, you need to reevaluate. When GPT-5-mini improves enough to handle some GPT-5 tasks, you update your routing logic. When Anthropic drops their prices, you recalculate which provider makes sense for each task. When your PM changes a prompt, that FAQ handler might need a smarter model.

The engineering time adds up. The rules become technical debt. Even Ramp, one of the companies processing over 1 trillion tokens according to OpenAI DevDay 2025, does this routing manually. On the This Week in Startups podcast, Jason Calacanis asked about comparison shopping different models. The Ramp founder explained they identify which calls need larger models versus smaller ones manually. Jason noted: "Your engineers need to be really up to date on the models and how things are changing."

That is the reality of manual routing. It requires constant attention from engineers who need to stay current on every model release, pricing change, and performance benchmark.

The Better Way: Intelligent Routing

All these manual strategies work. But they all share the same fundamental problem: they require constant human attention.

Intelligent routing solves this by making decisions automatically based on the actual complexity of each request. The system learns from real performance data instead of relying on static rules.

For each request, the system analyzes the actual complexity, reasoning requirements, and context. It selects the optimal model based on your goals. Maybe you want minimum cost while maintaining quality. Maybe you need the fastest response. Maybe you want the highest quality regardless of price.

The system makes this decision in real time, typically adding 20-50 milliseconds. Then it routes your request to the selected provider and model. After processing, it records the results and learns from them.

The learning never stops. If a cheaper model consistently produces quality results for a certain type of request, confidence increases. If a routing decision produces poor output, the system adjusts. When new models launch with better price-performance ratios, they get incorporated automatically.

Consider what this means in practice. An application processing 10 million requests monthly, all going to Claude Sonnet 4.5 at $3 per million input tokens and $15 per million output tokens. With typical token ratios, that is about $30,000 monthly.

With intelligent routing analyzing each request:

• 60% of requests route to cheaper models like GPT-5-mini and Haiku 3.5
• 35% stay on mid-tier models
• 5% upgrade to premium models when complexity demands it

The math works out to roughly $7,200 monthly. That is $22,800 saved monthly, or $273,600 annually. More importantly, the system requires zero engineering maintenance. No updating rules when GPT-6 launches. No recalculating economics when Anthropic changes prices. No debugging why the routing logic broke after a prompt change.

Sansa provides intelligent routing as a drop-in replacement for your existing API clients. You keep your API keys on your infrastructure. Your data stays private. The routing decisions happen server-side, but execution happens from your servers.

Setting it up takes an afternoon:

import { Sansa } from 'sansa';

const sansa = new Sansa({
  apiKey: process.env.SANSA_API_KEY,
  providers: {
    anthropic: { apiKey: process.env.ANTHROPIC_API_KEY },
    openai: { apiKey: process.env.OPENAI_API_KEY },
  },
});

const result = await sansa.create({
  callName: 'customer-support',
  messages: messages,
  llmBaseline: { 
    provider: 'anthropic', 
    model: 'claude-sonnet-4-5-20240620' 
  },
});

The baseline model defines your quality threshold. Sansa only routes to cheaper alternatives when it can match or exceed that quality. If confidence is low, it falls back to your baseline automatically.

You get complete observability too. Which models handled which requests. Cost per customer, per feature, per team. Token usage trends. Quality metrics. Everything you need to understand and optimize your AI spending.

The economic case is straightforward. At typical SaaS margins, a dollar saved equals 12 dollars earned. If you save $20,000 monthly on API costs, that is equivalent to generating $240,000 in new annual revenue. Most finance teams care more about reducing a $50,000 monthly expense than adding a $600 monthly tool cost.

Comparing Your Options

If you are spending $2,000 monthly on AI, manual optimization might be overkill. If you are spending $200,000 monthly, you need automation.

Manual model selection saves 50-70% if you can maintain it. Best for teams with dedicated engineering resources who can keep routing logic updated as models change. Initial setup takes 2-4 weeks. Ongoing maintenance is continuous.

Intelligent routing saves 60-90% with zero maintenance. Best for anyone spending more than $1,000 monthly who wants automated optimization. Setup takes 2-4 hours. Maintenance is effectively zero.

What You Should Do This Week

Start with visibility. Instrument your application to track costs by feature, customer, and model. You cannot make good decisions without data.

Then decide on your routing strategy. If you are spending less than $5,000 monthly and have engineering bandwidth, manual routing might work. If you are spending more, or you want to focus your engineering time on building features instead of maintaining cost optimization logic, intelligent routing is the better path.

Calculate your potential savings with the ROI Calculator, or join the waitlist to get early access.