The Hidden Cost of AI

AI models are incredibly powerful, but they can also be incredibly expensive. A single GPT-4 request might cost $0.01 — that's nothing, right? But at 1 million requests per month, that's $10,000.

In this guide, we'll share battle-tested strategies to reduce your AI costs by up to 60% without sacrificing quality.

Strategy 1: Choose the Right Model

The most impactful optimization is using the right model for each task.

Model Cost Comparison

Model

Input/1M

Output/1M

Best For

GPT-4o	$2.50	$10.00	Complex reasoning
GPT-4o-mini	$0.15	$0.60	General tasks
Claude 3.5 Sonnet	$3.00	$15.00	Long documents
Claude 3.5 Haiku	$0.25	$1.25	Fast responses
Gemini 2.0 Flash	$0.075	$0.30	Multimodal, speed
DeepSeek-V3	$0.27	$1.10	Best value

Decision Framework

Simple FAQ/Lookup → GPT-4o-mini or Gemini Flash General Chat → GPT-4o-mini or Claude Haiku Complex Analysis → GPT-4o or Claude Sonnet Code Generation → Claude Sonnet or DeepSeek

Summarization → Gemini Flash or DeepSeek

Potential Savings: 30-50%

Strategy 2: Prompt Optimization

Shorter prompts = fewer tokens = lower costs.

Before (wasteful)

System: You are a helpful assistant. Your job is to help users with their questions. Please be friendly and professional. Make sure to provide accurate information. If you don't know something, please say so. Always be respectful and considerate in your responses. Try to be concise but thorough.

User: What's the weather like?

Token count: ~80 tokens

After (optimized)

System: Helpful assistant. Be concise and accurate.

User: What's the weather like?

Token count: ~15 tokens

Savings: 81% on system prompt!

Tips for Prompt Optimization

Remove filler words ("please", "make sure to", "try to")

Use bullet points instead of paragraphs

Move examples to few-shot format only when needed

Use shorter variable names in structured outputs

Potential Savings: 20-40%

Strategy 3: Response Caching

Many requests are identical or similar. Cache them!

Simple Cache Implementation

import crypto from 'crypto';

const cache = new Map<string, { response: string; timestamp: number }>();
const CACHE_TTL = 3600000; // 1 hour

function getCacheKey(model: string, messages: Message[]): string {
  const content = JSON.stringify({ model, messages });
  return crypto.createHash('md5').update(content).digest('hex');
}

async function cachedCompletion(
  model: string,
  messages: Message[]
): Promise<string> {
  const key = getCacheKey(model, messages);
  const cached = cache.get(key);

  if (cached && Date.now() - cached.timestamp < CACHE_TTL) {
    return cached.response; // Free!
  }

  const response = await client.chat.completions.create({
    model,
    messages,
  });

  const content = response.choices[0].message.content || '';
  cache.set(key, { response: content, timestamp: Date.now() });

  return content;
}

When to Cache

FAQ responses
Static content generation
Translation of common phrases
Code boilerplate generation

When NOT to Cache

Personalized responses
Time-sensitive information
Creative content (where variety matters)

Potential Savings: 20-60% (depends on hit rate)

Strategy 4: Batching Requests

Instead of making many small requests, batch them:

Before (10 requests)

const results = [];
for (const item of items) {
  const result = await complete(Summarize: ${item});
  results.push(result);
}
// 10 API calls, 10x overhead

After (1 request)

const prompt = items
  .map((item, i) => [${i}] ${item})
  .join('\n\n');

const result = await complete(

Summarize each item:
${prompt}

Return as JSON: [{"id": 0, "summary": "..."}, ...]
);
// 1 API call

Potential Savings: 30-50% (reduced overhead)

Strategy 5: Truncation & Summarization

Long contexts are expensive. Truncate or summarize when possible.

Smart Context Management

function manageContext(messages: Message[], maxTokens: number): Message[] {
  let totalTokens = 0;
  const result: Message[] = [];

  // Always keep system message
  const system = messages.find(m => m.role === 'system');
  if (system) {
    result.push(system);
    totalTokens += estimateTokens(system.content);
  }

  // Add messages from newest to oldest
  for (let i = messages.length - 1; i >= 0; i--) {
    const msg = messages[i];
    if (msg.role === 'system') continue;

    const tokens = estimateTokens(msg.content);
    if (totalTokens + tokens > maxTokens) break;

    result.unshift(msg);
    totalTokens += tokens;
  }

  return result;
}

Summarization for Long Conversations

async function summarizeHistory(messages: Message[]): Promise<string> {
  const history = messages
    .filter(m => m.role !== 'system')
    .map(m => ${m.role}: ${m.content})
    .join('\n');

  const summary = await complete(

Summarize this conversation in 2-3 sentences:
${history}
, 'gpt-4o-mini'); // Use cheap model for summarization

  return summary;
}

Potential Savings: 40-70% (on long conversations)

Strategy 6: Set Token Limits

Always set max_tokens to prevent runaway costs:

const response = await client.chat.completions.create({
  model: 'gpt-4o',
  messages: [...],
  max_tokens: 500, // Prevent 10,000 token responses
});

Recommended Limits by Use Case

Use Case

max_tokens

Yes/No questions	10
Short answers	100
Explanations	300
Code generation	1000
Long-form content	2000

Potential Savings: 10-30%

Strategy 7: Monitor and Alert

You can't optimize what you don't measure.

Set Up in COZHUB Dashboard

Daily cost alerts: Get notified if spend exceeds threshold

Per-model breakdown: Identify expensive models

Request volume tracking: Spot unusual spikes

Key Metrics to Track

Cost per request (by model)
Cache hit rate
Token efficiency (output/input ratio)
Error rate (failed requests = wasted money)

Real-World Example

Let's see these strategies in action:

Before Optimization:

100,000 requests/month
All using GPT-4o
Average 1,000 tokens per request
Monthly cost: $7,500

After Optimization:

50% → GPT-4o-mini (simple queries)
30% → GPT-4o (complex queries)
20% → Cached responses
30% prompt token reduction
Monthly cost: $2,100

Total Savings: 72%!

Summary

Strategy

Potential Savings

Effort

Model selection	30-50%	Low
Prompt optimization	20-40%	Medium
Caching	20-60%	Medium
Batching	30-50%	Medium
Context management	40-70%	High
Token limits	10-30%	Low

Next Steps

Audit your current usage in COZHUB dashboard

Identify your top 5 most expensive request types

Apply model selection optimization first (biggest impact, lowest effort)

Implement caching for repeated queries

Set up monitoring and alerts

Need help optimizing? Contact our team for a free cost analysis.

Happy saving! 💰

Cost Optimization Strategies for AI Applications