Cost Optimization: Getting the Most from Your AI Credits

Running AI at scale can quickly become expensive. Here's how our highest-volume customers keep costs under control while maintaining quality.

Understanding Your Costs

First, let's break down where your credits go:

// Typical credit usage by model type
const creditCosts = {
  'flux-schnell': 1,      // Fast image generation
  'flux-dev': 2,          // Higher quality images
  'flux-pro': 5,          // Premium quality
  'minimax-video': 15,    // Video generation
  'stable-audio': 3,      // Audio generation
};

Strategy 1: Smart Model Selection

Don't use a sledgehammer for a thumbtack:

// Choose model based on use case
function selectModel(useCase) {
  switch (useCase) {
    case 'thumbnail':
    case 'preview':
      return 'flux-schnell';  // Fast and cheap
    
    case 'product-image':
    case 'marketing':
      return 'flux-dev';      // Balance of quality/cost
    
    case 'hero-image':
    case 'print':
      return 'flux-pro';      // Worth the premium
    
    default:
      return 'flux-schnell';
  }
}

Savings Potential: 30-50%

Most applications don't need the highest quality model for every request. Use premium models selectively.

Strategy 2: Intelligent Caching

Cache aggressively to avoid regenerating identical content:

import { createHash } from 'crypto';

const cache = new Map();

async function generateWithCache(prompt, options) {
  // Create cache key from prompt + options
  const cacheKey = createHash('sha256')
    .update(JSON.stringify({ prompt, options }))
    .digest('hex');
  
  // Check cache first
  if (cache.has(cacheKey)) {
    console.log('Cache hit! Saved 1 credit');
    return cache.get(cacheKey);
  }
  
  // Generate and cache
  const result = await abstrakt.run('flux-schnell', { prompt, ...options });
  cache.set(cacheKey, result);
  
  return result;
}

Advanced: Semantic Caching

For similar (not identical) prompts:

// Use embeddings to find semantically similar cached results
async function semanticCache(prompt) {
  const embedding = await getEmbedding(prompt);
  
  const similar = await vectorDB.search({
    vector: embedding,
    threshold: 0.95,  // High similarity required
    limit: 1
  });
  
  if (similar.length > 0) {
    return similar[0].result;
  }
  
  return null;
}

Savings Potential: 20-40%

Depending on how repetitive your use case is, caching can dramatically reduce costs.

Strategy 3: Request Batching

Batch multiple generations to reduce overhead:

// Instead of individual requests
const results = [];
for (const prompt of prompts) {
  results.push(await abstrakt.run('flux-schnell', { prompt }));
}

// Use batch API for better efficiency
const results = await abstrakt.batch('flux-schnell', 
  prompts.map(prompt => ({ prompt }))
);

Savings Potential: 10-15%

Batching reduces network overhead and may qualify for volume discounts.

Strategy 4: Progressive Enhancement

Generate low-quality first, high-quality on demand:

// Generate thumbnail first
const thumbnail = await abstrakt.run('flux-schnell', {
  prompt,
  image_size: { width: 512, height: 512 }
});

// Only generate full resolution if user requests
async function getFullResolution(thumbnailId) {
  return abstrakt.run('flux-dev', {
    prompt,
    image_size: { width: 1024, height: 1024 }
  });
}

Savings Potential: 40-60%

Most thumbnails are never clicked. Why generate full resolution for everything?

Strategy 5: Usage Limits and Quotas

Protect yourself from runaway costs:

class UsageManager {
  constructor(dailyLimit, userLimit) {
    this.dailyLimit = dailyLimit;
    this.userLimit = userLimit;
  }
  
  async checkAndIncrement(userId) {
    const dailyUsage = await this.getDailyUsage();
    const userUsage = await this.getUserUsage(userId);
    
    if (dailyUsage >= this.dailyLimit) {
      throw new Error('Daily limit reached');
    }
    
    if (userUsage >= this.userLimit) {
      throw new Error('User limit reached');
    }
    
    await this.incrementUsage(userId);
    return true;
  }
}

Strategy 6: Off-Peak Generation

Schedule non-urgent generation during off-peak hours:

// Queue batch jobs for off-peak processing
async function scheduleGeneration(prompts, priority = 'normal') {
  if (priority === 'low') {
    // Queue for off-peak processing (better rates)
    return abstrakt.queue.add({
      model: 'flux-schnell',
      prompts,
      schedule: 'off-peak'  // 2am-6am local time
    });
  }
  
  // Process immediately
  return abstrakt.batch('flux-schnell', prompts);
}

Real-World Case Study

Before optimization:

After optimization:

Savings: 47%

Monitoring Your Costs

Use our dashboard to track spending:

// Get usage analytics
const usage = await abstrakt.usage.get({
  startDate: '2026-01-01',
  endDate: '2026-01-31',
  groupBy: 'model'
});

console.log(usage);
// {
//   'flux-schnell': { requests: 50000, credits: 50000 },
//   'flux-dev': { requests: 15000, credits: 30000 },
//   ...
// }

Conclusion

Cost optimization is about being intentional with your AI usage. Start with these strategies:

1. Audit your current usage patterns

2. Implement caching first (highest ROI)

3. Review model selection for each use case

4. Monitor continuously and adjust

Questions? Our team can help analyze your usage at billing@abstrakt.one.