How Synthwave Studios Reduced AI Costs by 60% with Smart Caching

When Synthwave Studios launched their AI-powered game asset generator, they didn't anticipate the costs. Within three months, their AI spend had ballooned to $15,000/month—and they were only at 10% of their target user base.

Here's how they used Abstrakt to cut costs by 60% while actually improving performance.

The Challenge

Synthwave Studios builds tools for indie game developers. Their flagship product, AssetForge, generates game sprites, backgrounds, and UI elements using AI.

Initial Architecture

User Request → Direct API Call → AI Provider → Store Result → Return to User

Every request hit the AI provider. No caching. No optimization.

The Numbers (Before)

The Solution

Working with our team, Synthwave implemented a three-layer optimization strategy.

Layer 1: Exact Match Caching

Many users generate similar assets. "Blue slime enemy pixel art" gets requested hundreds of times.

// Abstrakt's built-in caching
const result = await abstrakt.run('flux-schnell', {
  prompt: 'Blue slime enemy, pixel art style, 32x32',
  cache: {
    enabled: true,
    ttl: 86400 * 7 // 7 days
  }
});

Result: 35% of requests served from cache

Layer 2: Semantic Similarity Caching

"Blue slime monster pixel art" and "Blue slime enemy pixel art" should return similar results.

// Enable semantic caching for similar prompts
const result = await abstrakt.run('flux-schnell', {
  prompt: userPrompt,
  cache: {
    enabled: true,
    semantic: true,
    similarityThreshold: 0.92
  }
});

Result: Additional 15% cache hits

Layer 3: Smart Model Selection

Not every generation needs the highest quality model.

// Thumbnails use fast model
if (outputSize <= 256) {
  model = 'flux-schnell';  // 1 credit
} 
// Full resolution uses quality model
else if (isPremiumUser) {
  model = 'flux-pro';      // 5 credits
}
// Standard users get balanced model
else {
  model = 'flux-dev';      // 2 credits
}

Result: 25% cost reduction from model optimization

Implementation Timeline

The Results (After)

Code Walkthrough

Here's the actual implementation pattern Synthwave uses:

class AssetGenerator {
  constructor() {
    this.abstrakt = new Abstrakt({ apiKey: process.env.ABSTRAKT_KEY });
  }

  async generate(prompt, options) {
    // Normalize prompt for better cache hits
    const normalizedPrompt = this.normalizePrompt(prompt);
    
    // Select model based on requirements
    const model = this.selectModel(options);
    
    // Generate with caching
    const result = await this.abstrakt.run(model, {
      prompt: normalizedPrompt,
      image_size: options.size,
      cache: {
        enabled: true,
        semantic: true,
        similarityThreshold: 0.92,
        ttl: 86400 * 7
      }
    });
    
    // Track for analytics
    await this.trackGeneration(result, options);
    
    return result;
  }

  normalizePrompt(prompt) {
    return prompt
      .toLowerCase()
      .trim()
      .replace(/\s+/g, ' ')
      // Standardize common variations
      .replace(/monster|creature|enemy/g, 'enemy')
      .replace(/sprite|character|asset/g, 'sprite');
  }

  selectModel(options) {
    if (options.size <= 256) return 'flux-schnell';
    if (options.premium) return 'flux-pro';
    return 'flux-dev';
  }
}

Lessons Learned

1. Prompt Normalization is Crucial

Before normalization, these were all cache misses:

After normalization, they all hit the same cache entry.

2. Start with Exact Caching

Semantic caching is powerful but has edge cases. Start with exact matching, measure your cache hit rate, then add semantic if needed.

3. Monitor Everything

// Track cache performance
abstrakt.on('cache_hit', (event) => {
  analytics.track('cache_hit', {
    prompt: event.prompt,
    savings: event.creditsSaved
  });
});

4. Set Appropriate TTLs

What's Next for Synthwave

With costs under control, they're now:

1. Scaling to 2M generations/month with confidence

2. Adding video generation for animated sprites

3. Building a prompt library of popular assets

Your Turn

Want to achieve similar results? Here's how to start:

1. Audit your current usage — identify repetitive prompts

2. Enable caching — start with exact matching

3. Implement model selection — match quality to use case

4. Monitor and iterate — track cache hit rates weekly

Questions about implementing caching for your use case? Reach out at enterprise@abstrakt.one.