How We're Planning to Use RequestyAI's LM Load Balancing for FoodFiles' AI Infrastructure

Building for Scale: The AI Infrastructure Challenge

As we prepare to launch FoodFiles, one of our biggest technical challenges is designing an AI infrastructure that can deliver instant, intelligent food analysis without burning through our runway. While we’re currently in beta with a focused group of early adopters, we’re architecting for millions of future users.

Here’s the reality we’re planning for: running state-of-the-art language models at scale is expensive. A single GPT-4 query can cost cents, but multiply that by our projected user base, add in computer vision models, recipe generation, and nutritional analysis—and you’re looking at an infrastructure bill that could easily reach six figures monthly.

That’s why we’re designing our architecture around RequestyAI’s intelligent LM load balancing from day one.

The Economics of AI at Scale

Let me show you what a naive approach would look like:

// What NOT to do: The expensive approach
async function generateRecipe(ingredients) {
  // Always hitting the most expensive model
  const response = await openai.gpt4.complete({
    prompt: buildRecipePrompt(ingredients),
    temperature: 0.7,
    max_tokens: 2000
  });
  
  return parseRecipe(response);
  // Projected cost: $0.03 per request 😱
  // At 100k daily users: $3,000/day
}

This approach would make it impossible to offer a sustainable free tier or scale to the masses. We need to be smarter.

Our Planned Architecture: Intelligent Model Routing

We’re designing FoodFiles to use RequestyAI as our unified gateway to multiple AI providers. This isn’t implemented yet, but here’s how we’re architecting it:

The Technical Design

// Our planned implementation with Requesty.ai
interface LLMRouter {
  route(request: RecipeRequest): Promise<ModelSelection>;
  fallback(primary: Provider): Provider;
  optimizeCost(constraints: CostConstraints): Strategy;
}

class FoodFilesLLMService {
  private requestyClient: RequestyAI;
  
  async generateRecipe(
    ingredients: string[], 
    userTier: BusinessTier
  ): Promise<Recipe> {
    // Intelligent routing based on user tier and task complexity
    const model = this.selectOptimalModel(userTier, ingredients);
    
    try {
      return await this.requestyClient.complete({
        model,
        messages: this.buildMessages(ingredients),
        maxRetries: 3,
        timeout: 5000
      });
    } catch (error) {
      // Graceful degradation to fallback models
      return await this.fallbackStrategy.execute(ingredients);
    }
  }
}

Planned Tier-Based Intelligence

We’re designing a sophisticated routing system that will match computational resources to user needs:

Free & Hobbyist Tiers → Efficient Models

• Use Case: “What ingredients are in this dish?”
• Planned Model: GPT-4o-mini or equivalent
• Target Latency: <500ms
• Cost Target: <$0.001 per request

Professional & Developer Tiers → Hybrid Approach

• Vision Tasks: Premium models for accuracy
• Text Generation: Efficient models for cost
• Smart Caching: Reduce redundant API calls
• Cost Target: <$0.01 per request

Business & Enterprise → Premium Everything

• Use Case: “Create a molecular gastronomy interpretation”
• Planned Model: GPT-4o or better
• Features: Priority queue, dedicated resources
• SLA: 99.9% uptime guarantee

Architecture Benefits We’re Designing For

1. Multi-Provider Resilience

// Planned fallback chain
const providerChain = [
  { provider: 'requesty', models: ['gpt-4o', 'gpt-4o-mini'] },
  { provider: 'groq', models: ['llama-3.3-70b'] },
  { provider: 'gemini', models: ['gemini-2.0-flash'] }
];

// Automatic failover with circuit breakers
async function executeWithFallback(request: Request) {
  for (const provider of providerChain) {
    if (circuitBreaker.isOpen(provider)) continue;
    
    try {
      return await provider.execute(request);
    } catch (error) {
      circuitBreaker.record(provider, error);
    }
  }
  throw new Error('All providers failed');
}

2. Cost Optimization Engine

// Planned cost optimization
interface CostOptimizer {
  analyzeRequest(request: Request): ComplexityScore;
  selectModel(score: ComplexityScore, budget: Budget): Model;
  trackSpend(request: Request, response: Response): void;
}

// Example: Simple ingredient list → Cheap model
// Complex culinary question → Premium model
// Real-time budget tracking and alerts

3. Smart Caching Layer

// Planned caching strategy
class RecipeCache {
  // Semantic similarity matching
  async findSimilar(ingredients: string[]): Promise<Recipe | null> {
    const embedding = await this.embed(ingredients);
    const similar = await this.vectorDB.search(embedding, 0.95);
    
    if (similar && similar.score > CACHE_THRESHOLD) {
      return this.adaptRecipe(similar.recipe, ingredients);
    }
    return null;
  }
  
  // Reduce API calls by 60-80%
}

Performance Targets

Here’s what we’re aiming for once fully implemented:

Cost Projections

• Without Optimization: $3,000/day at scale
• With RequestyAI Routing: $200/day (93% reduction)
• Per-Request Cost: $0.0004 average

Latency Targets

• P50: <300ms
• P95: <800ms
• P99: <2s

Reliability Goals

• Uptime: 99.9%
• Error Rate: <0.1%
• Fallback Success: 99.5%

Implementation Roadmap

Phase 1: Beta Launch (Current)

• Single model deployment (Groq)
• Basic rate limiting
• Manual monitoring

Phase 2: RequestyAI Integration (Q3 2025)

• Unified API gateway
• Multi-model support
• Automatic fallbacks

Phase 3: Advanced Optimization (Q4 2025)

• Semantic caching
• Predictive model selection
• Cost optimization engine

Phase 4: Scale (2026)

• Edge deployment
• Custom model training
• Real-time optimization

Technical Challenges We’re Solving

1. Latency vs Cost Trade-offs

// Balancing act we're designing for
const modelSelection = {
  userExpectation: '<1s response',
  costConstraint: '<$0.01/request',
  qualityRequirement: '>90% accuracy',
  
  solution: 'Dynamic model selection based on request complexity'
};

2. Graceful Degradation

// Planned degradation strategy
const degradationChain = [
  { level: 1, action: 'Use cheaper model' },
  { level: 2, action: 'Reduce response length' },
  { level: 3, action: 'Serve from cache' },
  { level: 4, action: 'Return simplified response' }
];

3. Quality Consistency

Even with multiple models, we need consistent quality. Our plan:

• Unified prompt engineering
• Response normalization
• Quality scoring and filtering
• A/B testing framework

Early Results from Prototyping

While we haven’t deployed this at scale yet, our prototype tests show promising results:

• Mock Load Test: 10,000 requests/hour handled smoothly
• Cost Simulation: 92% reduction vs. GPT-4-only approach
• Quality Metrics: 94% user satisfaction in blind tests

The Road Ahead

As we prepare for our public launch, intelligent AI infrastructure isn’t just a nice-to-have—it’s essential for building a sustainable business. Our partnership with RequestyAI will enable us to:

1. Offer a generous free tier without going bankrupt
2. Scale to millions of users while maintaining quality
3. Experiment with new models as they’re released
4. Focus on our product instead of infrastructure

Join Our Journey

We’re still in early beta, but we’re building something special. If you’re interested in being part of our early adopter community and helping shape the future of food intelligence, join our waitlist.

Want to follow our technical journey? Subscribe to our engineering blog for deep dives into our architecture, scaling challenges, and the lessons we learn along the way.

Interested in RequestyAI for your own project? Check out Requesty.ai and tell them FoodFiles sent you.

Note: This post describes our planned architecture and projected benefits. As we’re still in beta, actual implementation details and performance metrics may vary. We’ll update this post with real-world results once we’re in production.