MusicAPI.ai

The royalty-free music industry was ripe for disruption: Industry Pain Points: - Licensing complexity and legal risks - Limited variety in affordable music libraries - High costs for custom music ($500-5000 per track) - Time-consuming search for the right track - Storage and bandwidth costs for large music libraries Developer Needs: - Dynamic music generation for games - Mood-based music for different scenes - Consistent style across projects - Simple integration without audio expertise - Predictable, usage-based pricing

We built a developer-first API that generates music in real-time: Technical Architecture: - FastAPI for high-performance API endpoints - MusicGen model for music generation - Custom fine-tuned models for specific genres - Redis for caching and rate limiting - PostgreSQL for user data and analytics - AWS Lambda for serverless scaling - CloudFront for global audio delivery

AI Music Generation Pipeline We implemented a sophisticated music generation system using Meta's MusicGen:

# Music generation with style control
class MusicGenerator:
    def generate_track(self, params):
        # Load pre-trained model
        model = MusicGen.get_pretrained('facebook/musicgen-medium')
        
        # Apply style conditioning
        conditioning = self.build_conditioning(
            genre=params['genre'],
            mood=params['mood'],
            tempo=params['tempo'],
            instruments=params['instruments']
        )
        
        # Generate audio
        audio = model.generate_with_chroma(
            descriptions=[params['description']],
            melody_wavs=params.get('melody_reference'),
            duration=params['duration'],
            conditioning=conditioning
        )
        
        # Post-process and master
        return self.master_audio(audio)

API Architecture: - FastAPI for high-performance async handling - Redis queue for job management - S3 for audio storage with CloudFront CDN - WebSocket support for real-time generation updates Performance Optimizations: - Model quantization reduced memory usage by 40% - Batch processing for multiple requests - GPU pooling for cost optimization - Intelligent caching of similar requests

1. Initial Scaling Crisis (Day 45) The Problem: - Viral TikTok video led to 10x traffic spike - Response times degraded from 2s to 45s - Memory errors on Lambda functions - $2,000 unexpected AWS bill The Solution:

# Before: Naive implementation
def generate_music(prompt):
    model = load_model()  # Loading 2GB model every request!
    audio = model.generate(prompt)
    return audio

# After: Optimized implementation
# Model loaded once and cached in Lambda container
model = None

def get_model():
    global model
    if model is None:
        model = load_model()
    return model

def generate_music(prompt):
    # Check cache first
    cache_key = hashlib.md5(prompt.encode()).hexdigest()
    cached = redis_client.get(cache_key)
    if cached:
        return cached
    
    # Use connection pooling
    model = get_model()
    
    # Batch similar requests
    audio = batch_processor.process(model, prompt)
    
    # Cache for 24 hours
    redis_client.setex(cache_key, 86400, audio)
    return audio

Results: - Response time: 45s → 1.8s - AWS costs: -70% through caching - Capacity: 50 req/min → 5000 req/min 2. Quality vs Speed Tradeoff Challenge: Users wanted both high quality and fast generation Solution: Tiered Generation System - Instant Tier (<1s): Pre-generated variations - Fast Tier (<5s): Simplified model, cached stems - Quality Tier (<30s): Full model, custom generation Implementation:

class MusicGenerator:
    def generate(self, prompt, tier='fast'):
        if tier == 'instant':
            return self.get_cached_variation(prompt)
        elif tier == 'fast':
            return self.fast_generate(prompt)
        else:
            return self.quality_generate(prompt)
    
    def fast_generate(self, prompt):
        # Use quantized model (50% smaller)
        # Generate at lower sample rate
        # Upsample for final output
        pass
    
    def quality_generate(self, prompt):
        # Full model with all parameters
        # Multiple generation passes
        # Advanced post-processing
        pass

3. Cost Optimization Journey Month 1 Costs: - AWS Lambda: $800 - GPU instances: $1,200 - Bandwidth: $400 - Total: $2,400 - Revenue: $3,000 - Margin: 20% Optimization Strategies: 1. Spot Instances: 70% cost reduction for batch jobs 2. Caching Strategy: 60% of requests served from cache 3. Model Quantization: 50% reduction in memory usage 4. Geographic Distribution: Reduced bandwidth costs by 40% 5. Reserved Capacity: 35% discount on baseline compute Month 3 Costs: - Infrastructure: $1,800 - Revenue: $18,000 - Margin: 90% 4. Database Scaling Challenge Problem: Analytics queries slowing down production database Solution: Read Replica + Time-Series DB

-- Moved analytics to TimescaleDB
CREATE TABLE api_metrics (
    time TIMESTAMPTZ NOT NULL,
    user_id UUID,
    endpoint TEXT,
    duration_ms INT,
    tokens_used INT
);

SELECT create_hypertable('api_metrics', 'time');

-- Automated rollups for fast querying
CREATE MATERIALIZED VIEW daily_usage AS
SELECT 
    time_bucket('1 day', time) AS day,
    user_id,
    COUNT(*) as requests,
    AVG(duration_ms) as avg_duration,
    SUM(tokens_used) as total_tokens
FROM api_metrics
GROUP BY day, user_id;

5. Global Latency Optimization Challenge: 400ms+ latency for Asian users Solution: - Deployed edge functions in 5 regions - Implemented request routing based on geography - CDN for generated audio files - Result: <100ms latency globally 6. Handling Abuse & Rate Limiting Sophisticated Rate Limiting:

class RateLimiter:
    def __init__(self):
        self.limits = {
            'free': {'rpm': 10, 'daily': 100},
            'starter': {'rpm': 60, 'daily': 1000},
            'pro': {'rpm': 300, 'daily': 10000}
        }
    
    def check_rate_limit(self, user_id, tier):
        # Sliding window rate limiting
        # Distributed rate limiting with Redis
        # Burst allowance for good customers
        # Gradual backoff for repeated violations
        pass