Generative AI
Case Study: Generative AI
A generative AI startup building enterprise-grade content generation, code assistance, and creative tools powered by large language models.
BUSINESS NEED
The Challenge
Required specialized infrastructure for serving generative AI models with low latency, high throughput, and cost-effective scaling for enterprise customers.
CHALLENGES
Key Challenges
- Serving large generative models with acceptable latency for real-time use
- Managing model versions and deployments across enterprise customers
- Controlling inference costs while maintaining quality of service
- Handling bursty inference traffic patterns from enterprise users
SOLUTION
Our Approach
- Optimized inference serving with model quantization and batching
- Multi-tenant model serving architecture with customer-level isolation
- Dynamic scaling with GPU-aware autoscaling policies
- Model registry with blue-green deployment for zero-downtime updates
- Token-level usage metering for accurate customer billing
- Edge caching for common queries to reduce GPU compute costs
RESULTS
Business Benefits
- Model serving latency reduced to under 200ms for 90th percentile
- Inference costs reduced by 65% through optimization techniques
- Model deployment time reduced from hours to minutes
- Successfully serving 100M+ API calls per month across customers
- GPU utilization improved to 90%+ during business hours
“The optimized AI inference infrastructure enables us to serve enterprise customers with premium quality while maintaining sustainable economics.”