Scaling LLM Councils: From Prototype to Production
How to scale multi-model AI systems from experimentation to enterprise production.
LLM scalingAI productioncouncil architecturemulti-model AI scale
Scaling Challenges
Moving from prototype to production requires addressing scale challenges.
Performance at Scale
Latency Management
- Implement caching layers
- Use regional deployment
- Optimize model selection
Cost Management
- Monitor token usage
- Implement quotas
- Use tiered configurations
Architecture Patterns
Microservices
Deploy council as a service:
- API gateway
- Council orchestration
- Model provider abstraction
Event-Driven
Handle async workloads:
- Queue-based processing
- Webhook notifications
- Batch operations
Monitoring
Track key metrics at scale:
- Query volume
- Latency distribution
- Error rates
- Cost per query