Model Routing
Model routing components provide intelligent routing and selection of language models based on various criteria and optimization strategies.
LLM Router
This component routes requests to the most appropriate LLM based on OpenRouter model specifications.
Usage
LLM Router capabilities:
Intelligent model selection
Performance optimization
Cost optimization
Quality optimization
Load balancing
Inputs
models
Language Models
List of LLMs to route between
input_value
Input
The input message to be routed
judge_llm
Judge LLM
LLM that will evaluate and select the most appropriate model
optimization
Optimization
Optimization preference (quality/speed/cost/balanced)
Outputs
output
Output
The response from the selected model
selected_model
Selected Model
Name of the chosen model
Routing Strategies
Performance-Based Routing
Speed Optimization: Route to fastest models
Latency Minimization: Minimize response time
Throughput Maximization: Maximize requests per second
Load Balancing: Distribute load evenly
Resource Utilization: Optimize resource usage
Quality-Based Routing
Accuracy Optimization: Route to most accurate models
Task-Specific Routing: Route based on task type
Domain Expertise: Route to domain-specific models
Output Quality: Optimize for output quality
Capability Matching: Match model capabilities to requirements
Cost-Based Routing
Cost Minimization: Route to cheapest models
Budget Management: Stay within budget constraints
Cost-Performance Ratio: Optimize cost-performance balance
Usage Tracking: Track and monitor costs
Quota Management: Manage API quotas
Balanced Routing
Multi-criteria Optimization: Balance multiple factors
Weighted Scoring: Apply weights to different criteria
Dynamic Adjustment: Adjust routing based on performance
Adaptive Learning: Learn from routing outcomes
Context-Aware: Consider request context
Advanced Features
Model Selection Criteria
Model Capabilities
Context Length: Maximum input context size
Token Limits: Input/output token limitations
Model Type: Chat, completion, or embedding models
Supported Features: Function calling, vision, etc.
Language Support: Supported languages
Performance Metrics
Response Time: Average response time
Accuracy: Model accuracy for specific tasks
Reliability: Model uptime and availability
Error Rate: Frequency of errors or failures
Consistency: Consistency of outputs
Cost Considerations
Token Pricing: Cost per input/output token
Request Pricing: Cost per request
Subscription Models: Monthly/annual pricing
Volume Discounts: Bulk usage discounts
Free Tier Limits: Free usage allowances
Intelligent Routing Logic
Rule-Based Routing
Static Rules: Predefined routing rules
Conditional Logic: If-then routing conditions
Priority Lists: Ordered model preferences
Fallback Chains: Backup model sequences
Exception Handling: Handle routing failures
Machine Learning Routing
Predictive Models: Predict best model for requests
Reinforcement Learning: Learn from routing outcomes
Feature Engineering: Extract request features
Model Training: Train routing models
Continuous Learning: Adapt to changing patterns
Heuristic Routing
Task Classification: Classify request types
Pattern Matching: Match request patterns
Historical Performance: Use historical data
User Preferences: Consider user preferences
Context Analysis: Analyze request context
Model Pool Management
Model Registration
Model Discovery: Automatically discover available models
Capability Detection: Detect model capabilities
Performance Profiling: Profile model performance
Cost Integration: Integrate pricing information
Health Monitoring: Monitor model health
Dynamic Model Management
Hot Swapping: Replace models without downtime
A/B Testing: Test different routing strategies
Canary Deployments: Gradually roll out new models
Circuit Breakers: Handle model failures
Graceful Degradation: Fall back to backup models
Model Optimization
Performance Tuning: Optimize model parameters
Caching Strategies: Cache model responses
Request Batching: Batch requests for efficiency
Connection Pooling: Pool model connections
Load Balancing: Balance load across model instances
Monitoring and Analytics
Performance Monitoring
Response Times: Track model response times
Success Rates: Monitor routing success rates
Error Analysis: Analyze routing errors
Cost Tracking: Track routing costs
Usage Patterns: Analyze usage patterns
Quality Assurance
Output Quality: Monitor output quality
User Satisfaction: Track user satisfaction
A/B Testing: Compare routing strategies
Performance Regression: Detect performance issues
Compliance Monitoring: Ensure regulatory compliance
Reporting and Dashboards
Real-time Dashboards: Live routing metrics
Historical Reports: Historical performance reports
Cost Reports: Detailed cost breakdowns
Usage Analytics: Usage pattern analysis
Performance Benchmarks: Compare model performance
Use Cases
Multi-Model Applications
Model Ensemble: Combine multiple models
Specialized Tasks: Route to task-specific models
Fallback Systems: Backup model routing
Cost Optimization: Minimize operational costs
Performance Optimization: Maximize performance
Enterprise Deployments
Department Routing: Route by department needs
User-Based Routing: Route by user preferences
Compliance Routing: Route for compliance requirements
Budget Management: Manage departmental budgets
SLA Management: Meet service level agreements
Research and Development
Model Comparison: Compare model performance
Experimental Routing: Test new routing strategies
Performance Analysis: Analyze model performance
Cost Analysis: Analyze cost implications
Innovation: Explore new routing approaches
Usage Notes
Flexibility: Support for various routing strategies
Scalability: Handle high-volume routing decisions
Reliability: Robust fallback and error handling
Observability: Comprehensive monitoring and analytics
Cost Control: Effective cost management and optimization
Performance: Low-latency routing decisions
Last updated