Performance Highlights
Model Selection
<1ms
Instant routing decisions
Instant routing decisions
Throughput
10,000+
Requests per second
Requests per second
Cache Hit Rate
60-80%
Multi-tier efficiency
Multi-tier efficiency
Overhead
<3ms
Total added latency
Total added latency
Architecture Advantages
Lightning-Fast ML Pipeline
1
No LLMs in Routing
Pure ML classifiers make decisions instantly without large model inference
2
Pre-computed Embeddings
Classification happens without real-time embedding generation
3
Optimized Algorithms
Purpose-built for speed over complexity with zero unnecessary overhead
Multi-Tier Caching System
Caching Layers
Caching Layers
L1: Prompt-Response Cache
- Speed: Microsecond responses
- Use case: Identical requests
- Hit rate: 40-60%
- Speed: 1-2ms responses
- Use case: Similar meaning requests
- Hit rate: 20-30%
- Speed: 5-10ms responses
- Use case: Provider health and routing decisions
- Hit rate: Nearly 100%
Go-Powered Backend
Native Performance
Compiled binary with no runtime overhead or interpretation layers
Massive Concurrency
Handle thousands of simultaneous requests with goroutines
Memory Efficient
Minimal garbage collection impact and optimized memory usage
Fast Startup
Sub-second cold start times for instant scaling
Real-World Metrics
Latency Breakdown
Throughput Characteristics
Sustained Load
10,000+ req/s
Continuous high throughput
Continuous high throughput
Burst Capacity
50,000+ req/s
Short-term peak handling
Short-term peak handling
Linear Scaling
2x instances = 2x capacity
Predictable performance scaling
Predictable performance scaling
Performance Optimizations
Smart Request Processing
1
Parallel Classification
Request analysis happens concurrently with provider health checks
2
Pre-computed Routes
Common routing decisions are cached and reused across requests
3
Async Health Checks
Provider status updates happen in background without blocking requests
Efficient Data Handling
Zero-Copy Operations
Minimal memory allocation and copying during request processing
Connection Pooling
Persistent connections to all providers reduce connection overhead
Optimized JSON
Fast parsing and serialization with minimal allocations
Resource Management
Graceful degradation under extreme load conditions
Performance Comparison
Benchmarks run on identical hardware (4 CPU cores, 8GB RAM) with 1000 concurrent requests.
| Solution | Model Selection | Memory Usage | Cold Start | Throughput |
|---|---|---|---|---|
| Adaptive | <1ms | 50MB | <1s | 10,000+ req/s |
| Python-based | 50-200ms | 500MB+ | 10-30s | 500 req/s |
| LLM-based routing | 1-5s | 2GB+ | 60s+ | 10 req/s |
Cache Performance
Hit Rate Optimization
Different request patterns achieve different cache performance:Repeated Queries
90%+ hit rate
FAQ-style applications
FAQ-style applications
Similar Content
60-70% hit rate
Content generation tasks
Content generation tasks
Unique Requests
20-30% hit rate
Highly varied applications
Highly varied applications
Cache Warming Strategies
Adaptive automatically pre-loads cache with common patterns:
- Popular request types
- Frequently used prompts
- High-traffic user patterns
Monitoring and Observability
Built-in Metrics
Latency Percentiles
Track P50, P95, P99 response times across all endpoints
Cache Analytics
Monitor hit rates, cache efficiency, and performance gains
Provider Health
Real-time status and response time monitoring for all providers
Error Tracking
Detailed error rates, types, and recovery statistics
Dashboard Insights
Access real-time performance data in your Adaptive dashboard:- Request latency trends and percentiles
- Cache hit rates across all tiers
- Provider performance comparisons
- Cost savings from cache hits
- Throughput and scaling metrics
Performance Tip: Enable semantic caching for applications with similar but not identical requests to maximize cache efficiency.
Scaling Considerations
Horizontal Scaling
1
Load Balancing
Multiple Adaptive instances can be load-balanced for higher throughput
2
Cache Sharing
Distributed cache layers maintain efficiency across instances
3
Auto-scaling
Automatic instance scaling based on request volume and latency
Performance Best Practices
Enable All Caches
Use both semantic and prompt-response caching for maximum performance
Connection Reuse
Use persistent connections and connection pooling in your clients
Batch Requests
Group similar requests together when possible for better cache efficiency
Monitor Metrics
Watch performance dashboards to identify optimization opportunities