Performance

Ultra-fast routing with multi-tier caching and optimized Go architecture

Adaptive delivers industry-leading performance through optimized Go architecture, intelligent caching, and purpose-built ML algorithms designed for speed.

Performance Highlights

Model Selection

<1ms
Instant routing decisions

Throughput

10,000+
Requests per second

Cache Hit Rate

60-90% Multi-tier efficiency

Overhead

<3ms
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

L1: Prompt-Response Cache - Microsecond responses for identical requests (40-60% hit rate)
L2: Semantic Cache - 1-2ms responses for similar meaning requests (20-30% hit rate)
L3: Router Cache - 5-10ms responses for provider health decisions (nearly 100% hit rate)

Go-Powered Backend

Native Performance

Compiled binary with no runtime overhead

Massive Concurrency

Handle thousands of simultaneous requests with goroutines

Memory Efficient

Minimal garbage collection impact

Fast Startup

Sub-second cold start times

Real-World Metrics

Latency Breakdown

┌─────────────────┬──────────────┐
│ Component       │ Time         │
├─────────────────┼──────────────┤
│ Model Selection │ <1ms         │
│ Cache Lookup    │ <1ms         │
│ Provider Route  │ <1ms         │
├─────────────────┼──────────────┤
│ Total Overhead  │ <3ms         │
└─────────────────┴──────────────┘

Throughput Characteristics

Sustained Load

10,000+ req/s
Continuous high throughput

Burst Capacity

50,000+ req/s
Short-term peak handling

Linear Scaling

2x instances = 2x capacity
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

Repeated Queries

90%+ hit rate
FAQ-style applications

Similar Content

60-70% hit rate
Content generation tasks

Unique Requests

20-30% hit rate
Highly varied applications

Cache Warming

Adaptive automatically pre-loads cache with common patterns. Manual warming:

const patterns = ["Hello, how are you?", "Explain this concept:", "Write a summary of:"];
for (const pattern of patterns) {
  await openai.chat.completions.create({
    model: "adaptive/auto",
    messages: [{ role: "user", content: pattern }]
  });
}

Monitoring and Observability

Track performance 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

Next Steps

Prompt Caching

Learn about intelligent content-aware caching