Optimizing LLM Inference for Production
Reduce latency by 60% and cut costs in half with these practical techniques for running LLMs in production environments.
Optimizing LLM Inference for Production
Running LLMs in production is expensive and slow — unless you know where to optimize. This article covers the techniques we used at LegionEdge to make inference fast and affordable at scale.
The Cost Problem
A single GPT-4-class model call costs roughly $0.03 for a typical request. At 1M requests per day, that's $30,000/month just for inference. Multiply by the number of features using AI, and costs spiral quickly.
Technique 1: Prompt Caching
Most applications send highly repetitive system prompts. Caching the KV state for these shared prefixes can cut latency by 40-60%.
const cachedPrefix = await model.cachePrefix(systemPrompt);
// Subsequent calls reuse the cached prefix
const response = await model.complete({
cachedPrefix,
userMessage: input,
});Technique 2: Model Routing
Not every request needs the most powerful model. Route simple tasks to smaller, faster models:
| Task | Model | Latency | Cost |
|---|---|---|---|
| Classification | Haiku | 200ms | $0.001 |
| Summarization | Sonnet | 800ms | $0.008 |
| Complex reasoning | Opus | 3s | $0.03 |
Technique 3: Streaming + Early Termination
Stream responses to the user and terminate early when the answer is sufficient. This improves perceived latency dramatically.
Technique 4: Batching
Group multiple independent requests into a single batch call. Most providers offer batch APIs with 50% cost reduction.
Results
After applying these techniques across the LegionEdge platform:
- P50 latency: 1.2s → 450ms
- P99 latency: 8s → 2.1s
- Monthly cost: Reduced by 55%
- User satisfaction: NPS increased by 12 points
Conclusion
LLM optimization isn't about finding one silver bullet. It's about stacking multiple small wins across the request lifecycle.