Running LLMs in Production
1 min read
Cost Management
Effective prompt engineering reduces costs while improving performance.
Strategies:
- Optimize prompt length—remove unnecessary context
- Use appropriate model sizes for tasks (don’t use GPT-4 for simple classification)
- Implement caching for repeated or similar queries
- Monitor and analyze usage patterns to identify waste
Agents can be expensive due to multiple LLM calls per task.
| Strategy | Impact |
|---|---|
| Smaller models for simple steps | Reduce cost per call |
| Caching | Avoid redundant calls |
| Step limits | Cap maximum cost |
| Batching | Reduce API overhead |
| Cost Driver | Optimization |
|---|---|
| Embedding API calls | Cache embeddings, batch requests |
| Vector database | Right-size instance, use filtering |
| LLM tokens | Chunk size optimization, caching |
Latency
Multi-step agents have inherent latency from sequential operations.
| Strategy | Impact |
|---|---|
| Parallelization | Run independent steps concurrently |
| Streaming | Show progress during execution |
| Caching | Skip redundant operations |
| Simpler models | Faster inference |
| Bottleneck | Solutions |
|---|---|
| Embedding latency | Batch processing, caching, smaller models |
| Vector search latency | Index optimization, approximate search, filtering |
| LLM generation | Streaming, caching common queries |
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