Optimizing large language model performance requires balancing latency, throughput, and cost across the entire inference stack. While much attention focuses on model weights and quantization, production workloads are equally sensitive to prompt design, API usage patterns, and pricing architecture. The most effective optimizations combine client-side efficiency with an inference backend that minimizes overhead and cold starts. Oxlo.ai provides an OpenAI-compatible platform with request-based pricing and a broad model catalog, making it a natural testbed for these techniques.
Prompt and Context Management
Long contexts increase latency and memory pressure regardless of provider. Start by compressing system prompts, removing redundant instructions, and using structured delimiters. For conversational agents, implement a sliding window or summarization layer to prevent unbounded context growth.
from openai import OpenAI
client = OpenAI(
base_url="https://api.oxlo.ai/v1",
api
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