Best Practices for LLM Inference Optimization

Optimizing LLM inference is usually framed as a latency problem, but in production the real constraint is cost. Most providers bill by the token, which means every optimization, from prompt compression to model distillation, is ultimately an exercise in reducing token counts. The most effective way to cut costs is to change the pricing model itself. Oxlo.ai uses flat per-request pricing, so your bill scales with API calls, not with prompt length. The following practices show how to reduce inference overhead regardless of your provider, and where Oxlo.ai's architecture removes the trade-offs that token-based billing forces on you.

Right-Size Your Model

Using a 70B-parameter model for every task is like compiling a hello-world script on a GPU cluster. Start with a routing layer that matches task complexity to model capacity. Oxlo.ai hosts 45+ models, so you can route lightweight classification or summarization to efficient general-purpose models such as Qwen 3 32B, and reserve heavyweights like DeepSeek R1 671B MoE or GLM 5 for deep reasoning and long-horizon agentic tasks.

import openai
import os

client = openai.OpenAI(
    base_url="https://api.oxlo.ai/v1",
    api_key=os.environ["OXLO_API_KEY"]
)

def select_model(prompt: str, requires_reasoning: bool) -> str:
    if requires_reasoning:
        return "deepseek-r1-671b"
    elif "code" in prompt.lower():
        return "qwen-3-coder-30b"
    else:
        return "qwen-3-32b"

response = client.chat.completions.create(
    model=select_model("Refactor this function...", requires_reasoning=False),
    messages=[{"role": "user", "content": "Refactor this function to use asyncio."}],
    max_tokens=512
)

With token-based providers, a larger model also tends to produce longer outputs, which inflates costs on both input and output dimensions. Oxlo.ai's flat per-request pricing decouples model choice from cost, but routing correctly still improves latency and user experience.

Structure Prompts for Caching

Repeated system instructions, few-shot examples, and static context should be cached client-side or in a Redis store. Only send what changes, typically the user message. On token-based platforms, caching reduces token volume. On Oxlo.ai, it reduces the total number of requests, which directly lowers your bill because every API call is a discrete cost.

A simple pattern is to store the system prompt hash and reuse the conversation context without retransmitting the full setup. If your application uses multi-turn agents, keep the growing history server-side and append only the new user turn.

Batch Independent Operations

Whenever the API supports multiple inputs in a single call, batch them. This cuts HTTP overhead and reduces total request count. Oxlo.ai's embeddings endpoint, for example, accepts a list of strings in one request.

documents = [
    "Oxlo.ai offers flat per-request pricing for open-source LLMs.",
    "Batching embeddings reduces API overhead.",
    "Specialized models improve accuracy for domain tasks."
]

response = client.embeddings.create(
    model="bge-large",
    input=documents
)

On token-based providers, a large batch can spike input token counts and trigger rate-limit costs. Because Oxlo.ai charges per request, a batched embedding call costs the same as a single-text call, making batching a pure efficiency win.

Compress Context Windows

Long-context models like DeepSeek V4 Flash and Kimi K2.6 enable 1M-token and 131K-token windows, but feeding an entire