Sentiment Analysis with Oxlo and OpenAI SDK

#aiinfrastructure #oxlo #ai

Sentiment analysis remains one of the most common production workloads for large language models. Whether you are moderating user feedback, monitoring brand mentions, or scoring support tickets, the infrastructure underneath matters as much as the model itself. Oxlo.ai offers a developer-first inference platform with flat per-request pricing and full OpenAI SDK compatibility, so you can deploy sentiment classifiers at scale without rewriting client code or absorbing unpredictable token costs.

Why Sentiment Analysis Still Matters

Classification is often cheaper and faster than fine-tuning a custom model, especially when you need to cover multiple languages or domains. Modern instruction-tuned models can perform accurate zero-shot sentiment detection with nothing more than a clear system prompt. The real bottleneck is usually the inference backend: latency, uptime, and cost structure determine whether the pipeline is viable in production.

Configuring the OpenAI SDK for Oxlo.ai

Because Oxlo.ai is fully OpenAI API compatible, switching providers is a single line change to your base URL. There is no custom client to learn.

from openai import OpenAI
import os

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

This works with the official Python and Node.js SDKs, as well as standard HTTP clients. All standard endpoints, including chat completions and function calling, are available.

Building a Zero-Shot Classifier

A minimal sentiment pipeline needs only a system instruction and a user message. The example below uses Llama 3.3 70B, Oxlo.ai's general-purpose flagship, but you can substitute Qwen 3 32B, Kimi K2.6, or any other model in the catalog.

def classify_sentiment(text: str, model: str = "llama-3.3-70b") -> str:
    response = client.chat.completions.create(
        model=model,
        messages=[
            {
                "role": "system",
                "content": (
                    "Classify the sentiment of the user text. "
                    "Respond with exactly one word: Positive, Negative, or Neutral."
                )
            },
            {"role": "user", "content": text}
        ],
        temperature=0.0,
        max_tokens=10
    )
    return response.choices[0].message.content.strip()

# Example
label = classify_sentiment("The onboarding flow was smooth and intuitive.")
print(label)  # Positive

Guaranteeing Structure with JSON Mode

Freeform text is risky in production pipelines. Oxlo.ai supports JSON mode and function calling, so you can enforce a schema and parse results without regex guards.

import json

def classify_sentiment_structured(text: str) -> dict:
    response = client.chat.completions.create(
        model="qwen3-32b",
        messages=[
            {
                "role": "system",
                "content": (
                    "You are a sentiment analysis API. "
                    "Return a JSON object with keys: sentiment, confidence, reasoning."
                )
            },
            {"role": "user", "content": text}
        ],
        response_format={"type": "json_object"},
        temperature=0.0
    )
    return json.loads(response.choices[0].message.content)

result = classify_sentiment_structured(
    "Delivery was late, but the packaging was excellent."
)
print(json.dumps(result, indent=2))

Batch Processing and Long-Context Inputs

Real-world sentiment tasks rarely involve single sentences. You might process entire support threads, Reddit discussions, or multi-page reviews. With token-based providers, long inputs inflate costs linearly. Oxlo.ai uses flat per-request pricing, so analyzing a 10,000-token transcript costs the same as a one-liner. This makes Oxlo.ai particularly relevant for long-context and agentic workloads where conversation history or document context is required. Popular models like Llama 3.3 70B, Qwen 3 32B, and Kimi K2.6 are available with no cold starts.

Cost Predictability at Scale

Token-based billing creates variance. A spike in verbose customer feedback or a sudden need to prepend large system prompts can double your bill overnight. Oxlo.ai replaces token math with a flat cost per API request. For high-volume sentiment pipelines, especially those handling long documents, this request-based model can be significantly cheaper than token-based alternatives. You can prototype for free on the 60 requests per day tier, then scale through Pro or Premium plans as volume grows. See https://oxlo.ai/pricing for current plan details.

Conclusion

Sentiment analysis does not need to be expensive or complicated to deploy. By pointing the OpenAI SDK at Oxlo.ai, you keep your existing Python or Node.js code, gain access to 45+ open-source and proprietary models, and replace unpredictable token costs with flat per-request pricing. If you are running sentiment classifiers in production, Oxlo.ai is a relevant option worth evaluating.

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