Text summarization is one of the most common production workloads for large language models. Whether you are distilling news articles, legal contracts, or customer support transcripts, the pattern is straightforward: send a long document to an LLM and ask for a concise version. Because most teams already integrate via the OpenAI SDK, switching to a compatible provider requires zero client-side refactoring. Oxlo.ai offers a fully OpenAI SDK-compatible API with flat per-request pricing, which makes it a natural fit for summarization pipelines where input length varies.
Why Summarization Matters
Summarization reduces cognitive load and cuts downstream processing costs. Support teams use it to triage tickets. Finance teams use it to digest earnings reports. Developers use it to summarize logs and documentation. The value is clear, but implementation details determine whether the pipeline is reliable and economical at scale.
The OpenAI SDK Pattern
The standard approach uses a system prompt to define the summarization style and a user prompt that contains the source text. You control output length with max_tokens and can enforce structure with JSON mode if you need machine-readable output. The SDK expects a base URL, an API key, and a model identifier. Because Oxlo.ai is fully OpenAI SDK compatible, you only need to change two lines of configuration to point your existing client at https://api.oxlo.ai/v1.
Basic Implementation
The following Python example sends a document to Oxlo.ai and returns a short paragraph summary. Note the base_url and api_key fields.
import os
from openai import OpenAI
client = OpenAI(
base_url="https://api.
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