Oxlo.ai
The Oxlo Team released Oxlo.ai on June 25, 2026. It is a serverless inference API platform that comprehensively provides the latest large language models (LLMs). This service introduces an innovative pricing system that charges based on the number of API requests, instead of the traditional method of charging based on the amount of tokens used. Developers can easily integrate it by simply changing the base URL, leveraging its full compatibility with existing frameworks such as the OpenAI SDK, without requiring complex modifications.
Oxlo.ai, released by the Oxlo Team on June 25, 2026, is a serverless inference API platform that comprehensively provides the latest large language models (LLMs). This service introduces an innovative pricing system that charges based on the number of API requests, rather than the traditional method of charging based on the amount of tokens used. Developers can easily integrate over 35 of the latest frontier and open-source models, such as DeepSeek R1, Llama 3.3, and Qwen, into their applications by simply changing the base URL, without requiring complex modifications, and leveraging its full compatibility with existing frameworks like the OpenAI SDK. This allows them to quickly deploy powerful AI applications.
Traditional AI services have employed a token-based pricing system, where costs fluctuate based on the number of characters in the input data and output responses, making it extremely difficult to predict budgets when scaling system size. In particular, in RAG (Retrieval-Augmented Generation) architectures that input large research papers or prompt templates, or in environments that run multi-dimensional agent feedback loops, unexpected cost overruns are common. Oxlo.ai eliminates this uncertainty by introducing a request-based flat pricing system, similar to a buffet where users pay a fixed fee and can use the service as much as they want, rather than being charged based on the weight of each dish. Furthermore, it guarantees a zero-training policy, ensuring that no user data is used for additional training or fine-tuning of the AI model, thereby fully addressing the security concerns of companies and research institutions that are extremely sensitive to information leakage.
In the fields of biotechnology and bioinformatics research, Oxlo.ai functions as a core infrastructure for large-scale genomic literature analysis and patient case summary pipelines. For example, when extracting gene mutation information and drug response data from tens of thousands of PubMed abstracts related to cancer immunotherapy, the prompt size per request can reach tens of thousands of tokens, which could result in astronomical costs with a typical API. Researchers can build an automated agent pipeline that precisely analyzes text by specifying the endpoint of the OpenAI library in a Python environment to Oxlo.ai and feeding large inputs to the deepseek-r1 model, thereby completing large-scale information extraction while maintaining a fixed daily cost. This provides the optimal development environment for securing the best computational performance within a limited research budget while safely handling raw clinical data without the risk of data leakage.
💻 System Requirements
"N/A (Cloud Serverless Hosting)"
"N/A (minimum 100MB for installation of OpenAI SDK, etc.)"
⚡ Installation
4-1. Quick Start
pip install openai
4-2. Detailed Installation
# Set the Oxlo API key in the environment variable
export OXLO_API_KEY="your_oxlo_api_key_here"
Example of Using the Python SDK
from openai import OpenAI
# Initialize the Oxlo.ai OpenAI-compatible API client
client = OpenAI(
api_key="YOUR_OXLO_API_KEY",
base_url="https://api.oxlo.ai/v1"
)
# Executing large-scale queries using the DeepSeek R1 model
response = client.chat.completions.create(
model="deepseek-r1",
messages=[
{"role": "user", "content": "Explain the advantages of request-based pricing for large agent workflows."}
],
temperature=0.3,
max_tokens=4000
)
print(response.choices[0].message.content)
FAQ
What is Oxlo.ai?
Oxlo.ai, released by the Oxlo Team on June 25, 2026, is a serverless inference API platform that comprehensively provides the latest large language models (LLMs). This service introduces an innovative pricing system that charges based on the number of API requests, rather than the traditional method of charging based on the amount of tokens used. Developers can easily integrate over 35 of the latest frontier and open-source models, such as DeepSeek R1, Llama 3.3, and Qwen, into their applications by simply changing the base URL, without requiring complex modifications, and leveraging its full compatibility with existing frameworks like the OpenAI SDK. This allows them to quickly deploy powerful AI applications. Traditional AI services have employed a token-based pricing system, where costs fluctuate based on the number of characters in the input data and output responses, making it extremely difficult to predict budgets when scaling system size. In particular, in RAG (Retrieval-Augmented Generation) architectures that input large research papers or prompt templates, or in environments that run multi-dimensional agent feedback loops, unexpected cost overruns are common. Oxlo.ai eliminates this uncertainty by introducing a request-based flat pricing system, similar to a buffet where users pay a fixed fee and can use the service as much as they want, rather than being charged based on the weight of each dish. Furthermore, it guarantees a zero-training policy, ensuring that no user data is used for additional training or fine-tuning of the AI model, thereby fully addressing the security concerns of companies and research institutions that are extremely sensitive to information leakage. In the fields of biotechnology and bioinformatics research, Oxlo.ai functions as a core infrastructure for large-scale genomic literature analysis and patient case summary pipelines. For example, when extracting gene mutation information and drug response data from tens of thousands of PubMed abstracts related to cancer immunotherapy, the prompt size per request can reach tens of thousands of tokens, which could result in astronomical costs with a typical API. Researchers can build an automated agent pipeline that precisely analyzes text by specifying the endpoint of the OpenAI library in a Python environment to Oxlo.ai and feeding large inputs to the deepseek-r1 model, thereby completing large-scale information extraction while maintaining a fixed daily cost. This provides the optimal development environment for securing the best computational performance within a limited research budget while safely handling raw clinical data without the risk of data leakage.
When should I use Oxlo.ai?
The Oxlo Team released Oxlo.ai on June 25, 2026. It is a serverless inference API platform that comprehensively provides the latest large language models (LLMs). This service introduces an innovative pricing system that charges based on the number of API requests, instead of the traditional method of charging based on the amount of tokens used. Developers can easily integrate it by simply changing the base URL, leveraging its full compatibility with existing frameworks such as the OpenAI SDK, without requiring complex modifications.
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