Send the request [chapter] deterministic

response = requests.post(ENDPOINT, json=payload) print(response.json())

`

This snippet demonstrates how a simple HTTP call can trigger a sophisticated inference workflow managed by capacity.so. The platform abstracts away the underlying infrastructure, allowing developers to focus on the logic of their workflows.

Integrating Capacity.so with Workflow Engines

Most workflow engines (e.g., Airflow, Prefect, Temporal) support custom plugins or hooks. By integrating capacity.so as a backend service, teams can dynamically allocate resources based on workflow stages.

For instance, a typical document-driven development pipeline might involve:

1. Parsing a markdown file into a structured representation. 2. Running a series of LLM-based transformations (e.g., summarization, extraction). 3. Writing the output to a database or repository.

At each stage, capacity.so can be queried to determine the optimal compute allocation:

```python