Benchmark Scaling | termina

Get scaling models for a specific category.

Scaling models show how metrics change as companies grow (e.g., how growth rates change with revenue scale). Each model contains raw data points, predicted trends, and confidence bands.

Parameters:

category: The business category (e.g., “saas”, “ai”, “consumer”)
model: Optional list of specific models to return (e.g., [“12m_growth_vs_scale”])

Get scaling models for a specific category. Scaling models show how metrics change as companies grow (e.g., how growth rates change with revenue scale). Each model contains raw data points, predicted trends, and confidence bands. **Parameters:** - `category`: The business category (e.g., "saas", "ai", "consumer") - `model`: Optional list of specific models to return (e.g., ["12m_growth_vs_scale"])

Authentication

AuthorizationBearer

Bearer authentication of the form Bearer <token>, where token is your auth token.

Path parameters

categorystringRequired

group_idintegerRequired

Query parameters

modellist of strings or nullOptional

Response

Successful Response

raw_datalist of maps from strings to doubles

Raw data points used to generate the scaling model. Each dictionary contains exactly two key-value pairs representing the x-metric (typically a scale metric like revenue) and y-metric (the metric being modeled). The keys are dynamic metric names that vary by model - for example, ‘12m_growth_vs_scale’ uses ‘rolling_3m_annualized_revenue’ (x) and ‘cmgr12’ (y). Use the /data/benchmark/metadata/models endpoint to see which columns each model uses.

Raw data points used to generate the scaling model. Each dictionary contains exactly two key-value pairs representing the x-metric (typically a scale metric like revenue) and y-metric (the metric being modeled). The keys are dynamic metric names that vary by model - for example, '12m_growth_vs_scale' uses 'rolling_3m_annualized_revenue' (x) and 'cmgr12' (y). Use the /data/benchmark/metadata/models endpoint to see which columns each model uses.

predlist of maps from strings to doubles

Predicted center trend line of the scaling model. Each dictionary contains the same two metric keys as raw_data, representing the model’s predicted y-value at each x-value along the trend line.

bandslist of objects

Bands representing the range of the scaling model.

1	from termina import Termina
2
3	client = Termina(
4	token="YOUR_TOKEN",
5	)
6	client.benchmark.get_scaling(
7	group_id=1,
8	category="category",
9	)

Authentication

Path parameters

Query parameters

Response

Errors