# What is metrics layer? Definition and Examples | Silicon

URL: https://meetsilicon.com/glossary/metrics-layer/

> A metrics layer is the part of a semantic layer that defines business metrics, like revenue or churn rate, once in code so they can be reused consistently.

The terms are often used interchangeably. Strictly, a metrics layer focuses on metric definitions (aggregations, filters, ratios), while a semantic layer also covers entities, dimensions, joins and access rules.
## What a metric definition contains
A metric definition names the measure (for example, the sum of net_amount), the table it comes from, any filters (exclude refunds, exclude test accounts), the time grain it can be reported at, and the dimensions it can be broken down by. Ratio metrics, such as conversion rate, reference two other metrics.
## Why teams add one
Without a metrics layer, each dashboard rebuilds the same calculation and small differences creep in, such as one chart that excludes refunds and one that does not. Leadership then sees two revenue numbers for the same month and has to ask which is right. Defining the metric once removes that question for every chart built on it.
## How Silicon handles it
In Silicon, metrics are defined in dbt YAML or Silicon YAML, reviewed in pull requests like any other code, and listed in the metrics catalog with their owner and description.
FAQ
## Metrics layer questions
### What is the difference between a metrics layer and a semantic layer? +
A metrics layer covers metric definitions. A semantic layer also covers entities, joins, dimensions and access rules. Most products sold today as one include parts of the other.
### Where should metric definitions live? +
Next to the data models they use, in version control, so a change to a model and the metrics that depend on it can be reviewed together.
## Related
- Semantic layer ->
- dbt ->
- Semantic layer vs metrics layer ->
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