Unpopular opinion: The price per 'agent unit' is a made-up metric to confuse buyers.

Having recently benchmarked several BI and analytics platforms for a client, I was struck by how pricing models have shifted from data volume or user seats to nebulous 'agent units' or 'compute units'. This appears to be a deliberate abstraction designed to obscure true cost drivers and complicate comparison.

In practice, an 'agent unit' often bundles several resource dimensions:
* A vague measure of data processed (rows, GB, API calls)
* Some allowance for concurrent queries or active models
* An implied slice of platform maintenance

This bundling makes it nearly impossible to forecast costs based on actual usage patterns. For example, a spike in dashboard usage or a scheduled data refresh could consume units unpredictably. Unlike cloud data warehouses where you can audit and optimize cost via SQL query history or slot consumption, these units are a black box.

Consider a simpler, more transparent model we implemented internally using dbt and a dedicated warehouse. Cost is directly tied to compute hours, which we can monitor and attribute.

```sql
-- Example: Cost attribution per model/dashboard (simplified)
SELECT
query_tag['dbt_model'] as model_name,
SUM(total_elapsed_time) / 1000 / 60 / 60 as compute_hours,
compute_hours * as estimated_cost
FROM snowflake.account_usage.query_history
WHERE query_tag IS NOT NULL
GROUP BY 1
ORDER BY 2 DESC;
```

The opacity of the 'agent unit' forces buyers into a position of trust without verification. It becomes difficult to answer basic questions: What is my marginal cost for adding one more dashboard? How does cost scale if my data volume doubles but my user count stays the same? This lack of clarity is a significant red flag in contracts, as it inherently limits data portability and cost-control strategies. Vendors should be compelled to define these units in terms of measurable, infrastructure-level components.