Know what drives
your warehouse bill.

Separate the two bills

DataCharted’s plan covers the agreed sync service. Your connected Google Cloud project pays for its own BigQuery usage. Google describes the main BigQuery costs as compute for query processing and storage, with different compute pricing models. Rates and allowances vary; use Google’s current pricing for an estimate.

Look beyond the number of contacts

Data size matters, but so does how you use it. Repeated full refreshes, broad SQL queries, multiple dashboards, and long reporting windows can create very different workloads for agencies with similar location counts. Measure a representative pilot instead of assuming that one location has a fixed cloud cost.

• Sync cadence: choose the frequency your reports need. Allow each run to finish.
• Refresh mode: full refresh is necessary for some mutable datasets; avoid using it more often than the reporting requirement justifies.
• Dashboard queries: start with bounded date ranges and reporting models that include only useful fields.
• Stored history: agree how much history to keep if you add snapshots or derived tables.

Set controls before expanding

Review BigQuery job usage after the pilot. Use Google’s query-estimation and cost-control features where appropriate. Billing budget alerts are useful notifications; do not assume an alert automatically stops every charge. See Google’s cost-control guidance for supported limits and the tradeoffs of enforcing them.

Keep the estimate honest

We do not quote a universal “cost per GHL location” for Google Cloud. Your project’s pricing model, region, data volume, query patterns, and other workloads affect the bill. Guided setup can include a review of the pilot’s workload so you can make a more informed decision.

Next: plan your dashboard connection or review DataCharted’s subscription pricing.