Google BigQuery

Google BigQuery is a serverless data platform positioned as an autonomous data to AI system, automating the data life cycle from ingestion through to AI-driven insight, with Gemini features now included in the standard pricing models.

Serverless is the architectural property everything else follows from, and it changed what a data warehouse costs to own. Conventional warehouses require capacity to be provisioned and paid for continuously, which means an organisation sizes for its heaviest query and pays for that around the clock. BigQuery separates storage from compute and charges for bytes processed, so an analytics workload that runs for two hours a day costs what two hours cost.

The corollary deserves stating just as plainly, because it is where teams get caught: a badly written query scanning an entire table costs real money every time somebody runs it, and a dashboard refreshing every minute against unpartitioned data can produce a genuinely alarming bill. Partitioning, clustering and reviewing query cost are operational disciplines rather than optimisations here.

The free monthly allowance is substantial enough to be useful rather than symbolic, at 10 GiB of stored data and up to 1 TiB of queries every month, which covers a small analytics workload indefinitely. Billing beyond that is usage-based through the Google Cloud pricing calculator rather than plan tiers. Note that the 300 dollar figure on Google Cloud pages is new-customer credit and the 350,000 dollar figure is a startup programme, neither being a BigQuery rate.