Trino vs Google BigQuery
Both are alternatives to Snowflake. Here's how they stack up — verified facts, no spin.
Also searched as Google BigQuery vs Trino — same comparison, one verdict.
Trino
Query everything where it already lives, without moving any of it.
Trino is a distributed SQL engine that queries data in place — S3, Postgres, MySQL, Kafka, Elasticsearch — and joins across all of them in a single statement. Apache-2.0, around 13k stars, built by the team behind Presto. The point is that it removes the loading step entirely: no ingestion pipeline, no second copy, no drift between the warehouse and the source. That is a genuinely different architecture from Snowflake rather than a cheaper version of it, and it suits organisations whose data is scattered and will stay scattered.
Google BigQuery
Serverless warehousing with no cluster to size — if you are already on GCP.
BigQuery is fully serverless: there is no warehouse to start, stop or size, you write SQL and Google works out the compute. On-demand pricing is roughly $6.25 per terabyte scanned, so a well-partitioned table can be very cheap and an unpartitioned `SELECT *` can be alarming. Included here because for a team already inside Google Cloud, the honest comparison is not open versus proprietary but which proprietary — and BigQuery's operational simplicity genuinely beats Snowflake's for infrequent workloads.
Side by side
| Trino | Google BigQuery | |
|---|---|---|
| Sovereignty Score | 91 | 32 |
| Open source | Yes | No |
| Self-hostable | Yes | No |
| Local-first | Yes | No |
| License | Apache-2.0 | Proprietary (hosted service) |
| Pricing | Free and open source. Managed options from Starburst and others. | About $6.25 per TB scanned on demand, or flat-rate capacity. Storage around $20/TB/month. Checked 2026-08-03. |
Trino edges it on the Sovereignty Score, but the right pick depends on the trade-offs below.
Trino
Strengths
- +Queries data where it lives — no ingestion, no second copy
- +Joins across completely different systems in one SQL statement
- +Apache-2.0 with a large connector ecosystem
- +Proven at very large scale
Trade-offs
- −A cluster to operate, with real memory tuning
- −Only as fast as the slowest underlying source
- −No storage of its own — it is an engine, not a warehouse
- −Java operations knowledge assumed
Google BigQuery
Strengths
- +Genuinely serverless — nothing to size, start or stop
- +Cheap for infrequent queries against well-partitioned data
- +Native integration with the Google Cloud and Workspace estate
- +Strong ML and geospatial support built in
Trade-offs
- −Proprietary, hosted, and deepens Google Cloud lock-in
- −Scan-based pricing punishes careless queries severely
- −Egress costs make leaving expensive
- −Practically requires you to be on GCP already
Related alternative guides
Facts verified 2026-08-03. Licenses and pricing change — spotted something out of date? That's a correction we want.