ClickHouse vs Google BigQuery
Both are alternatives to Snowflake. Here's how they stack up — verified facts, no spin.
Also searched as Google BigQuery vs ClickHouse — same comparison, one verdict.
ClickHouse
TOP PICKAnalytical queries at a speed that reframes what you thought was slow.
ClickHouse is a columnar database built for analytical queries and it is startlingly fast — billions of rows scanned in under a second on modest hardware is routine rather than exceptional. Apache-2.0, around 49k stars, and used at scale by Cloudflare, Uber and many others. Compression is excellent, so storage costs fall as well as compute. It is the strongest technical answer on this page, and the cost is operational: schema and index design matter enormously here, and a badly designed table is slow in ways a warehouse would have hidden from you.
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
| ClickHouse | Google BigQuery | |
|---|---|---|
| Sovereignty Score | 93 | 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. ClickHouse Cloud is a paid managed option. | About $6.25 per TB scanned on demand, or flat-rate capacity. Storage around $20/TB/month. Checked 2026-08-03. |
ClickHouse is Macrostack's recommended Snowflake alternative, so it's our pick here.
ClickHouse
Strengths
- +Frequently outruns Snowflake on analytical workloads, at a fraction of the cost
- +Apache-2.0 with no enterprise carve-out
- +Excellent compression — storage bills drop noticeably
- +Proven at enormous scale by well-known operators
Trade-offs
- −Schema and index design matter far more than in a warehouse
- −Updates and deletes are awkward by design
- −Operating a cluster is real work
- −Weaker governance and lineage tooling than Snowflake
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.