ClickHouse vs DuckDB
Both are alternatives to Snowflake. Here's how they stack up — verified facts, no spin.
Also searched as DuckDB 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.
DuckDB
The warehouse you did not need — analytics in a single file, on your laptop.
DuckDB is an in-process analytical database: no server, no cluster, no account. It queries Parquet and CSV directly, runs inside Python or R, and handles hundreds of gigabytes on a normal machine. MIT, around 40k stars. Its real contribution is the uncomfortable question it poses to this whole category — a very large number of Snowflake deployments hold data that DuckDB would answer instantly, for nothing, without anyone provisioning a warehouse. Ask how much data you actually have before you shop for infrastructure to hold it.
Side by side
| ClickHouse | DuckDB | |
|---|---|---|
| Sovereignty Score | 93 | 97 |
| Open source | Yes | Yes |
| Self-hostable | Yes | Yes |
| Local-first | Yes | Yes |
| License | Apache-2.0 | MIT |
| Pricing | Free and open source. ClickHouse Cloud is a paid managed option. | Free and open source. No server, so no hosting cost either. |
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
DuckDB
Strengths
- +No infrastructure at all — it is a library, not a service
- +Queries Parquet and CSV in place with no loading step
- +Handles hundreds of gigabytes on ordinary hardware
- +MIT, tiny, and embeds anywhere
Trade-offs
- −Single-machine — there is no cluster and no horizontal scale
- −Not built for many concurrent users
- −No built-in governance, access control or sharing
- −Wrong tool once you genuinely have warehouse-scale data
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Facts verified 2026-08-03. Licenses and pricing change — spotted something out of date? That's a correction we want.