Snowflake → ClickHouse
What it saves, what actually moves, what you rebuild — and the thing that catches people.
What it costs, and what it saves
Snowflake bills per credit for compute plus storage, and idle warehouses that auto-resume produce bills nobody predicted. ClickHouse self-hosted is the machine — often an order of magnitude less for analytics-shaped workloads.
Moves cleanly
The data, via Parquet export to object storage and then ClickHouse's s3 table function.
You rebuild
SQL. Both speak SQL but the dialects differ meaningfully, and Snowflake-specific functions have no direct equivalents.
What Snowflake costs you today
Credits, consumed per second of compute. Standard is roughly $2 per credit and Enterprise around $3, with a credit buying one hour of the smallest warehouse; each size step doubles both capacity and burn rate. Storage is about $23 per terabyte per month. The failure mode is well known and expensive: an auto-resume warehouse left running, or a dashboard refreshing every minute, quietly burns credits all night. Figures checked 2026-08-03; heavily negotiated at volume.
What actually holds you in
Moderate, and lower than the migration industry suggests. The data is yours and unloads to Parquet or CSV without drama. SQL is broadly standard, so most queries port with modest edits. What genuinely holds you is the surrounding platform — Snowpark, Streams and Tasks, the data-sharing arrangements with partners, and any transformation logic written as Snowflake-specific stored procedures. Teams whose transformations live in dbt move comfortably; teams who wrote them in Snowflake's own primitives are rewriting.
What you are moving to
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.
Free and open source. ClickHouse Cloud is a paid managed option.
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
What you give up
- 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
The migration, step by step
- 1Export tables to Parquet in S3 or GCS with COPY INTO
- 2Design ClickHouse tables with the right MergeTree engine and ORDER BY — this decides performance more than hardware does
- 3Load from object storage with the s3 table function
- 4Port queries, starting with the dashboards people actually open
- 5Compare results row by row on a sample before trusting anything
The gotcha
ClickHouse's ORDER BY is not a sort — it is the primary index and it determines whether a query scans a million rows or a billion. Getting it wrong makes ClickHouse look slower than Snowflake, and the fix is a table redesign rather than a tuning flag.
When to stay on Snowflake
You need Snowflake's data sharing, or your team's SQL skills are the constraint — ClickHouse rewards expertise and punishes its absence.
More Layer 3 migrations
Figures verified 2026-08-03 against vendor pricing pages. Prices change and migrations differ by estate — treat the cost delta as a starting model, not a quote. Rankings and recommendations here are merit-only; affiliate income never changes a verdict. See our methodology.