ClickHouse and Snowflake are both columnar analytical databases built for OLAP workloads, and both can answer aggregation queries over billions of rows faster than a general-purpose relational database ever could. The similarities stop roughly there. They were built under entirely different constraints, deployed in different ways, priced differently, and optimized for workloads that only partially overlap. Picking the wrong one has real consequences - not just in query latency, but in operational burden and monthly spend.
Architecture: Shared-Nothing vs Separated Storage and Compute
ClickHouse started as an open-source, shared-nothing columnar database developed at Yandex for their web analytics product. Each node in a ClickHouse cluster owns its own storage and compute, processes queries independently, and coordinates with peers only for distributed query execution. This tight coupling of storage and compute is what gives ClickHouse its raw speed on a single node: data locality is maximized, vectorized execution operates directly on local disk blocks, and there is no cross-network I/O on the hot path. The tradeoff is that scaling requires adding nodes and rebalancing shards. ClickHouse Cloud has moved to a decoupled model using object storage (S3-compatible backends) for persistence, bringing it closer in spirit to Snowflake's architecture, but self-hosted ClickHouse still runs in the classic shared-nothing model.
Snowflake's architecture separates storage and compute entirely. Data lives in cloud object storage (S3, Azure Blob, or GCS depending on your cloud provider), and compute is handled by independent clusters called virtual warehouses. Each virtual warehouse is an MPP cluster that can be started, paused, and resized independently, and multiple warehouses can query the same data simultaneously without contention. This model makes Snowflake genuinely elastic: you pay for compute only when warehouses are running, and you can spin up a separate warehouse for a large ad hoc query without affecting your production reporting workload. The catch is latency. Every query must read data over the network from object storage unless it hits Snowflake's local SSD cache. Cold queries on large datasets that miss cache will be slower than equivalent queries in a ClickHouse cluster where data is on local NVMe.
Snowflake is proprietary and cloud-only. You do not run it on-premises, you do not get access to the source code, and your deployment options are limited to AWS, Azure, and GCP in Snowflake's own account. ClickHouse is Apache 2.0 licensed and can run anywhere: bare metal, Kubernetes, your own cloud account, or through ClickHouse Cloud. That distinction matters for organizations with data residency requirements, existing infrastructure investments, or a preference for not being locked into a single vendor's managed service pricing.
Query Performance: Raw Speed vs Consistency
On aggregation-heavy, single-table or denormalized flat-table workloads, ClickHouse is measurably faster than Snowflake. ClickHouse's own published benchmarks on ClickBench show it processing hundreds of millions to billions of rows in under a second on commodity hardware configurations where Snowflake takes several seconds. These are not synthetic micro-benchmarks constructed to flatter ClickHouse - the workload is a real analytical schema from Yandex.Metrica, representative of event log queries that product analytics and observability systems run constantly.
The picture changes for complex normalized schemas with multi-table joins. TPC-H benchmarks, which test normalized data warehouse query patterns with correlated subqueries, multi-level joins, and aggregations over multiple tables, consistently show ClickHouse performing worse relative to its single-table strength. ClickHouse's query optimizer does not push predicates across subquery boundaries (CTEs and subqueries act as optimization fences), which means complex query plans cannot be rewritten the way Snowflake's optimizer can restructure them. Snowflake handles star-schema and snowflake-schema queries - the kind that dominate traditional data warehouse workloads - more consistently across varying query shapes.
For interactive, low-latency queries on high-cardinality event data - logging 50 million events per day and querying them with sub-second response times - ClickHouse is the right tool. Snowflake's 60-second minimum billing per warehouse start and its inherent cold-query latency make it poorly suited to serving queries that must complete in under 200ms at high concurrency.
Data Ingestion
ClickHouse can ingest millions of rows per second using batch inserts. The ReplicatedMergeTree engine (and its variants like SummingMergeTree, AggregatingMergeTree) handles high-throughput writes by accepting small batches and merging parts asynchronously in the background. Production systems routinely write through Kafka using the Kafka table engine, which polls and inserts continuously. The ingestion model is pull-based and schema-on-write: you define a table with explicit column types and a sort key, and data lands directly into the table without a separate loading step.
Snowflake ingestion is more ETL-oriented. The standard path for bulk loading is COPY INTO from staged files in S3/GCS/Azure Blob, where Snowflake reads Parquet, CSV, or JSON from a stage object. Continuous ingestion is possible through Snowpipe, which automates file-triggered loading via SQS notifications or REST API calls. For true streaming, Snowflake released Snowpipe Streaming (GA July 2023), which uses a Java or Python SDK to write rows directly without the staging step. It works, but it is not designed for the millions-of-rows-per-second throughput that ClickHouse handles natively.
Cost Model: Credits vs Compute Hours
Snowflake bills in credits. One credit equals one hour of compute for an X-Small warehouse (a single-node cluster). Warehouses scale in powers of two: a Small costs 2 credits/hour, a Medium 4, a Large 8, an X-Large 16, a 2X-Large 32, and so on. On the Standard edition, credits cost roughly $2 each on-demand; Enterprise raises that to around $3. A Medium warehouse running 8 hours a day costs approximately $64/day at Standard pricing (4 credits/hour × 8 hours × ~$2/credit). The 60-second minimum per warehouse start means short, frequent queries on an auto-suspended warehouse incur startup overhead both in latency and cost - you pay for a full minute even if the query runs in five seconds.
ClickHouse Cloud also bills per-second for compute, but the effective rates are lower for equivalent analytical throughput, particularly on single-table OLAP workloads where ClickHouse needs less compute to answer the same question faster. Self-hosted ClickHouse eliminates the managed service margin entirely: you pay for the underlying infrastructure (EC2, GKE nodes, bare metal) and manage the cluster yourself. For teams with the operational capacity to run it, self-hosted ClickHouse on three or four high-memory nodes with NVMe storage is often dramatically cheaper than Snowflake for the same query volume. The operational cost is real - schema migrations, shard rebalancing, replication topology, and upgrade management require engineering time that Snowflake absorbs on your behalf.
Storage costs favor Snowflake for raw compressed bytes: Snowflake charges roughly $23/TB/month on a capacity commitment or approximately $40/TB/month on-demand in US regions (pricing varies by cloud region and contract type), and its compression ratios on columnar data are strong. Self-hosted ClickHouse storage costs are just your infrastructure costs, and ClickHouse's compression can be aggressive - LZ4 and ZSTD are available as codec options per column, and domain-specific codecs like Delta for timestamps or Gorilla for floating-point metrics can reduce storage by another 20-40% on top of general-purpose compression.
SQL Compatibility and Ecosystem
Snowflake speaks standard SQL closely enough that most queries written for traditional data warehouses port with minor modifications. It supports window functions, CTEs, lateral joins, semi-structured data via the VARIANT type, and a FLATTEN function for unnesting nested JSON. BI tools like Tableau, Looker, and dbt work against Snowflake with minimal friction. Snowflake's role-based access control, row-level security policies, and column masking policies make it straightforward to build governed, multi-team data warehouses where different groups see different slices of the data.
ClickHouse has its own SQL dialect that diverges from standard SQL in places. It does not enforce NULL handling the same way - functions return type-specific defaults rather than propagating NULL in many cases. Joins require explicit type specification. In older versions, users had to manually place the larger table on the left; since ClickHouse 24.12 the query planner automatically reorders two-table joins for optimal memory usage. On the other hand, ClickHouse exposes functions with no equivalent elsewhere: runningAccumulate, arrayJoin, quantileTDigest, groupBitmap, and dozens of domain-specific aggregations built for analytics on high-cardinality event data. dbt supports ClickHouse via an official adapter maintained by ClickHouse, Inc. (not available in dbt Cloud, but functional for dbt Core), though the ecosystem depth is thinner than Snowflake's and some dbt macros need adjustment for ClickHouse's SQL dialect.
When Each Makes Sense
Choose ClickHouse when your workload is dominated by high-throughput writes of event or log data and queries that aggregate over large time ranges on a small number of tables. Observability platforms, product analytics, and real-time user-facing dashboards that must return results in under a second are ClickHouse's natural domain. Companies like Cloudflare, Uber, and monday.com run ClickHouse at scale precisely because it sustains millions of rows per second of ingestion while serving sub-second aggregation queries concurrently. If you are outgrowing PostgreSQL on an analytics-heavy workload and the data model is flat or can be denormalized, ClickHouse will likely give you the best performance per dollar.
Choose Snowflake when the workload is a governed, multi-team data warehouse where different analysts run ad hoc queries against normalized schemas, where BI tool integration and data sharing between accounts matter, and where the engineering team should not be managing database infrastructure. Snowflake's time travel, cross-account data sharing, automatic query result caching, and multi-warehouse isolation make it a strong platform for organizations that want analytics infrastructure that largely runs itself. It is also the safer choice for complex ETL pipelines with nested subqueries and multi-table joins where Snowflake's optimizer handles query rewrites that ClickHouse's does not.
The two are not mutually exclusive. A pattern that works in practice: ClickHouse as a purpose-built query layer for high-throughput operational analytics (serving dashboards and APIs), with Snowflake as the governed data warehouse for cross-functional reporting and historical analysis. Whether that complexity is worth maintaining depends on team size and how distinct those two workloads actually are.
