Comparisons

ClickHouse vs Snowflake

ClickHouse is a high-performance, SQL-based analytics database built to power everything from traditional data warehouse workloads to real-time dashboards and user-facing applications. Snowflake, while effective for data warehousing, often struggles with latency-sensitive or highly concurrent workloads.

Whether you’re building real-time applications, running large-scale event analytics, or simply looking to accelerate existing warehouse workloads, ClickHouse provides superior performance, significantly lower cost, simpler pricing, and industry-leading efficiency.

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Why ClickHouse is better:

Reduction in costs

3-5x

Faster queries

38%

Better compression

Read our comprehensive guide about migrating from Snowflake to ClickHouse.

ClickHouse compared to Snowflake

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Join others migrating to ClickHouse from Snowflake

With Snowflake, we were using the standard plan, small compute, which cost nearly six times more than ClickHouse Cloud. We got several seconds query time and no materialized views. With ClickHouse Cloud’s production instance, we are getting sub-second query time along with materialized views. The decision to switch was a no-brainer for us.

Snowflake [was] too slow and costly for our needs. While it performs well for processing in-house data, it becomes quite expensive when handling real-time customer data within a product, which negatively impacts the product's unit economics.

Over time, those queries had become painfully slow in Snowflake and Postgres. Some took over a minute. Others timed out entirely...The payoff [of migrating to ClickHouse] came right away. Queries that once failed now ran in six seconds, with no caching required.

Tired of unpredictable costs, gated features, and pricing models that penalize interactivity?

You’re not alone. Many teams are rethinking their architecture.
Discover why they’re moving real-time and user-facing workloads to ClickHouse.

Snowflake is a proprietary cloud data warehouse built on a hybrid of shared-disk and shared-nothing designs. It stores data in object storage, accessed by stateless compute clusters (“warehouses”) that scale horizontally.

ClickHouse’s self-managed, open-source deployment uses a shared-nothing architecture, giving you full control over distribution and scaling. In contrast, ClickHouse Cloud uses a shared storage architecture with full separation of storage and compute, scaling elastically over a single copy of data in object storage.

Snowflake and ClickHouse take different approaches to scaling - one rigid, one dynamic. Snowflake relies on fixed-size “warehouses” that must be manually configured and resized. Handling concurrency requires multi-cluster warehouses, driving up costs.

ClickHouse Cloud, by contrast, scales elastically. Resources expand or contract automatically within user-defined limits - vertically for heavier queries and horizontally for high concurrency. The result is better performance under load, simpler operations, and more predictable pricing.

ClickHouse is engineered for speed. Its vectorized engine, efficient memory use, and columnar compression deliver sub-second queries at scale. Snowflake, while strong for long batch jobs, struggles in real-time or high-concurrency scenarios without costly compute

Public benchmarks, including a join-heavy workload built for Snowflake and Databricks, show ClickHouse not only matching but outperforming on speed and cost. Without tuning or schema changes, it executed multi-billion-row joins in a fraction of the time and cost.

ClickHouse is a high-performance analytics database used for real-time dashboards, product analytics, and observability - natively supported via ClickStack. With support for wide events, high compression, and fast aggregations, it delivers low-latency queries at scale and excels in high-concurrency scenarios, including agentic workflows.

Snowflake is built for batch analytics and BI. Strong for reporting, it lacks the cost efficiency, latency, and flexibility needed for real-time, agentic, or observability use cases.

ClickHouse, with fully managed CDC via ClickPipes, streams changes from databases like Postgres or MySQL for real-time analytics. With support for 70+ file formats, catalogs like Hive and Glue, open table formats such as Iceberg, and external engines to query sources like Postgres, MongoDB, or S3 directly, ClickHouse offers unmatched interoperability.

Snowflake typically requires staging data in internal tables or external stages before querying. It offers fewer direct query options and no built-in engines for joins against external systems. External tables mainly target data lakes (e.g., S3) and still rely on materialized metadata.

Yes
Available on both self-managed and ClickHouse Cloud
Yes
Handles 1,000+ QPS per node natively
Yes
<1s latency for streaming data
Yes
JSON with type fidelity
Yes
Separation of storage and compute by design
Yes
Compute can be added/removed instantly
Yes
Full control over sorting and partitioning
Yes
Native streaming via ClickPipes
Yes
Efficient row-level updates supported
Yes
Async inserts optimized for small batches
Yes
Auto-scales vertically & horizontally
Yes
Yes, via parallel replicas
Yes
Up to 1,000 concurrent queries per node
Yes
Includes vertical scaling for high-memory queries
Yes
Custom hardware profiles supported
Yes
Query-predicate-level controls on cache
Yes
Separation of compute and storage
Yes
Distributed cache across nodes
Yes
Merge states & projections for fast aggregates
Yes
Dictionary acceleration for dimension tables
Yes
Sub-second latency at scale (no extra charges)
Yes
1,000+ concurrent queries per node
Yes
Incremental & refreshable with full SQL support
Yes
38% better compression in benchmarks
Yes
Joins across billions of rows
Yes
Native support via ClickStack
Yes
Real-time analytics with low latency
Yes
Continuous ingestion at scale
Yes
Low-latency SQL over vectors + events
Yes
Internal dashboards supported
Yes
Third-party catalogs supported
Yes
70+ formats including Parquet, ORC, Avro, JSON, CSV
Yes
Connect to Postgres, MongoDB, MySQL, S3, Kafka, and more
Yes
Query in-place via table engines (e.g. Postgres, S3)
Yes
ClickPipes CDC for MySQL and Postgres
Yes
Open format support

No
Cloud only option, no self-managed
Intermediate—
Needs multi-cluster setup with extra cost
Intermediate—
5–10s latency with Snowpipe Streaming
Intermediate—
Semi-structured data but no type preservation
Yes
Supports separation of compute and storage
Yes
Supports stateless compute clusters
Yes
Clustering available, but incurs extra charges
Yes
Streaming via Snowpipe
Yes
Row-level updates supported
Yes
Small batch inserts supported
No
Manual warehouse sizing (t-shirt sizes)
No
Not supported
Intermediate—
Default 8 queries per warehouse
Intermediate—
Horizontal only, nodes are fixed size
Intermediate—
Fixed sizes only
Intermediate—
Cache layer shared across warehouses
Yes
Separation of compute and storage
Yes
Distributed cache supported
No
Not supported
No
Not supported
Intermediate—
Requires clustering + materialized views (enterprise tier)
Intermediate—
8 QPS/warehouse default; needs multi-cluster scaling
Intermediate—
Enterprise-only; refreshable with limited SQL
Intermediate—
Standard columnar compression
Yes
Joins across billions of rows
No
No viable solution; cost prohibitive
Intermediate—
Limited by latency and cost
Intermediate—
Expensive at scale
Intermediate—
High-concurrency workloads not recommended
Yes
Best for batch workloads
No
Third-party catalogs supported
Intermediate—
Limited to common formats (e.g. Parquet, CSV)
Intermediate—
Object stores only
Intermediate—
Requires ingestion or external functions
Yes
CDC supported
Yes
Open format support

Feature comparison of ClickHouse and Snowflake
Featured
Flexible deployment	Yes Available on both self-managed and ClickHouse Cloud	No Cloud only option, no self-managed
High query concurrency1,000+ QPS per node	Yes Handles 1,000+ QPS per node natively	Intermediate— Needs multi-cluster setup with extra cost
Real-time ingest	Yes <1s latency for streaming data	Intermediate— 5–10s latency with Snowpipe Streaming
Native semi-structured data type with type preservation	Yes JSON with type fidelity	Intermediate— Semi-structured data but no type preservation
Shared-storage architecture with decoupled compute and storage	Yes Separation of storage and compute by design	Yes Supports separation of compute and storage
Stateless compute nodes with fast warm-up time	Yes Compute can be added/removed instantly	Yes Supports stateless compute clusters
Control over data ordering and co-location	Yes Full control over sorting and partitioning	Yes Clustering available, but incurs extra charges
Streaming ingestion support	Yes Native streaming via ClickPipes	Yes Streaming via Snowpipe
Lightweight updates / row-level mutation	Yes Efficient row-level updates supported	Yes Row-level updates supported
Micro batch/single row inserts	Yes Async inserts optimized for small batches	Yes Small batch inserts supported
Elastic scalingUp/down with load	Yes Auto-scales vertically & horizontally	No Manual warehouse sizing (t-shirt sizes)
Distributes query execution across nodes	Yes Yes, via parallel replicas	No Not supported
High query concurrency per node	Yes Up to 1,000 concurrent queries per node	Intermediate— Default 8 queries per warehouse
Vertical and horizontal scaling	Yes Includes vertical scaling for high-memory queries	Intermediate— Horizontal only, nodes are fixed size
Custom hardware profiles	Yes Custom hardware profiles supported	Intermediate— Fixed sizes only
Granular cache control	Yes Query-predicate-level controls on cache	Intermediate— Cache layer shared across warehouses
Compute compute separation	Yes Separation of compute and storage	Yes Separation of compute and storage
Distributed cache across warehouse	Yes Distributed cache across nodes	Yes Distributed cache supported
Native aggregate optimizationMerge states, projections	Yes Merge states & projections for fast aggregates	No Not supported
Dictionaries for dimension table acceleration	Yes Dictionary acceleration for dimension tables	No Not supported
Sub-second query latency at scale	Yes Sub-second latency at scale (no extra charges)	Intermediate— Requires clustering + materialized views (enterprise tier)
High concurrency performance	Yes 1,000+ concurrent queries per node	Intermediate— 8 QPS/warehouse default; needs multi-cluster scaling
Materialized view support	Yes Incremental & refreshable with full SQL support	Intermediate— Enterprise-only; refreshable with limited SQL
Data compression	Yes 38% better compression in benchmarks	Intermediate— Standard columnar compression
Join performanceMulti-billion rows	Yes Joins across billions of rows	Yes Joins across billions of rows
ObservabilityLogs, metrics, traces	Yes Native support via ClickStack	No No viable solution; cost prohibitive
Real-time analytics External dashboards, product analytics, customer-facing applications	Yes Real-time analytics with low latency	Intermediate— Limited by latency and cost
Streaming / continuous data ingestion	Yes Continuous ingestion at scale	Intermediate— Expensive at scale
GenAI / LLM agentic workloads	Yes Low-latency SQL over vectors + events	Intermediate— High-concurrency workloads not recommended
Internal analytics and operational dashboards	Yes Internal dashboards supported	Yes Best for batch workloads
Support for third-party catalogs	Yes Third-party catalogs supported	No Third-party catalogs supported
File format support	Yes 70+ formats including Parquet, ORC, Avro, JSON, CSV	Intermediate— Limited to common formats (e.g. Parquet, CSV)
External table engines	Yes Connect to Postgres, MongoDB, MySQL, S3, Kafka, and more	Intermediate— Object stores only
Query external data in-place	Yes Query in-place via table engines (e.g. Postgres, S3)	Intermediate— Requires ingestion or external functions
Change Data Capture (CDC)	Yes ClickPipes CDC for MySQL and Postgres	Yes CDC supported
Support for open table and file formatse.g. Iceberg, Parquet, ORC	Yes Open format support	Yes Open format support

Built for real-time

Power always-on, low-latency, high-concurrency workloads

Predictable pricing

No surprise bills or penalties for usage spikes or need to upgrade to expensive plans to access advanced features

Lower costs

3–5x better performance per dollar than Snowflake, less spend, and more headroom.

Open source and open standards

Flexible deployments models from open source to managed cloud and BYOC, with support for external data catalogues and open table formats

Migrate your workload from Snowflake today

Cut costs, boost performance, and unlock real-time analytics with ClickHouse.
We’ll get you started on a 30 day trial and $300 credits to spend at your own pace.

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ClickHouse vs Snowflake

ClickHouse compared to Snowflake

Join others migrating to ClickHouse from Snowflake

Tired of unpredictable costs, gated features, and pricing models that penalize interactivity?

Built for real-time

Predictable pricing

Lower costs

Open source and open standards

Migrate your workload from Snowflake today

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