ON THIS PAGE
## Architecture
The biggest difference among cloud data warehouses are whether they separate storage and compute, how much they isolate data and compute, and what clouds they can run on.
| Feature | Snowflake | ClickHouse |
|---|---|---|
| Separation of storage and compute | Yes | Yes – SharedMergeTree engine in ClickHouse Cloud enables full separation of storage and compute, with compute-compute separation through Warehouses feature (introduced 2025) allowing multiple isolated compute services sharing the same data |
| Supported cloud infrastructure | AWS, Azure, GCP with full feature parity across all three major clouds | AWS, GCP, Azure, cloud service and on-premises |
| Isolated tenancy – option for dedicated resources | • Multi-tenant pooled resources • Isolated tenancy available via VPS tier | • Multi-tenant metadata layer • Isolated tenancy for compute & storage per client in cloud |
| Control vs abstraction of compute | • Configurable warehouse sizes (XS to 6XL) • Multi-cluster warehouses with auto-scaling • Choice between Generation 1 and Generation 2 standard warehouses • MAX_CONCURRENCY_LEVEL parameter for resource allocation | Configurable cluster size and compute types in ClickHouse Cloud with granular control over nodes (1-128 nodes) and node characteristics. Warehouses feature enables multiple isolated read-only compute environments. |
| Self-hosted and hybrid deployment options | Snowflake for Government Cloud and private cloud options available | Self-managed deployments available with full control over infrastructure |
| ACID Compliance and Transactions | Full ACID compliance with Time Travel and zero-copy cloning capabilities | Limited ACID compliance with MergeTree engine family. |
Snowflake was one of the first decoupled storage and compute architectures, making it the first to have nearly unlimited compute scale and workload isolation, and horizontal user scalability. It runs on AWS, Azure and GCP. It is multi-tenant over shared resources in nature and requires you to move data out of your VPC and into the Snowflake cloud. "Virtual Private Snowflake" (VPS) is its highest-priced tier, and can run a dedicated isolated version of Snowflake. Its virtual warehouses can be T-shirt sized along an XS/S/M…/4XL axis, where each discrete T-shirt size is bundled with fixed HW properties that are abstracted from the users. Snowflake has recently added support for Snowflake managed Iceberg tables.
ClickHouse was originally developed at Yandex, the Russian search engine, as an OLAP engine for low latency analytics. It was built as an on-premise solution with coupled storage & compute, and a large variety of tuning options in the form of indexes and and merge trees. ClickHouse's architecture is famous for its focus on performance and low-latency queries. The tradeoff is that it is considered very difficult to work with. SQL support is very limited, and tuning/running it requires significant engineering resources.
## Scalability
There are three big differences among data warehouses and query engines that limit scalability: decoupled storage and compute, dedicated resources, and continuous ingestion.
| Feature | Snowflake | ClickHouse |
|---|---|---|
| Elasticity – Scaling for larger data volumes and faster queries | • Instant warehouse resize (XS to 6XL) with no downtime • Multi-cluster auto-scaling • Generation 2 warehouses provide ~2x performance improvement over Generation 1 | Automatic horizontal and vertical scaling in ClickHouse Cloud with SharedMergeTree architecture. Manual scaling for self-managed deployments with cluster rebalancing capabilities |
| Elasticity – Scaling for higher concurrency | • Single warehouse supports many concurrent queries (MAX_CONCURRENCY_LEVEL=8 controls resource allocation per query, not query limit) • Multi-cluster warehouses enable thousands of concurrent queries with auto-scaling • Unlimited virtual warehouses can be created | Supports high concurrency with proper resource allocation and configuration. Vertical auto-scaling and horizontal manual scaling. Additional warehouses can idle to zero billing. Primary service always on in multi-warehouse configurations. |
Snowflake scales very well both for data volumes and query concurrency. The decoupled storage/compute architecture supports resizing clusters without downtime, and in addition, supports auto-scaling horizontally for higher query concurrency during peak hours.
ClickHouse doesn't offer any dedicated scaling features or mechanisms. While it can deliver linearly scalable performance for some types of queries, scaling itself has to be done manually. Hardware is self-managed in ClickHouse. This means that to scale you would have to provision a cluster and migrate.
## Performance
Performance is the biggest challenge with most data warehouses today. While decoupled storage and compute architectures improved scalability and simplified administration, for most data warehouses it introduced two bottlenecks; storage, and compute. Most modern cloud data warehouses fetch entire partitions over the network instead of just fetching the specific data needed for each query. While many invest in caching, most do not invest heavily in query optimization. Most vendors also have not improved continuous ingestion or semi-structured data analytics performance, both of which are needed for operational and customer-facing use cases.
| Feature | Snowflake | ClickHouse |
|---|---|---|
| Indexes | • Search Optimization Service for point lookups and selective queries (additional cost) • Clustering keys for data organization and automatic clustering • Materialized views • Snowflake Optima automatic indexing on Generation 2 warehouses (no additional cost) • No traditional database indexes | • Primary indexes • Skipping indexes (minmax, set, bloom filters, ngrambf_v1, tokenbf_v1) • MergeTree indexes • Incremental Materialized views |
| Compute tuning | • Warehouse T-shirt sizing (XS to 6XL) • Multi-cluster configuration and scaling policies • Generation 1 vs Generation 2 warehouse selection • MAX_CONCURRENCY_LEVEL parameter tuning • Query Acceleration Service for long-running queries | Configurable compute resources in cloud offering |
| Storage format | Columnar micro-partitioned & compressed storage | Columnar, supports sorted, compressed, encoded & sparsely indexed files with native Apache Iceberg support. |
| Table-level partition & pruning techniques | • Data automatically divided into micro-partitions • Automatic pruning at micro-partition level • Clustering keys for data organization with automatic clustering • Snowflake Optima provides additional automatic pruning optimization on Gen2 warehouses | Partitioning by date/time and custom partitions with MergeTree indexes. |
| Result cache | Yes | Yes, results cache with TTL and query condition cache. |
| Warm cache (SSD) | Yes, at micro-partition level granularity | Yes, at indexed data-range level granularity |
| Support for semi-structured data & JSON functions within SQL | Yes | Yes, including Lambda expressions and native JSON data type (GA in v25.3) |
| Vector Search and AI Capabilities | AI integration through Cortex AI and Snowpark ML | • Native vector search capabilities and embeddings • MCP Server for AI driven analytics • Natural Language to SQL • SQL based Inference |
| Query Optimizations | • Search Optimization Service for point lookups (additional cost) • Query Acceleration Service (QAS) for long-running and unpredictable workloads • Snowflake Optima automatic optimization on Generation 2 warehouses (no additional cost) • Automatic clustering with background maintenance • Materialized views with automatic refresh • Result cache (24hrs) • Cost-based optimization with dynamic query rewriting | • Primary indexes (ORDER BY) • Data skipping indexes (minmax, set, bloom filters, ngrambf_v1, tokenbf_v1) • Materialized views • Projections • PREWHERE optimization • Query analysis tools • Automatic global join reordering (v25.9) • Enhanced JSON query optimization • Streaming secondary indices |
Snowflake typically comes on top for most queries when it comes to performance in public TPC-based benchmarks when compared to BigQuery and Redshift, but only marginally. Its micro partition storage approach effectively scans less data compared to larger partitions. The ability to isolate workloads over the decoupled storage & compute architecture lets you avoid competition for resources compared to multi-tenant shared resource solutions, and the ability to increase warehouse sizes can often enhance performance (for a higher price), but not always linearly. Snowflake's recently released "Search optimization service" delivers index-like behavior for point queries, but comes at an additional cost.
ClickHouse is famous for being one of the fastest local runtimes ever built for OLAP workloads. Its columnar storage, compression and indexing capabilities make it a consistent leader in benchmarks. Its lack of support for standard SQL and lack of query optimizer means that it's less suitable for traditional BI workloads, and more suitable for engineering managed workloads. While fast, it requires a lot of tuning and optimization.
## Use cases
There are a host of different analytics use cases that can be supported by a data warehouse. Look at your legacy technologies and their workloads, as well as the new possible use cases, and figure out which ones you will need to support in the next few years.
| Feature | Snowflake | ClickHouse |
|---|---|---|
| Low-latency dashboards | • Sub-second to seconds response times at TB+ scale with proper clustering and optimization • Enhanced by Query Acceleration Service and Search Optimization Service • Generation 2 warehouses provide ~2x performance improvement over Generation 1 • Snowflake Optima provides automatic optimization | • Sub-second load times at TB+ scale with proper indexing • ClickHouse Cloud reduces engineering overhead with managed service • Proven low-latency performance (120ms at 2500 QPS in benchmarks) • Purpose-built for low-latency OLAP and real-time analytics |
| Enterprise BI | • Mature and comprehensive Enterprise DW feature set • Extensive integrations with Enterprise BI ecosystem • Multi-cloud deployment options with consistent experience • Strong SQL compliance and wide ecosystem support • Zero-copy data sharing capabilities | • Growing ecosystem with 50+ integrations including major BI tools • Native MySQL protocol support enables broad BI tool compatibility • Strong SQL compliance with PostgreSQL compatibility • Best suited for modern analytical workloads and engineering-managed use cases |
| Data Apps and AI Applications (Customer-facing low-latency high concurrency) | • Multi-cluster warehouses support thousands of concurrent users with auto-scaling • Individual warehouses support many concurrent queries (not limited to 8 concurrent queries) • Sub-second to seconds response times with proper optimization • Generation 2 warehouses provide significant performance improvements for high-concurrency workloads • AI integration through Cortex AI | • Sub-second response times at TB+ scale • Supports 1000 concurrent users per replica • Strong price-performance on customer-facing applications • Native vector search and embeddings |
| Ad hoc | • Excellent for ad-hoc with decoupled storage/compute • Auto-scaling and instant compute provisioning • Minimal predefined optimization required • Query Acceleration Service handles unpredictable workloads automatically • Snowflake Optima provides automatic optimization for recurring patterns | • Good for ad-hoc queries with ClickHouse Cloud's separated storage/compute architecture • Join optimizations enable more query complexity • Strong sampling capabilities (TABLESAMPLE) for exploratory analysis • Resource management through user quotas prevents query interference • Materialized views offer performance improvements for common aggregation patterns, ad-hoc users specify directly in SQL |
Snowflake is a well rounded general purpose cloud data warehouse, that can also span beyond traditional BI & Analytics use cases into Ad-Hoc and ML use cases. Thanks to the flexible decoupeld storage & compute architecture that allows you to isolate and control the amount of compute per workload, it's possible to tackle a broad spectrum of workloads. However, like its close siblings Redshift & BigQuery, it struggles to deliver low-latency query performance at scale, making it a lesser fit for operational use cases and customer-facing data apps.
ClickHouse was not designed to be a data warehouse, but rather a low-latency query execution runtime. Managing it typically requires significant engineering overhead. Hence, it's a good fit for engineering managed operational use cases and customer-facing data apps, where low latency matters. It is not a good fit for a general purpose data warehouse, nor for Ad-Hoc analytics or ELT.