ClickHouse and Snowflake are both powerful analytical databases, but they differ significantly in architecture, pricing, and optimal use cases. Snowflake pioneered the cloud data warehouse with its separation of storage and compute, while ClickHouse delivers unmatched query performance for real-time analytics. This comparison helps you understand which platform best fits your analytical needs.
Platform Overview
ClickHouse
ClickHouse is an open-source columnar database designed for real-time analytical queries. It’s available as self-managed software or as ClickHouse Cloud, a fully managed service.
Key characteristics:
- Blazing fast query performance
- Real-time data ingestion and querying
- Open source with managed cloud option
- Exceptional compression ratios
- Cost-effective at scale
Snowflake
Snowflake is a fully managed cloud data warehouse offering separation of storage and compute with automatic scaling and zero maintenance.
Key characteristics:
- Fully managed, zero operations
- Separation of storage and compute
- Near-unlimited concurrency
- Time travel and data sharing
- Multi-cloud deployment
Architecture Comparison
ClickHouse Architecture
ClickHouse uses a shared-nothing architecture with tight coupling between storage and compute:
- Storage: MergeTree engine with columnar format
- Compute: Query processing on same nodes as data
- Scaling: Add nodes, redistribute data
- Replication: Async replication across replicas
Snowflake Architecture
Snowflake separates storage and compute entirely:
- Storage: Centralised in cloud object storage
- Compute: Virtual warehouses scale independently
- Scaling: Spin up warehouses on demand
- Replication: Automatic within cloud region
Performance Benchmarks
Query Performance
| Query Type | ClickHouse | Snowflake (Medium) |
|---|---|---|
| Simple aggregation (1B rows) | 0.8s | 4-8s |
| Complex GROUP BY | 1.5s | 8-15s |
| Time-series query | 0.3s | 3-6s |
| Large JOIN | 5s | 10-20s |
| Query cold start | None | 1-3s |
ClickHouse performance advantages:
- No cold start latency
- Vectorised query execution
- Data stored in optimised format
- Minimal overhead between query and data
Snowflake performance considerations:
- Virtual warehouse startup adds latency
- Excellent for concurrent workloads
- Performance scales linearly with warehouse size
- Caching improves repeated queries
Concurrency
| Metric | ClickHouse | Snowflake |
|---|---|---|
| Concurrent queries | 100-200 per node | Unlimited (warehouse scaling) |
| Query queueing | Possible under load | Warehouses handle isolation |
| Resource isolation | Limited | Excellent |
Pricing Comparison
ClickHouse Cloud Pricing
Compute: ~$0.30-0.50 per compute hour
Storage: ~$0.04 per GB/month
Ingestion: Included
Queries: Included in computeSnowflake Pricing
Compute: $2-4+ per credit (varies by tier)
1 credit ≈ 1 hour of X-Small warehouse
Storage: $23-40 per TB/month (compressed)
Data transfer: Cloud provider ratesCost Analysis
| Scenario | ClickHouse Cloud | Snowflake |
|---|---|---|
| 10TB, moderate queries | ~$800/month | ~$2,500/month |
| 100TB, heavy analytics | ~$4,000/month | ~$15,000/month |
| Real-time dashboards | Very efficient | Credit-intensive |
ClickHouse cost advantages:
- Lower per-query costs for intensive workloads
- No credit system complexity
- Efficient for always-on workloads
Snowflake cost advantages:
- Pay only when computing (auto-suspend)
- Predictable with resource monitors
- Multi-cluster for isolation
For detailed cost strategies, see our cloud cost optimisation guide.
Feature Comparison
| Feature | ClickHouse | Snowflake |
|---|---|---|
| Real-time ingestion | Excellent | Good (Snowpipe) |
| Query performance | Fastest | Very good |
| Concurrency scaling | Good | Excellent |
| Zero maintenance | Cloud only | Yes |
| Time travel | Limited | 90 days |
| Data sharing | Basic | Excellent |
| Semi-structured (JSON) | Good | Excellent |
| Geospatial | Basic | Good |
| ML integration | Limited | Snowpark |
| Ecosystem | Growing | Extensive |
Use Case Recommendations
Choose ClickHouse When:
Real-time analytics and dashboards
-- Sub-second response on billions of events
SELECT
toStartOfHour(timestamp) AS hour,
count() AS events,
uniqExact(user_id) AS unique_users,
quantile(0.95)(response_time) AS p95_latency
FROM web_events
WHERE timestamp >= now() - INTERVAL 24 HOUR
GROUP BY hour
ORDER BY hourHigh-frequency data ingestion
- Millions of events per second
- Log and event analytics
- Time-series metrics
- IoT sensor data
Cost-sensitive workloads
- Always-on analytical queries
- High query volumes
- Predictable, low-cost scaling
Choose Snowflake When:
Enterprise data warehousing
-- Complex analytics with time travel
SELECT * FROM sales_data
AT(TIMESTAMP => '2026-01-01 00:00:00'::timestamp)
WHERE region = 'EMEA';Data sharing requirements
- Share data with partners
- Data marketplace participation
- Cross-organisation collaboration
Variable workloads
- Burst capacity needs
- Multiple teams with isolation
- Seasonal analytics patterns
Broad ecosystem needs
- Many data source integrations
- BI tool compatibility
- ML with Snowpark
Integration and Ecosystem
ClickHouse Ecosystem
Data ingestion:
- Kafka, Kinesis direct integration
- ClickPipes for managed ingestion
- HTTP interface for custom sources
- Vector, Fluent Bit for logs
BI and visualisation:
- Grafana (excellent integration)
- Metabase, Superset
- Tableau, Looker
Orchestration:
- dbt (adapter available)
- Airflow, Dagster
Snowflake Ecosystem
Data ingestion:
- Snowpipe for continuous loading
- 100+ connectors
- Fivetran, Airbyte, Stitch
BI and visualisation:
- Native Snowsight
- All major BI tools
Advanced features:
- Snowpark for Python/Java/Scala
- Streamlit integration
- Data marketplace
Operational Comparison
ClickHouse Operations
Self-managed:
- Full control over configuration
- Requires database expertise
- Lower costs, higher effort
- Kubernetes operators available
ClickHouse Cloud:
- Managed infrastructure
- Automatic scaling
- Built-in monitoring
- Simplified operations
Snowflake Operations
- Fully managed, zero administration
- Automatic optimisation
- Built-in monitoring and alerts
- No infrastructure decisions
Migration Paths
To ClickHouse from Snowflake
Consider when:
- Query performance is critical
- Real-time requirements
- Cost reduction needed
- Simpler data model
Migration approach:
- Export data via COPY INTO
- Transform to ClickHouse schema
- Load using clickhouse-client
- Migrate queries (mostly compatible SQL)
To Snowflake from ClickHouse
Consider when:
- Need data sharing features
- Want fully managed experience
- Require Snowpark ML capabilities
- Variable workload patterns
Hybrid Architecture
Many organisations use both platforms:
┌─────────────┐ ┌─────────────┐
│ Snowflake │ │ ClickHouse │
│ (DW/ETL) │────▶│ (Real-time) │
└─────────────┘ └─────────────┘
│ │
▼ ▼
Batch Reports Live Dashboards
Data Science Operational Analytics
Ad-hoc Analysis Monitoring- Snowflake: Data warehouse, complex transformations, data science
- ClickHouse: Real-time dashboards, operational analytics, monitoring
Conclusion
Both ClickHouse and Snowflake are excellent analytical platforms with different strengths:
Choose ClickHouse for real-time analytics, high-performance queries, cost efficiency, and always-on workloads where sub-second latency matters.
Choose Snowflake for enterprise data warehousing, data sharing, variable workloads, and when fully managed operations and broad ecosystem support are priorities.
Consider using both if you need Snowflake’s data warehouse capabilities alongside ClickHouse’s real-time performance for operational dashboards.
Need help selecting the right analytics platform? Contact our team to discuss your data architecture requirements.
Related Resources
- How Tasrie IT Services Uses ClickHouse
- ClickHouse vs BigQuery 2026
- ClickHouse vs Redshift 2026
- Cloud Native Database Guide 2026
External Resources: