Choosing between ClickHouse and Snowflake often comes down to a tradeoff between raw speed and managed convenience. ClickHouse delivers sub-second query performance on raw event data at lower cost, while Snowflake offers elastic scaling and a fully managed platform that handles diverse analytical workloads with minimal tuning.
This guide compares their architectures, performance characteristics, pricing models, and feature sets to help you decide which system fits your use case. You'll also find practical migration steps if you're considering a switch from Snowflake to ClickHouse.
Architecture differences that drive performance
ClickHouse is an open-source OLAP database built for fast, real-time analytics on raw event data, while Snowflake is a fully managed, cloud-native data warehouse designed to handle diverse analytical workloads with minimal configuration. The way each system stores and processes data creates different performance characteristics that matter when you're choosing between them.
Columnar storage and compression
ClickHouse stores data in columns rather than rows, which means queries only read the specific columns they need. When you're aggregating millions of rows to calculate daily active users, ClickHouse skips all the columns your query doesn't reference. Snowflake also uses columnar storage but organizes data into micro-partitions that bundle multiple columns together, so queries sometimes read more data than strictly necessary.
Both systems compress data to save storage space and speed up queries. ClickHouse gives you direct control over compression algorithms like LZ4 or ZSTD, and you can choose specialized codecs for numeric data that compress even further. Snowflake handles compression automatically, which makes setup easier but leaves less room for optimization.
Separation of compute and storage
Snowflake completely separates compute from storage, so you can scale each independently. You can spin up additional virtual warehouses to handle more concurrent queries without touching your storage layer, and you only pay for compute when queries are actually running. This works well when query volume is unpredictable or spiky.
ClickHouse traditionally couples compute and storage more tightly, though ClickHouse Cloud now offers some separation. This tighter coupling reduces latency because data and processing stay physically closer together, which helps when you need sub-second query times. The tradeoff is that scaling requires more planning around cluster sizing.
Data ordering, partitions, and indexing
ClickHouse uses an ORDER BY clause when you create tables to physically sort data on disk. This sorting creates a sparse primary index that makes range queries and time-series filtering very fast. When you partition data by day or hour, ClickHouse can skip entire partitions when your query filters on the partition key.
Snowflake automatically clusters data based on how you ingest and query it, though you can define explicit clustering keys for frequently filtered columns. The automatic approach reduces tuning work but may not optimize as aggressively as a hand-tuned ClickHouse schema for specific query patterns.
Performance benchmarks: is ClickHouse or Snowflake faster
Query speed depends on your workload characteristics, data volume, schema design, and cluster configuration. Both systems deliver fast results, but they excel in different scenarios.
Simple aggregations at 100 GB
ClickHouse typically outperforms Snowflake by 2–10× on simple aggregations like COUNT, SUM, and GROUP BY queries over raw event data. The combination of columnar storage, aggressive compression, and sparse indexing lets ClickHouse scan and aggregate billions of rows in seconds. Calculating daily active users from clickstream data often completes in under a second with ClickHouse.
Snowflake handles these queries well too, but the overhead of its virtual warehouse model and micro-partition architecture adds latency. For workloads that run many simple aggregations repeatedly, ClickHouse's architecture provides a speed advantage.
Join-heavy queries at 1 TB
Snowflake was built for complex data warehousing workloads that involve multiple tables and joins. Its query optimizer and distributed execution engine handle multi-table joins efficiently, especially when tables are properly clustered. Snowflake's ability to scale compute horizontally by adding more nodes to a virtual warehouse helps with large join operations.
ClickHouse can perform joins, but its architecture is optimized for queries that filter and aggregate within a single large table. Joins in ClickHouse work best when the right-hand table fits in memory or when using specialized table engines like Join or Dictionary. For workloads dominated by star schema joins or complex multi-table queries, Snowflake often performs better.
High-cardinality filtering under 1 second
ClickHouse excels at queries that filter on high-cardinality fields like user IDs, session IDs, or trace IDs. The sparse primary index and data ordering let ClickHouse skip large portions of data without scanning, which keeps query times under one second even on petabyte-scale datasets. This makes ClickHouse ideal for observability and monitoring use cases where you need to drill into specific events quickly.
Snowflake's clustering and pruning capabilities also reduce the amount of data scanned, but the overhead of spinning up compute resources and reading from remote storage adds latency. For interactive, ad-hoc queries that require sub-second response times, ClickHouse generally performs better.
Concurrency tests at 500 QPS
ClickHouse uses a shared-nothing architecture where each node processes queries independently. This design handles high query concurrency well, especially when queries are simple and fast. However, each query consumes CPU and memory on the node, so extremely high concurrency can require scaling out the cluster.
Snowflake's multi-cluster warehouse feature automatically scales compute resources to handle spikes in query volume. This elasticity works well for environments with unpredictable concurrency, though the cost of running multiple warehouses can add up quickly.
Pricing models and cost predictability
Understanding how each system charges for compute, storage, and data movement helps you estimate total cost and avoid surprises.
Credit-based consumption vs usage-based nodes
Snowflake uses a credit-based pricing model where you purchase credits upfront or pay as you go. Compute is charged per second based on the size of the virtual warehouse you run, and storage is billed separately per terabyte per month. This separation makes it easy to scale compute and storage independently, but costs can escalate quickly if warehouses are left running or if queries are inefficient.
ClickHouse pricing varies by deployment model. ClickHouse Cloud charges based on compute and storage usage, similar to Snowflake but often at a lower rate. Self-hosted ClickHouse requires provisioning and managing your own infrastructure, which gives you more control over costs but adds operational overhead.
- Snowflake credits: Charged per second of warehouse runtime, with rates varying by warehouse size
- ClickHouse Cloud: Charges based on node hours and storage, typically 30-50% lower than Snowflake for similar workloads
- Self-hosted ClickHouse: Infrastructure costs only, but requires DevOps expertise
Storage costs and data retention strategies
Both systems charge for storage, but the rates and compression ratios differ. ClickHouse's aggressive compression can reduce storage costs significantly, achieving 38% better compression than Snowflake, especially for time-series or log data where compression ratios of 10:1 or higher are common. Snowflake also compresses data but typically achieves lower ratios.
For long-term data retention, consider using tiered storage or moving older data to cheaper storage classes. ClickHouse supports cold storage tiers where infrequently accessed data can be stored on S3 or similar object storage. Snowflake offers Fail-safe and Time Travel features that automatically retain historical data, but these features add to storage costs.
Network egress and cross-cloud charges
Data movement between regions or clouds can add hidden costs. Snowflake charges for data egress when you move data out of the platform, and cross-cloud replication incurs additional fees. If your architecture spans multiple clouds or regions, these costs can become substantial.
ClickHouse also charges for network egress, though self-hosted deployments give you more control over network topology. Keeping data and compute in the same region or cloud minimizes egress costs for both systems.
Advanced architecture and security for cloud-native analytics
Evolving deployment models and BYOC flexibility
Modern analytics teams need flexibility, control and compliance in the same platform. Recent managed ClickHouse patterns support Bring Your Own Cloud (BYOC) so that all data stays inside the customer’s own AWS, GCP or Azure account while the service still provides managed scaling, upgrades and observability, aligning with modern cloud computing principles.This gives organizations operational isolation, lets them reuse existing cloud commits and keeps data residency under their own policies.
At the same time, current architectures keep compute and storage decoupled so that CPU, RAM and IOPS can be scaled without touching object storage.This model fits real-time workloads because it avoids overprovisioning and keeps data on S3, GCS or compatible object storage while compute scales for spikes. The result is a setup that is still low latency but much more cost-aware.
Real-time ingestion and CDC improvements
Real-time analytics depends on getting data in fast. ClickHouse supports native streaming ingestion with Materialized Views that apply transformations on write, and it also connects directly to Kafka, MQTT and database CDC feeds for operational sources, which is essential when working with streaming data.With these capabilities, new events can be available for querying with sub-second latency without adding another processing layer.
For migration and consolidation scenarios, modern ingestion flows use parallelized loads and optimized joins to reduce batch times while keeping data consistent.Incremental ingestion means that only new or changed rows are processed, which protects performance for very large tables and keeps dashboards always fresh. This technique is a foundation of efficient real-time data ingestion workflows.
Security and governance enhancements
Security for analytical databases is no longer just IP allowlists. Current ClickHouse distributions add AES-256 encryption, integration with encrypted object storage, and the ability to encrypt or obfuscate sensitive columns directly in SQL. This improves protection for PII and financial attributes without moving the data out of the cluster.
Enterprises can define role based access control (RBAC) with hierarchical roles and column level permissions, so the same cluster can serve multiple teams with different data visibility. Managed environments include centralized policy management, key rotation and certified controls for common audits, which reduces the amount of custom security glue that teams need to maintain.
Network level security is reinforced through private connectivity options so that query traffic and ingestion traffic stay inside the provider network and do not traverse the public internet. This improves both throughput and attack surface reduction.
Multi cloud and hybrid analytics strategies
Some organizations have to keep a part of their data on premises or in a specific region. Current ClickHouse based stacks support hybrid deployments where an on premises cluster and a cloud cluster work together through object storage and replication. This allows teams to keep sensitive data local while still running global analytics in the cloud.
Support for open table formats and object storage makes it possible to query datasets that live in different clouds without copying everything first. With a single control plane that monitors all clusters, teams can apply the same governance, quotas and alerting across regions and providers.
Observability and operational efficiency
High throughput analytics needs to be observable. ClickHouse exposes system tables for queries, parts, merges and background tasks. When these are exported to Prometheus, Grafana or Datadog, operators can see exactly which workload is generating CPU pressure or storage amplification.
Modern deployments add alerting on query time, error rate and disk growth, and combine this with autoscaling policies so that new compute is added only when real traffic appears.This turns operations into a closed feedback loop: metrics detect pressure, autoscaling reacts, and costs stay under control.
Cost optimization and cloud efficiency at scale
Understanding real cloud economics
Analytics costs are driven by how long compute runs, how much data is scanned and how efficiently it is compressed. Some warehouse style models introduce a large markup between the VM that actually runs the query and the price exposed to the customer. ClickHouse based deployments keep this gap smaller because they run close to raw infrastructure and let users pick the exact instance profile.
For steady 24x7 workloads, this is important. A real time dashboard that customers hit all day will be cheaper on an engine that can stay hot and scan less data than on a model that spins up large virtual warehouses for every burst. ClickHouse uses columnar storage plus CPU cache optimizations to keep the cost per query low even when concurrency grows.
Compression, storage tiers and retention policies
Storage is where long term costs appear. ClickHouse routinely achieves very high compression ratios on time series, log and event data, often 10:1 or better, and in many cases noticeably better than generic columnar storage. This reduces object storage bills and also reduces the amount of data that queries have to read.
On top of compression, teams can enable tiered storage. Recent data stays on fast local or SSD layers for millisecond queries. Older data is offloaded to cheaper S3 or GCS classes and is still queryable when needed.
This combination lets companies keep months or years of observability or product analytics without linearly increasing spend.
Benchmarking and elastic scaling behavior
Elasticity is useful only if it is predictable. ClickHouse maintains sub second latency for simple aggregates even when queries per second increase, because each node processes its own shard of data without a central coordinator becoming a bottleneck.
In environments where hundreds of users run similar queries, this architecture avoids having to provision independent compute clusters for every group.
When traffic changes, autoscaling can add replicas or change instance size instead of starting an entirely new warehouse. This is a simpler and cheaper scaling unit, especially for API style analytics where query patterns are known in advance.
Open source economics and transparent billing
Because ClickHouse is open and its system tables expose per query resource usage, teams can build their own cost models that map queries to business units or customers. There is no opaque credit translation layer. This transparency is useful in multi tenant products where costs need to be recharged or shown to customers.
Once costs are observable, they can be enforced. CI/CD pipelines can run cost guardrails that reject changes which increase scanned bytes or merge pressure above a threshold. This keeps performance and budgets aligned over time.
Modern cloud practices for cost control
There are several practical levers to keep analytics spend under control:
Right size compute by choosing instance families that match CPU and disk throughput to actual ClickHouse merge and query workloads.
Schedule cluster suspension or downscale during off hours in environments with predictable traffic.
Audit compression and table engines regularly to ensure that new tables follow the same storage policies as the core event tables.
Keep data and compute in the same region to avoid egress and cross cloud replication fees.
These practices work particularly well with ClickHouse because the engine already minimizes data reads and uses background merges to maintain performance.
Building cost efficient real time stacks
A common pattern is to run ClickHouse inside Kubernetes with an operator that automates replica creation, backups and version upgrades.This deployment model supports scalable real-time data processing and analytics for customer-facing dashboards.
This approach gives a fixed envelope of cost and still allows real time analytics for customer facing dashboards. Because the operator understands ClickHouse internals, it can scale only the parts that are actually under pressure.
This model is suitable for multi tenant SaaS analytics. Instead of creating one warehouse per tenant, a single ClickHouse cluster can host multiple databases and apply RBAC and schema level isolation.
Compute is shared, so the total cost per tenant is lower, but users still see their own data only.
Evaluating cost efficiency over time
A good analytics platform should get cheaper per unit of business as the product grows.
ClickHouse supports this outcome because query performance degrades slowly as data sets grow, especially when tables are ordered and partitioned correctly. With compression and tiered storage, keeping more data does not multiply costs at the same rate.
Teams can track KPIs such as cost per million events, cost per active user, storage saved by compression and average query latency per GB.
If these KPIs stay flat or improve while traffic grows, the architecture is efficient. If they spike, observability from system tables can show which queries or tables need to be redesigned.
Infrastructure governance and financial observability
Cost control is easier when infrastructure, performance and finance use the same signals.
By sending ClickHouse metrics to a monitoring stack, teams can create dashboards that show query latency, CPU, merges, storage growth and cost estimates in a single place.
Alerting on these metrics prevents silent cost regressions such as an unbounded materialized view or an ingestion job that duplicates data.
Managed ClickHouse platforms already expose usage, query and resource reports that make this financial observability available to developers, not only to ops. This keeps analytics fast and also keeps the monthly bill aligned with what the business expects.
ClickHouse vs Snowflake features comparison for real-time analytics
Feature parity matters when choosing a database, especially for real-time analytics where certain capabilities are table stakes.
Materialized views and CDC ingestion
ClickHouse offers real-time materialized views that incrementally update as new data arrives. This lets you pre-aggregate data or transform it on write, which speeds up read queries. Materialized views in ClickHouse are lightweight and efficient, making them ideal for real-time dashboards and alerts.
Snowflake supports materialized views as well, but they are not incrementally maintained by default. Instead, Snowflake uses streams and tasks to implement change data capture (CDC) and incremental processing. This approach works but requires more setup and incurs additional compute costs for running tasks.
Time series functions and windowing
ClickHouse includes specialized functions for time-series analysis, such as toStartOfInterval, windowFunnel, and retention. These functions make it easy to analyze event sequences, calculate retention cohorts, and perform session analysis. Window functions like ROW_NUMBER and LAG are also supported for more complex analytical queries.
Snowflake provides a comprehensive set of window functions and time-series capabilities, including LEAD, LAG, and FIRST_VALUE. For general-purpose analytics, Snowflake's SQL dialect is more familiar to users coming from traditional data warehouses. However, ClickHouse's specialized functions often perform better for event-driven analytics.
Semi-structured data handling
Both systems handle JSON, arrays, and nested data types, but with different approaches. ClickHouse supports nested columns and array types natively, allowing you to store and query complex data structures efficiently. Functions like arrayJoin and JSONExtractString make it easy to work with semi-structured data.
Snowflake treats JSON as a VARIANT type and provides functions like FLATTEN and GET_PATH to extract fields. Snowflake's approach is more flexible for schema-on-read scenarios, while ClickHouse's typed columns offer better query performance when the schema is known.
Role-based access and masking
Snowflake offers mature role-based access control (RBAC) with support for row-level security and dynamic data masking. You can define policies that restrict access to sensitive data based on user roles, which is important for compliance and governance.
ClickHouse supports user-based access control and SQL-based grants, but row-level security and data masking require more manual setup. For enterprise environments with strict security requirements, Snowflake's built-in governance features are more mature.
| Feature | ClickHouse | Snowflake |
|---|---|---|
| Real-time materialized views | Yes, incremental | Limited, requires tasks |
| Time-series functions | Extensive, specialized | Standard SQL, general-purpose |
| JSON and nested types | Native, typed columns | VARIANT, schema-on-read |
| Row-level security | Manual setup | Built-in policies |
| Query latency | Sub-second for simple queries | Seconds, depends on warehouse |
| Concurrent users | Scales with cluster size | Elastic, multi-cluster |
Best option for database software: ClickHouse or Snowflake
Choosing between ClickHouse and Snowflake depends on your workload characteristics, team expertise, and priorities around cost, performance, and ease of use.
Decision matrix by workload pattern
If your primary use case is real-time analytics on high-volume event data like logs, metrics, or clickstream data, ClickHouse is often the better choice. Its architecture delivers sub-second query latency and lower costs for these workloads. Use cases like observability, monitoring, and real-time dashboards benefit from ClickHouse's speed and efficiency.
If your workload involves complex data warehousing, multiple data sources, and diverse analytical queries, Snowflake's ease of use and elastic scaling make it a strong option. Snowflake handles mixed workloads well, including ad-hoc queries, batch processing, and machine learning pipelines. Teams that prioritize managed infrastructure and minimal operational overhead often prefer Snowflake.
Total cost scenarios at different scales
At smaller scales (under 1 TB), both systems are cost-effective, but ClickHouse often delivers better price-performance for simple queries. As data volume grows, ClickHouse's compression and query efficiency can result in significant cost savings, with querying costing 7× less than Snowflake, especially for high-throughput, repetitive queries.
At larger scales (10 TB and above), operational complexity becomes a factor. Self-hosted ClickHouse requires DevOps expertise to manage clusters, tune performance, and handle scaling. Snowflake's fully managed model reduces operational burden but can become expensive if compute usage is not carefully monitored.
- Choose ClickHouse for: Sub-second queries, cost optimization, observability data, real-time dashboards, high-throughput ingestion
- Choose Snowflake for: Ease of use, diverse workloads, managed infrastructure, complex joins, elastic scaling, mature governance
Migrating workloads from Snowflake to ClickHouse step by step
Migrating from Snowflake to ClickHouse involves planning around schema differences, data movement, and query translation. A phased approach reduces risk and allows you to validate each step.
Dual-write ingestion setup
Start by implementing parallel data pipelines that write to both Snowflake and ClickHouse. This allows you to validate data consistency and test ClickHouse performance without disrupting existing systems. Use tools like Kafka, Airbyte, or custom ETL scripts to duplicate writes.
Monitor data arrival and compare row counts, checksums, and sample queries between the two systems. This dual-write period gives you confidence that ClickHouse is receiving and processing data correctly.
Backfill historical data
Export historical data from Snowflake using COPY INTO or UNLOAD commands to stage data in S3 or another object storage. Transform the data to match ClickHouse's schema, paying attention to data types, date formats, and nested structures.
Load the data into ClickHouse using the INSERT INTO ... SELECT pattern or by reading directly from S3 with the s3 table function. Optimize ClickHouse table schemas by choosing appropriate ORDER BY keys, partition keys, and compression codecs based on your query patterns.
Validate queries and access patterns
Translate Snowflake SQL queries to ClickHouse syntax. Most queries will work with minimal changes, but some functions and window operations may require adjustments. Test query performance on ClickHouse to ensure it meets your latency requirements.
Update application connection strings, drivers, and API integrations to point to ClickHouse, ensuring you follow SQL optimization best practices for your new queries. If you're using Tinybird, you can create REST API endpoints from your ClickHouse queries, which simplifies integration with application backends.
Cut over and decommission Snowflake
Once you've validated data consistency and query performance, gradually shift production traffic to ClickHouse. Start with non-critical workloads or read-only queries, then move essential workloads after confirming stability.
After the cutover is complete and ClickHouse is handling all production traffic, decommission Snowflake resources to stop incurring costs. Keep Snowflake data available for a short period as a fallback, then delete it once you're confident in the migration.
The bottom line and next steps with Tinybird
ClickHouse and Snowflake serve different needs, and the right choice depends on your priorities around speed, cost, ease of use, and operational complexity. ClickHouse delivers faster query performance and lower costs for real-time analytics, while Snowflake offers a more managed experience with better support for complex data warehousing.
Why managed ClickHouse speeds delivery
Managing ClickHouse infrastructure requires expertise in cluster configuration, scaling, and performance tuning. Tinybird eliminates this complexity by providing a fully managed ClickHouse platform that handles infrastructure, scaling, and optimization automatically. This allows developers to focus on building features instead of managing databases.
Tinybird also adds a developer-friendly API layer on top of ClickHouse, making it easy to expose ClickHouse queries as REST APIs. This speeds up integration with application backends and removes the need to write custom API code.
Sign up for a free Tinybird plan
You can start using Tinybird in minutes by signing up for a free account at https://cloud.tinybird.co/signup. The free tier includes enough resources to test ClickHouse queries, ingest sample data, and create API endpoints.
FAQs about ClickHouse vs Snowflake
How does open source governance affect ClickHouse roadmap risk?
ClickHouse's open-source nature provides transparency into development priorities and allows the community to contribute features and fixes. This reduces vendor lock-in compared to Snowflake's proprietary roadmap, where feature development is controlled entirely by Snowflake.
Can ClickHouse coexist with Snowflake in a hybrid data stack?
Many organizations run both systems for different use cases, using ClickHouse for real-time analytics and Snowflake for complex data warehousing and business intelligence workloads. Data can be replicated between the two systems using ETL tools like Airbyte or Fivetran, though this adds operational complexity and cost.
What tooling exists for automatic cost monitoring in ClickHouse deployments?
ClickHouse offers built-in system tables like system.query_log and system.metrics for resource monitoring. Cloud providers and third-party tools like Grafana, Datadog, and Prometheus provide cost tracking and alerting for managed deployments. Tinybird includes observability features that track query performance and resource usage automatically.