ClickHouse vs Redshift 2026: Cloud Data Warehouse Comparison

ClickHouse and Amazon Redshift are both popular choices for analytical workloads, but they differ significantly in architecture, performance characteristics, and pricing models. Redshift offers deep AWS integration with a traditional data warehouse approach, while ClickHouse delivers superior query performance with more flexible deployment options. This comparison helps you choose the right platform for your analytics needs.

Platform Overview

ClickHouse

ClickHouse is an open-source columnar database optimised for real-time analytical queries, available self-managed or via ClickHouse Cloud.

Key characteristics:

• Fastest query performance
• Real-time data ingestion
• Excellent compression (10-20x)
• Multi-cloud and self-hosted options
• Open source

Amazon Redshift

Redshift is AWS’s fully managed data warehouse service, offering tight integration with the AWS ecosystem and petabyte-scale analytics.

Key characteristics:

• Deep AWS ecosystem integration
• Fully managed service
• Redshift Serverless option
• Mature enterprise features
• Petabyte scale

Architecture Comparison

ClickHouse Architecture

┌─────────────────────────────────────────┐
│         ClickHouse Cluster              │
├─────────────────────────────────────────┤
│  ┌─────────┐  ┌─────────┐  ┌─────────┐  │
│  │ Shard 1 │  │ Shard 2 │  │ Shard N │  │
│  │ (Node)  │  │ (Node)  │  │ (Node)  │  │
│  └────┬────┘  └────┬────┘  └────┬────┘  │
│       │            │            │       │
│       └────────────┼────────────┘       │
│                    │                    │
│     Local SSD / Object Storage          │
│       (MergeTree, Columnar)             │
└─────────────────────────────────────────┘

Redshift Architecture

┌─────────────────────────────────────────┐
│          Redshift Cluster               │
├─────────────────────────────────────────┤
│  ┌─────────────────────────────────┐    │
│  │         Leader Node             │    │
│  │   (Query Planning, Results)     │    │
│  └───────────────┬─────────────────┘    │
│                  │                      │
│  ┌───────────────▼─────────────────┐    │
│  │        Compute Nodes            │    │
│  │  ┌─────┐  ┌─────┐  ┌─────┐     │    │
│  │  │ DC2 │  │ DC2 │  │ DC2 │     │    │
│  │  │ RA3 │  │ RA3 │  │ RA3 │     │    │
│  │  └─────┘  └─────┘  └─────┘     │    │
│  └─────────────────────────────────┘    │
│                  │                      │
│        Managed Storage (RA3)            │
└─────────────────────────────────────────┘

Performance Comparison

Query Performance

Query Type	ClickHouse	Redshift (RA3)
Simple aggregation (1B rows)	0.5s	5-10s
Complex GROUP BY	1s	8-15s
Time-series rollup	0.3s	3-6s
Large JOIN	3s	10-20s
Concurrent queries	Excellent	Good

ClickHouse advantages:

• 5-10x faster for most analytical queries
• No cold start latency
• Better compression reduces I/O
• Vectorised execution

Redshift advantages:

• Automatic query optimisation over time
• Workload Management (WLM) for mixed workloads
• Better for complex multi-statement transactions
• Mature result caching

Concurrency and Scaling

Metric	ClickHouse	Redshift
Max concurrent queries	100+ per node	Limited by WLM slots
Scaling approach	Add nodes	Resize or Serverless
Scale time	Minutes	10-30 minutes (classic)
Serverless option	ClickHouse Cloud	Redshift Serverless

Pricing Comparison

ClickHouse Cloud Pricing

Compute: ~$0.30-0.50 per compute hour
Storage: ~$0.04 per GB/month
Ingestion: Included
Data transfer: Standard rates

Redshift Pricing

Provisioned (RA3):

RA3.xlplus: $1.086/hour (4 vCPU, 32GB)
RA3.4xlarge: $3.26/hour (12 vCPU, 96GB)
RA3.16xlarge: $13.04/hour (48 vCPU, 384GB)
Managed storage: $0.024 per GB/month

Serverless:

$0.36-0.50 per RPU-hour
Storage: $0.024 per GB/month
Minimum: Base capacity RPUs

Cost Comparison Scenarios

Scenario	ClickHouse Cloud	Redshift RA3
10TB, moderate load	~$1,500/month	~$3,000/month
50TB, heavy analytics	~$4,000/month	~$8,000/month
100TB, enterprise	~$8,000/month	~$15,000/month

ClickHouse typically provides 40-60% cost savings for comparable analytical workloads.

For comprehensive cost strategies, see our AWS cost optimisation guide.

Feature Comparison

Feature	ClickHouse	Redshift
Query language	ClickHouse SQL	PostgreSQL-compatible
Real-time ingestion	Excellent	Good (Streaming)
AWS integration	Via S3, Kinesis	Native
ML integration	Limited	Redshift ML
Data sharing	Basic	Excellent
Federated queries	S3, remote DBs	Spectrum, Federation
Concurrency scaling	Manual	Automatic
Workload management	Basic	Advanced WLM
Backup/restore	Manual/Cloud	Automatic
Encryption	Yes	Yes (integrated)

AWS Integration Comparison

ClickHouse on AWS

-- Query S3 data directly
SELECT *
FROM s3(
    'https://bucket.s3.amazonaws.com/data/*.parquet',
    'AWS_ACCESS_KEY',
    'AWS_SECRET_KEY',
    'Parquet'
)
WHERE date >= '2026-01-01'

Integration options:

• S3 for data import/export
• Kinesis via Kafka interface
• IAM roles (ClickHouse Cloud)
• VPC endpoints

Redshift on AWS

-- Native Spectrum for S3
SELECT *
FROM spectrum_schema.external_table
WHERE date >= '2026-01-01';

-- Federated queries to RDS
SELECT *
FROM pg_catalog.svv_external_tables
WHERE schemaname = 'federated_rds';

Native integrations:

• S3 (COPY, UNLOAD, Spectrum)
• Kinesis Data Firehose
• DynamoDB export
• SageMaker (Redshift ML)
• QuickSight, Athena
• Lake Formation
• Secrets Manager

Use Case Recommendations

Choose ClickHouse When:

Real-time analytics and dashboards

-- Sub-second dashboard queries
SELECT
    toStartOfMinute(event_time) AS minute,
    count() AS events,
    uniqExact(user_id) AS users,
    quantile(0.99)(latency_ms) AS p99_latency
FROM events
WHERE event_time >= now() - INTERVAL 1 HOUR
GROUP BY minute
ORDER BY minute DESC

High-performance log analytics

• Application logs at scale
• Observability data
• Clickstream analytics

Cost-optimised workloads

• Large data volumes
• High query frequency
• Predictable pricing needs

Multi-cloud requirements

• Avoid AWS lock-in
• Hybrid deployments
• Cloud-agnostic strategy

Choose Redshift When:

Deep AWS ecosystem integration

-- Leverage Redshift ML
CREATE MODEL customer_churn
FROM (
    SELECT customer_features.*, churned
    FROM customer_features
)
TARGET churned
FUNCTION predict_churn
IAM_ROLE 'arn:aws:iam::123456789:role/RedshiftML';

Enterprise data warehousing

• Complex ETL workflows
• Multiple data sources
• Mature governance needs

Data sharing across AWS accounts

-- Create datashare for cross-account access
CREATE DATASHARE sales_share;
ALTER DATASHARE sales_share ADD SCHEMA public;
GRANT USAGE ON DATASHARE sales_share TO ACCOUNT '123456789012';

Mixed workloads with WLM

• Different query priorities
• Resource isolation
• SLA management

Migration Strategies

Redshift to ClickHouse

Consider migrating when:

• Query performance is critical
• Costs are exceeding budget
• Multi-cloud flexibility needed

Migration approach:

-- Export from Redshift to S3
UNLOAD ('SELECT * FROM analytics_table')
TO 's3://bucket/export/'
IAM_ROLE 'arn:aws:iam::123456789:role/RedshiftS3'
PARQUET;

-- Import to ClickHouse
INSERT INTO analytics_table
SELECT * FROM s3(
    'https://bucket.s3.amazonaws.com/export/*.parquet',
    'Parquet'
);

ClickHouse to Redshift

Consider migrating when:

• Deep AWS integration needed
• Redshift ML capabilities required
• Enterprise governance requirements

Operational Comparison

ClickHouse Operations

Self-managed:

• Full control
• Operational overhead
• Cost efficient at scale

ClickHouse Cloud:

• Managed service
• Multi-cloud
• Simpler operations

Redshift Operations

Provisioned:

• Traditional cluster management
• Predictable performance
• Manual scaling

Serverless:

• Automatic scaling
• Pay-per-use
• Simplified operations

Query Language Differences

ClickHouse SQL

-- ClickHouse-specific syntax
SELECT
    toStartOfHour(timestamp) AS hour,
    countIf(status = 'error') AS errors,
    quantileTDigest(0.95)(latency) AS p95,
    arrayJoin(tags) AS tag
FROM events
WHERE timestamp >= now() - INTERVAL 24 HOUR
GROUP BY hour
HAVING errors > 10

Redshift SQL (PostgreSQL-compatible)

-- PostgreSQL-compatible syntax
SELECT
    DATE_TRUNC('hour', timestamp) AS hour,
    COUNT(*) FILTER (WHERE status = 'error') AS errors,
    PERCENTILE_CONT(0.95) WITHIN GROUP (ORDER BY latency) AS p95,
    tag
FROM events, UNNEST(tags) AS t(tag)
WHERE timestamp >= CURRENT_TIMESTAMP - INTERVAL '24 hours'
GROUP BY hour, tag
HAVING COUNT(*) FILTER (WHERE status = 'error') > 10

Performance Optimisation

ClickHouse Optimisation

-- Optimise table engine and sort key
CREATE TABLE events (
    timestamp DateTime64(3),
    user_id UInt64,
    event_type LowCardinality(String),
    properties String
) ENGINE = MergeTree()
PARTITION BY toYYYYMM(timestamp)
ORDER BY (event_type, timestamp, user_id)

Redshift Optimisation

-- Optimise distribution and sort keys
CREATE TABLE events (
    timestamp TIMESTAMP,
    user_id BIGINT,
    event_type VARCHAR(100),
    properties VARCHAR(65535)
)
DISTKEY(user_id)
SORTKEY(timestamp);

-- Analyse for query optimisation
ANALYZE events;

Conclusion

ClickHouse and Redshift serve different needs in the AWS analytics landscape:

Choose ClickHouse for maximum query performance, cost efficiency, real-time analytics, and when you need multi-cloud flexibility or want to avoid AWS lock-in.

Choose Redshift when deep AWS ecosystem integration is critical, you need Redshift ML, data sharing across AWS accounts, or enterprise features like WLM and mature governance.

Consider your ecosystem: If you’re heavily invested in AWS with services like Kinesis, QuickSight, and SageMaker, Redshift provides seamless integration. If performance and cost are priorities, ClickHouse delivers more value.

For help architecting your AWS analytics infrastructure, contact our team to discuss your requirements.

Related Resources

• How Tasrie IT Services Uses ClickHouse
• ClickHouse vs Snowflake 2026
• ClickHouse vs BigQuery 2026
• AWS Cloud Cost Optimisation Guide
• EKS Architecture Best Practices

External Resources:

• ClickHouse Documentation
• Amazon Redshift Documentation
• Redshift Pricing