ClickHouse vs Redshift 2026: Cost & Performance
ClickHouse is a columnar OLAP database optimized for sub-second queries on massive datasets with lower operational costs, while Redshift is AWS's managed data warehouse solution offering seamless AWS integration and enterprise support at higher pricing. ClickHouse excels in analytical performance per dollar; Redshift wins for organizations already invested in AWS infrastructure.
ClickHouse
High-performance open-source columnar OLAP database optimized for analytical queries on massive datasets
Data-intensive organizations, analytics startups, companies processing 100B+ events daily who can invest in DevOps
Amazon Redshift
AWS's fully managed data warehouse service with native cloud integration and enterprise-grade reliability
Enterprise organizations with AWS commitment, teams prioritizing operational simplicity over cost, companies needing 24/7 support SLAs
Quick Answer
AI SummaryClickHouse is a columnar OLAP database optimized for sub-second queries on massive datasets with lower operational costs, while Redshift is AWS's managed data warehouse solution offering seamless AWS integration and enterprise support at higher pricing. ClickHouse excels in analytical performance per dollar; Redshift wins for organizations already invested in AWS infrastructure.
Our Verdict
AI-assistedChoose ClickHouse if you need maximum analytical query performance, operate at scale (100B+ rows), require cost-efficiency, and can manage infrastructure or use a specialized managed provider. Choose Redshift if you're already in the AWS ecosystem, need enterprise-grade SLAs, prefer turnkey deployment, and can justify the premium pricing for operational simplicity.
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Choose ClickHouse if
Best pickData-intensive organizations, analytics startups, companies processing 100B+ events daily who can invest in DevOps
Choose Amazon Redshift if
Enterprise organizations with AWS commitment, teams prioritizing operational simplicity over cost, companies needing 24/7 support SLAs
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Key Differences at a Glance
- Query Performance on 1B+ Row Datasets:✓ ClickHouse wins(50-100ms (typical) vs 200-500ms (typical))
- Cost per TB/Month (On-Demand):✓ ClickHouse wins($0.06-$0.12 vs $1.50-$3.00)
- Deployment Model:Self-managed or managed service vs Fully managed (AWS only)
Key Facts & Figures
76 numeric metrics compared
| Metric | ClickHouse | Amazon Redshift | Ratio |
|---|---|---|---|
| Query Latency (1 billion rows)(seconds) | 1.2 seconds | — | — |
| Monthly Cost (100 GB compressed)(USD) | $150 | — | — |
| Ingestion Throughput(events/sec) | 1,000,000 events/sec | — | — |
| Compression Ratio(ratio) | 8:1-12:1 | — | — |
| Learning Curve (1-10 Scale)(scale) | 7/10 (moderate-hard) | — | — |
| Query Latency (1GB aggregation)(milliseconds) | 500-2000ms | — | — |
| Compression Ratio (typical)(ratio) | 10:1 to 40:1 | — | — |
| Memory Required (minimal)(MB) | 500-2000MB | — | — |
| Ingest Throughput(million rows/second) | 1-5 million rows/sec | — | — |
| Setup Time to First Query(minutes) | 30-120 minutes | — | — |
| SQL Standard Compliance(percent) | 70% ANSI SQL | 95% (PostgreSQL-compatible) | |
| Query Latency (p99)(milliseconds) | 50-200ms (historical) | 8000ms | |
| Ingestion Latency (end-to-end)(milliseconds) | 1000-10000ms | — | — |
| Memory Usage per Query(MB) | 50-200MB | — | — |
| Maximum Cluster Size(petabytes) | 1000+ | Petabyte scale | |
| Typical Cost per TB/year(USD) | $800-1500 | — | — |
| Ingestion Latency(seconds) | 10-60 seconds | — | — |
| Query Latency (100M rows)(milliseconds) | 50-500ms | — | — |
| Data Compression Ratio(ratio) | 15:1 | 7.5:1 | |
| Maximum Cluster Nodes(nodes) | 1000+ nodes tested | — | — |
| GitHub Stars (2026)(stars) | 34,000+ | — | — |
| Typical Maximum Dataset Size(GB) | ~1,000,000+ GB (1+ PB) | — | — |
| Idle Memory Usage(MB) | 500-2000 MB | — | — |
| Supported Data Formats(formats) | 12+ formats (TSV, Native, Avro, Protobuf, etc.) | — | — |
| Query Latency (100M rows, simple aggregation)(milliseconds) | 500-1500ms | — | — |
| Typical Storage Cost(USD per TB per month) | $20-40 | — | — |
| Max Recommended Dataset Size(terabytes) | 100TB+ efficiently | — | — |
| SQL Feature Completeness(percentage) | 95% (PostgreSQL-compatible) | — | — |
| Max Ingestion Throughput(events/second) | 100,000-500,000 events/sec | — | — |
| Storage Cost per TB/Month(USD) | $50-150 | — | — |
| Typical Node Memory(GB) | 8-32GB | — | — |
| Minimum Recommended Cluster Size(nodes) | 3-5 nodes | — | — |
| Max Dataset Size (Practical)(TB) | 1000TB+ (unlimited with tiering) | — | — |
| Query Latency (1B row scan, 10 column aggregate)(milliseconds) | 50-100ms | 500-2000ms | |
| Storage Cost (per TB/month)(USD) | $15-25 | Included in node cost | |
| Typical Data Compression Ratio(x) | 10-40x | 4-8x | |
| Minimum Cluster Size (nodes)(nodes) | 1 (can run standalone) | 2 (minimum production) | |
| Max Concurrent Queries (default config)(queries) | Unlimited (resource-based) | 32 (base, expandable via Concurrency Scaling) | — |
| Data Ingestion Latency(milliseconds) | Microseconds to milliseconds | Batch (minutes to hours typical) | |
| AWS Service Integration (native)(count) | 5-10 (via third-party) | 20+ (native connectors) | |
| GitHub Stars (as of 2026)(count) | 25000+ | Not open-source (proprietary) | — |
| Query Latency (1 billion rows, simple SELECT)(milliseconds) | 150ms | — | — |
| Cost per GB Scanned(USD) | $0.015 | — | — |
| Maximum Ingestion Rate(rows per second) | 1,000,000 | — | — |
| Infrastructure Management Overhead(hours per month) | 40-80 hours | — | — |
| Minimum Monthly Cost (basic setup)(USD) | $500 (ClickHouse Cloud starter) | — | — |
| Cloud Provider Support(count) | 4+ (AWS, Azure, GCP, on-premise) | — | — |
| Automatic Scaling Time(seconds) | 60-300 (manual cluster resize required) | — | — |
| Average Query Latency (1 Billion Row Scan)(ms) | 75ms | 350ms | |
| Monthly Cost per TB Stored(USD) | $0.09 | $2.25 | |
| Time to Production Deployment(minutes) | 1440 (self-managed) / 60 (managed) | 20 | |
| Maximum Concurrent Queries(queries/sec) | 100,000+ | 10,000+ | |
| Uptime SLA Guarantee(%) | 99.0% (self-managed) / 99.95% (managed) | 99.99% | |
| Native AWS Service Integration(count) | 3 (S3, Kinesis via 3rd party, basic) | 12+ (S3, Glue, Lambda, Athena, QuickSight, IAM, EventBridge, etc.) | |
| Data Ingestion Rate(GB/sec) | 1-5 | 0.5-2 | |
| Typical Query Cost (per TB scanned)(USD) | $1.50-$5.00 | $1.50-$5.00 | |
| Setup Time (to production)(days) | 1-3 | 1-3 | |
| Typical Memory Per Node(GB) | 160-256 | 160-256 | |
| Query Latency (Average)(milliseconds) | 1-10 seconds | 1-10 seconds | |
| Data Freshness(seconds) | Minutes to hours typical | Minutes to hours typical | |
| Concurrent User Support(users) | 2,000+ | 2,000+ | |
| Ingestion Streaming Support(events per second) | Limited via Kinesis/S3 batch | Limited via Kinesis/S3 batch | |
| Base Monthly Cost (Small Cluster)(USD) | $2,160-8,640 (dc2.large, 2-4 nodes) | $2,160-8,640 (dc2.large, 2-4 nodes) | |
| Base Hourly Cost (2-node cluster)(USD/hour) | $0.50 (DC2.large) | $0.50 (DC2.large) | |
| Query Performance (TPC-DS 100GB)(seconds) | ~16 seconds | ~16 seconds | |
| Scaling Adjustment Time(minutes) | 10-15 (requires cluster resize + restart) | 10-15 (requires cluster resize + restart) | |
| Maximum Single Query Data Scanned(petabytes) | 100+ | 100+ | |
| Cloud Providers Supported(count) | 1 (AWS only) | 1 (AWS only) | |
| Annual Contract Discount(percent) | Up to 30% | Up to 30% | |
| Configuration Tuning Required(hours (estimated)) | 40-80 (distribution keys, sort keys, vacuum) | 40-80 (distribution keys, sort keys, vacuum) | |
| Annual TCO (100TB storage, average usage)(USD) | $200,000 | $200,000 | |
| TPC-DS Query Benchmark (100GB dataset)(seconds) | 42 | 42 | |
| Setup Time to Production(hours) | 40-60 hours | 40-60 hours | |
| Maximum Concurrent Users(users) | 50 (standard) | 50 (standard) | |
| Data Marketplace Size(datasets) | ~200 (limited) | ~200 (limited) | |
| Reserved Instance Discount(percent) | 70% | 70% |
Sourced from publicly available data ·
Key Differences
7 attributes compared head-to-head
- 50-100ms (typical)(winner)Query Performance on 1B+ Row Datasets200-500ms (typical)
- $0.06-$0.12(winner)Cost per TB/Month (On-Demand)$1.50-$3.00
- Self-managed or managed serviceDeployment ModelFully managed (AWS only)
- Limited (third-party tools required)AWS Ecosystem IntegrationNative (Glue, S3, Lambda, QuickSight)(winner)
- 2-4 weeks (self-managed) or 1 day (managed)Setup Time for Production Cluster15-30 minutes(winner)
- 10:1 to 20:1(winner)Compression Ratio Typical5:1 to 10:1
- Community-driven or 3rd-party vendorsEnterprise SLA & Support24/7 AWS support with SLA guarantee(winner)
- Query Performance on 1B+ Row Datasets
ClickHouse
50-100ms (typical)(winner)
Amazon Redshift
200-500ms (typical)
- Cost per TB/Month (On-Demand)
ClickHouse
$0.06-$0.12(winner)
Amazon Redshift
$1.50-$3.00
- Deployment Model
ClickHouse
Self-managed or managed service
Amazon Redshift
Fully managed (AWS only)
- AWS Ecosystem Integration
ClickHouse
Limited (third-party tools required)
Amazon Redshift
Native (Glue, S3, Lambda, QuickSight)(winner)
- Setup Time for Production Cluster
ClickHouse
2-4 weeks (self-managed) or 1 day (managed)
Amazon Redshift
15-30 minutes(winner)
- Compression Ratio Typical
ClickHouse
10:1 to 20:1(winner)
Amazon Redshift
5:1 to 10:1
- Enterprise SLA & Support
ClickHouse
Community-driven or 3rd-party vendors
Amazon Redshift
24/7 AWS support with SLA guarantee(winner)
Full Comparison
| Attribute | ClickHouse | Amazon Redshift |
|---|---|---|
| Query Latency (1 billion rows)(seconds) | 1.2 seconds | — |
| Ingestion Throughput(events/sec) | 1,000,000 events/sec | — |
| Query Latency (1GB aggregation)(milliseconds) | 500-2000ms | — |
| Ingest Throughput(million rows/second) | 1-5 million rows/sec | — |
| Query Latency (p99)(milliseconds) | 50-200ms (historical)(winner) | 8000ms |
Show 13 more attributesIngestion Latency(seconds) 10-60 seconds — Query Latency (100M rows)(milliseconds) 50-500ms — Query Latency (100M rows, simple aggregation)(milliseconds) 500-1500ms — Max Ingestion Throughput(events/second) 100,000-500,000 events/sec — Query Latency (1B row scan, 10 column aggregate)(milliseconds) 50-100ms 500-2000ms Query Latency (1 billion rows, simple SELECT)(milliseconds) 150ms — Maximum Ingestion Rate(rows per second) 1,000,000 — Automatic Scaling Time(seconds) 60-300 (manual cluster resize required) — Average Query Latency (1 Billion Row Scan)(ms) 75ms 350ms Data Ingestion Rate(GB/sec) 1-5 0.5-2 Query Latency (Average)(milliseconds) 1-10 seconds — Query Performance (TPC-DS 100GB)(seconds) ~16 seconds — TPC-DS Query Benchmark (100GB dataset)(seconds) 42 — | ||
| Monthly Cost (100 GB compressed)(USD) | $150 | — |
| Storage Cost (per TB/month)(USD) | $15-25 | Included in node cost(winner) |
| Cost per GB Scanned(USD) | $0.015 | — |
| Minimum Monthly Cost (basic setup)(USD) | $500 (ClickHouse Cloud starter) | — |
| Base Hourly Cost (2-node cluster)(USD/hour) | $0.50 (DC2.large) | — |
Show 2 more attributesAnnual Contract Discount(percent) Up to 30% — Reserved Instance Discount(percent) 70% — | ||
| Setup Time(minutes) | 240 minutes | — |
| Setup Time to First Query(minutes) | 30-120 minutes | — |
| Data Retention for Time-Travel(days) | Not native | — |
| Streaming Integration | Limited (Kafka via TableEngine) | — |
| Transaction Support(consistency level) | No ACID (eventual consistency) | — |
| SQL Feature Completeness(percentage) | 95% (PostgreSQL-compatible) | — |
| Time-Series Aggregation Support(native features) | Standard SQL; requires manual time bucketing | — |
Show 1 more attributeSQL Compatibility(percentage) MySQL-compatible with ClickHouse extensions — | ||
| Compression Ratio(ratio) | 8:1-12:1 | — |
| Licensing Model | Open-source (free) + optional support | — |
| Typical Cost per TB/year(USD) | $800-1500 | — |
| Learning Curve (1-10 Scale)(scale) | 7/10 (moderate-hard) | — |
| Compression Ratio (typical)(ratio) | 10:1 to 40:1 | — |
| Memory Usage per Query(MB) | 50-200MB | — |
| Memory Required (minimal)(MB) | 500-2000MB | — |
| SQL Standard Compliance(percent) | 70% ANSI SQL | 95% (PostgreSQL-compatible)(winner) |
| Supported Data Formats(formats) | 12+ formats (TSV, Native, Avro, Protobuf, etc.) | — |
| Ingestion Latency (end-to-end)(milliseconds) | 1000-10000ms | — |
| Data Ingestion Latency(milliseconds) | Microseconds to milliseconds(winner) | Batch (minutes to hours typical) |
| Maximum Cluster Size(petabytes) | 1000+ | Petabyte scale(winner) |
| Maximum Cluster Nodes(nodes) | 1000+ nodes tested | — |
| Typical Maximum Dataset Size(GB) | ~1,000,000+ GB (1+ PB) | — |
| Max Recommended Dataset Size(terabytes) | 100TB+ efficiently | — |
| Max Dataset Size (Practical)(TB) | 1000TB+ (unlimited with tiering) | — |
Show 2 more attributesMaximum Concurrent Queries(queries/sec) 100,000+ 10,000+ Maximum Concurrent Users(users) 50 (standard) — | ||
| Native SQL Support | Standard SQL with extensions | — |
| Multi-tenancy Isolation | Limited/requires custom logic | — |
| Compute-Storage Decoupling | Coupled (scale together) | — |
| Data Compression Ratio(ratio) | 15:1(winner) | 7.5:1 |
| Typical Data Compression Ratio(x) | 10-40x(winner) | 4-8x |
| GitHub Stars (2026)(stars) | 34,000+ | — |
| GitHub Stars (as of 2026)(count) | 25000+ | Not open-source (proprietary) |
| Idle Memory Usage(MB) | 500-2000 MB | — |
| Typical Memory Per Node(GB) | 160-256 | — |
| Typical Storage Cost(USD per TB per month) | $20-40 | — |
| Storage Cost per TB/Month(USD) | $50-150 | — |
| Monthly Cost per TB Stored(USD) | $0.09(winner) | $2.25 |
| Typical Query Cost (per TB scanned)(USD) | $1.50-$5.00 | — |
| Base Monthly Cost (Small Cluster)(USD) | $2,160-8,640 (dc2.large, 2-4 nodes) | — |
Show 1 more attributeAnnual TCO (100TB storage, average usage)(USD) $200,000 — | ||
| Typical Node Memory(GB) | 8-32GB | — |
| Minimum Cluster Size (nodes)(nodes) | 1 (can run standalone)(winner) | 2 (minimum production) |
| Deployment Flexibility | AWS only | — |
| Cloud Platform Support | AWS only | — |
| Minimum Recommended Cluster Size(nodes) | 3-5 nodes | — |
| Cloud Provider Support(count) | 4+ (AWS, Azure, GCP, on-premise) | — |
| Cloud Providers Supported(count) | 1 (AWS only) | — |
| Max Concurrent Queries (default config)(queries) | Unlimited (resource-based) | 32 (base, expandable via Concurrency Scaling) |
| AWS Service Integration (native)(count) | 5-10 (via third-party) | 20+ (native connectors)(winner) |
| Data Marketplace Size(datasets) | ~200 (limited) | — |
| Infrastructure Management Overhead(hours per month) | 40-80 hours | — |
| Time to Production Deployment(minutes) | 1440 (self-managed) / 60 (managed) | 20(winner) |
| Setup Time (to production)(days) | 1-3 | — |
| Scaling Adjustment Time(minutes) | 10-15 (requires cluster resize + restart) | — |
| Configuration Tuning Required(hours (estimated)) | 40-80 (distribution keys, sort keys, vacuum) | — |
| Uptime SLA Guarantee(%) | 99.0% (self-managed) / 99.95% (managed) | 99.99%(winner) |
| Native AWS Service Integration(count) | 3 (S3, Kinesis via 3rd party, basic) | 12+ (S3, Glue, Lambda, Athena, QuickSight, IAM, EventBridge, etc.)(winner) |
| Supported Data Retention(duration) | Unlimited historical storage | — |
| Data Freshness(seconds) | Minutes to hours typical | — |
| Ingestion Streaming Support(events per second) | Limited via Kinesis/S3 batch | — |
| Concurrent User Support(users) | 2,000+ | — |
| License Type | AWS Proprietary Managed Service | — |
| Maximum Single Query Data Scanned(petabytes) | 100+ | — |
| Setup Time to Production(hours) | 40-60 hours | — |
ClickHouseShow 13 more attributes
Show 2 more attributes
Show 1 more attribute
Show 2 more attributes
Show 1 more attribute
Pros & Cons
10 pros·6 cons across both
ClickHouse
Pros
- Sub-second query latency on billion+ row datasets (50-100ms typical)
- 10-20x better storage compression than traditional warehouses (10:1 to 20:1 ratio)
- 15-25x lower cost per TB/month ($0.06-$0.12 vs $1.50-$3.00 for Redshift)
- Handles 100K+ queries per second with minimal CPU overhead
- Supports real-time data ingestion with automatic data deduplication
Cons
- Requires deep technical expertise for self-managed deployment and optimization
- Limited native integrations with BI tools (requires connectors for Tableau, Looker, QuickSight)
- Smaller ecosystem and fewer managed service providers vs AWS Redshift
Amazon Redshift
Pros
- Fully managed with 99.99% uptime SLA and 24/7 enterprise support
- Native integration with AWS services (S3, Glue, Lambda, QuickSight, IAM)
- Deployment and schema setup in under 30 minutes, zero DevOps overhead
- Concurrency scaling handles 10K+ concurrent users without performance degradation
- Automated backup, patching, and disaster recovery built-in
Cons
- Cost is 15-25x higher per TB than ClickHouse ($1.50-$3.00/TB/month for on-demand)
- Query performance on massive datasets (1B+ rows) is 4-10x slower than ClickHouse (200-500ms vs 50-100ms)
- Vendor lock-in to AWS ecosystem; challenging to migrate data out
Frequently Asked Questions
5 questions
ClickHouse is dramatically cheaper—approximately 15-25x lower cost per TB stored ($0.06-$0.12/TB/month vs $1.50-$3.00). However, Redshift has no infrastructure management costs, while self-managed ClickHouse requires DevOps expertise. For a 100TB warehouse, Redshift costs ~$15,000/month vs ClickHouse at ~$900/month (self-managed) or ~$3,000/month with a managed provider.
Resources & Learn More
Curated sources to dive deeper
Where to Buy
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Wikipedia
- W
ClickHouse on Wikipedia (opens in new tab)
High-performance open-source columnar OLAP database optimized for analytical queries on massive datasets
- W
Amazon Redshift on Wikipedia (opens in new tab)
AWS's fully managed data warehouse service with native cloud integration and enterprise-grade reliability
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