ClickHouse is a self-hosted columnar database optimized for analytical queries with lower operational costs at scale, while BigQuery is a fully managed cloud data warehouse with zero infrastructure overhead and integrated ecosystem. ClickHouse excels at sub-second query speeds on massive datasets; BigQuery prioritizes ease of use and seamless Google Cloud integration.
ClickHouse vs BigQuery — Side-by-side comparison on A Versus B
Open-source columnar OLAP database optimized for analytical queries with extreme performance at massive scale.
Fully managed cloud data warehouse offering serverless SQL analytics with integrated ML and BI tools.
Deployment Model. Query Performance (1TB dataset). Cost per TB scanned. Setup Time. Data Compression Ratio. Maximum query size. Native ML Integration
Choose ClickHouse if you operate at petabyte scale, need sub-second query latency, have deep data engineering expertise, and want to minimize per-query costs—it's ideal for real-time analytics and time-series data. Choose BigQuery if you prioritize fast time-to-value, need integrated ML/BI tools, prefer zero infrastructure management, and operate at terabyte-to-low-petabyte scale with predictable workloads.
ClickHouse: 1,000,000 events/sec | BigQuery: N/A
ClickHouse: Open-source (free) + optional support | BigQuery: N/A
ClickHouse: 7/10 (moderate-hard) | BigQuery: N/A
ClickHouse: 10:1 to 40:1 | BigQuery: N/A
ClickHouse: 1-5 million rows/sec | BigQuery: N/A
ClickHouse: 30-120 minutes | BigQuery: 10-15 minutes
ClickHouse: 70% ANSI SQL | BigQuery: N/A
ClickHouse: 50-200ms (historical) | BigQuery: N/A
ClickHouse: 1000-10000ms | BigQuery: N/A
ClickHouse: Standard SQL with extensions | BigQuery: N/A
ClickHouse: Limited/requires custom logic | BigQuery: N/A
ClickHouse: 10-60 seconds | BigQuery: N/A
ClickHouse: 12:1 average | BigQuery: 5:1 average
ClickHouse: 1000+ nodes tested | BigQuery: N/A
ClickHouse: Limited (Kafka via TableEngine) | BigQuery: N/A
ClickHouse: No ACID (eventual consistency) | BigQuery: N/A
ClickHouse: ~1,000,000+ GB (1+ PB) | BigQuery: N/A
ClickHouse: 12+ formats (TSV, Native, Avro, Protobuf, etc.) | BigQuery: N/A
Query Latency (100M rows, simple aggregation)
ClickHouse: 100TB+ efficiently | BigQuery: N/A
ClickHouse: 95% (PostgreSQL-compatible) | BigQuery: N/A
ClickHouse: Standard SQL; requires manual time bucketing | BigQuery: N/A
ClickHouse: 100,000-500,000 events/sec | BigQuery: N/A
ClickHouse: 1000TB+ (unlimited with tiering) | BigQuery: N/A
Query Latency (1B row scan, 10 column aggregate)
ClickHouse: 1 (can run standalone) | BigQuery: N/A
ClickHouse: Unlimited (resource-based) | BigQuery: N/A
ClickHouse: Microseconds to milliseconds | BigQuery: N/A
ClickHouse: 5-10 (via third-party) | BigQuery: N/A
Query Latency (1 billion rows, simple SELECT)
ClickHouse: 40-80 hours | BigQuery: 0-5 hours
ClickHouse: $500 (ClickHouse Cloud starter) | BigQuery: $0 (pay-per-query, ~$100/month typical)
ClickHouse: 4+ (AWS, Azure, GCP, on-premise) | BigQuery: 1 (Google Cloud only)
ClickHouse: 60-300 (manual cluster resize required) | BigQuery: 2-5 (automatic)
ClickHouse: MySQL-compatible with ClickHouse extensions | BigQuery: ANSI SQL-2011 standard
Average Query Latency (1 Billion Row Scan)
ClickHouse: 1440 (self-managed) / 60 (managed) | BigQuery: N/A
ClickHouse: 99.0% (self-managed) / 99.95% (managed) | BigQuery: N/A
ClickHouse: 3 (S3, Kinesis via 3rd party, basic) | BigQuery: N/A
ClickHouse: 14 days | BigQuery: 0.25 days
ClickHouse: 1,000+ | BigQuery: Unlimited (auto-scaling)
ClickHouse: No (third-party integration required) | BigQuery: Yes (BigQuery ML)
ClickHouse: Native optimization, ideal for billions of events | BigQuery: Supported but not optimized
ClickHouse: 99.5% (self-hosted dependent) | BigQuery: 99.99%
ClickHouse: N/A | BigQuery: 1 (GCP only)
ClickHouse: N/A | BigQuery: 5-10 minutes
ClickHouse: N/A | BigQuery: ~200 datasets
ClickHouse: N/A | BigQuery: Unlimited with auto-scaling
Total Cost of Ownership (Annual, 100TB dataset)
ClickHouse: N/A | BigQuery: $78,000-$156,000
Query Latency (10GB dataset, simple aggregate)
Query Latency (1TB dataset, complex join)
ClickHouse: N/A | BigQuery: 1000+ TB (unlimited)
ClickHouse: N/A | BigQuery: 1000+ simultaneous
ClickHouse: N/A | BigQuery: Yes (BigQuery ML with 15+ model types)
ClickHouse: N/A | BigQuery: N/A (Google proprietary)
ClickHouse: N/A | BigQuery: Structured (relational tables)
ClickHouse: N/A | BigQuery: 1 (Google Cloud only)
ClickHouse: N/A | BigQuery: Fully serverless, automatic scaling
ClickHouse: N/A | BigQuery: BigQuery ML (SQL-based only)
Minimum Learning Curve (months for competency)
Starting Monthly Cost (1 TB storage + compute)
ClickHouse: N/A | BigQuery: $25-150 (on-demand queries)
ClickHouse: N/A | BigQuery: No (append-only snapshots)
How to choose between ClickHouse and BigQuery
ClickHouse vs BigQuery — Structured Comparison Data
Machine-readable comparison dataset for ClickHouse vs BigQuery. Contains 94 attribute comparisons, 5 FAQ pairs.
ClickHouse vs BigQuery — Attribute Category Vocabulary
Taxonomy of attribute categories used to classify comparison data for ClickHouse vs BigQuery.
ClickHouse is significantly faster—85ms average latency vs BigQuery's 3,200ms on 1TB datasets. This 37x speed difference grows at petabyte scale due to ClickHouse's columnar compression and local query processing. BigQuery's slower speed reflects network overhead and distributed query planning, which trades latency for simplicity.
ClickHouse costs $0.01 per TB scanned vs BigQuery's $6.25 per TB—a 625x cost advantage at scale. For a company scanning 100 TB monthly, this equals $1,200/month for ClickHouse vs $750,000/month for BigQuery. However, ClickHouse requires 2-4 engineers at $200K+ annually to operate, making BigQuery cheaper for organizations with <10TB monthly scans.
Can I switch from BigQuery to ClickHouse later?
Partial switching is possible: you can export BigQuery data to Cloud Storage ($0.02/GB) and import into ClickHouse, but SQL query rewrites may be needed (ClickHouse uses slightly different SQL dialect). BigQuery's native integrations (Looker, Data Studio) require reimplementation. Plan for 4-8 weeks of migration effort for established systems.
BigQuery ML is superior for non-data-engineers: create models in 5-10 lines of SQL with automatic hyperparameter tuning, feature engineering, and model serving. ClickHouse requires exporting data to Python/R for ML, adding latency and operational complexity. However, ClickHouse integrates with tools like LightGBM and CatBoost for more control.
How do they handle real-time data ingestion?
ClickHouse excels at real-time: sub-second inserts for millions of rows/second with immediate query availability (no refresh lag). BigQuery has 90-second streaming lag by default (paid option reduces to 15 seconds) and batch ingestion is standard. ClickHouse is ideal for live dashboards; BigQuery suits near-real-time reporting.
How to Choose Between ClickHouse and BigQuery
Deployment Model: ClickHouse has Self-hosted or managed service, while BigQuery has Fully managed SaaS only.
Consider: Query Performance (1TB dataset)
Query Performance (1TB dataset): ClickHouse has 50-200ms average, while BigQuery has 2-8 seconds average.
Cost per TB scanned: ClickHouse has $0.00-0.02, while BigQuery has $6.25.
Setup Time: ClickHouse has 2-4 weeks, while BigQuery has 5-15 minutes.
Data Compression Ratio: ClickHouse has 8:1 to 20:1, while BigQuery has 3:1 to 8:1.
Maximum query size: ClickHouse has Unlimited (disk-based), while BigQuery has 1MB request limit.
Native ML Integration: ClickHouse has Limited (third-party), while BigQuery has BigQuery ML built-in.