Comparisons

ClickHouse vs BigQuery

BigQuery, limited to GCP, handles ad-hoc queries and complex, long-running analysis effectively, but scaling introduces major challenges in both cost and performance management. A per-query pricing model drives up expenses as usage grows, penalizing expansion.

In contrast, ClickHouse is deployable on any cloud and delivers stable, resource-based pricing with high concurrency and dynamic scaling - ideal for interactive, user-facing workloads without surprise bills. Read more below to see how ClickHouse and BigQuery compare across cost, performance, and supported features.

Why ClickHouse is better:

21x

Reduction in costs

Faster queries

60%

Better compression

Read our comprehensive guide about migrating from BigQuery to ClickHouse.

ClickHouse compared to BigQuery

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BigQuery’s query latency

BigQuery often struggles with sub-second queries due to baseline latency on uncached results.

ClickHouse, built for real-time analytics at scale, delivers the fastest and most resource-efficient performance - consistently serving queries in under a second.

Whether you’re aggregating large volumes of data in real-time, interactively slicing and dicing on the fly, or powering customer-facing dashboards, ClickHouse ensures blazing speed.

We needed a solution that could scale, but also provide end-user facing analytics capabilities with low latency and high throughput. Read blog

BigQuery’s high cost

BigQuery’s per-query pricing and streaming insert fees often limit usage, reduce ROI, and penalize frequent ingestion. ClickHouse Cloud avoids these trade-offs with fixed pricing, no per-query or insert costs, and best-in-class resource efficiency - delivering maximum cost-effectiveness at scale.

ClickHouse solves most of our problems very efficiently at a small fraction of the price in terms of infrastructure. This is a far better advantage for us in our books

We simply don’t want the hassle of trying to figure out in advance of how many BigQuery slots to purchase - what a headache! Read blog

BigQuery’s query concurrency

BigQuery caps concurrency based on compute availability, queuing or rejecting queries while charging per query - making high-concurrency workloads costly and unpredictable.

ClickHouse takes the opposite approach: reserve compute once, pay a fixed price, and run 1,000+ queries per node. Need more capacity? Simply add nodes. Costs stay predictable, and scalability comes without slot management or complex tuning.

Another issue was BigQuery's limit of 100 concurrent queries, which created bottlenecks for Gumlet's customers. "If our customers needed to fire more analytics API requests than that, they would fail or go into a queue.
Read blog

Explore why users are migrating from BigQuery to ClickHouse.

Tired of unpredictable costs?
Need milliseconds when queries take seconds?
Want predictable, high concurrency without the headaches?

ClickHouse Cloud gives users precise scaling control with workload quotas, vertical and horizontal scaling (manual or automatic), custom hardware profiles, and fast resume from idle. Users benefit from ClickHouse being open source, making it ideal for hybrid and multi-cloud deployments.

BigQuery, by contrast, shines at elastic scaling for large batch jobs through dynamic slot allocation. But this limits transparency and control: concurrency depends on slot reservations, there’s no vertical tuning or custom hardware choice, and caching mainly aids data skipping. It works well for high-throughput batch workloads, but less so for use cases demanding consistent concurrency and predictable performance.

ClickHouse provides fine-grained scaling control with workload quotas, vertical and horizontal scaling (manual or automatic), custom hardware profiles, and rapid resume from idle. It supports 1,000+ concurrent queries per node with predicate-level cache reuse - ideal for high-concurrency, low-latency analytics.

BigQuery scales elastically for large batch jobs by dynamically allocating slots, but with less transparency and control. Concurrency depends on slot reservations, there’s no vertical tuning or custom hardware choice, and caching mainly aids data skipping. This suits high-throughput batch workloads, but not workloads needing consistent concurrency and predictable performance.

ClickHouse delivers sub-second latency on multi-billion-row datasets with vectorized execution, native aggregates, and dictionary acceleration. Efficient compression reduces I/O, while flexible materialized views and granular cache controls keep hot data instantly accessible for demanding workloads.

BigQuery simplifies infrastructure with a serverless model but depends on reserved slots for concurrency and upfront commitments at scale. It supports large joins and vectorized execution, yet typical latencies are 1–2 seconds. Materialized views have SQL limits, and caching is coarse - skipping broad segments rather than fine predicate-levels - making it better for batch and moderately interactive analytics.

ClickHouse shines at real-time, high-frequency workloads, powering dashboards, product analytics, and customer apps with sub-second responses. Native support for logs, metrics, and traces (via ClickStack) makes it a strong fit for observability Optimizations like projections, dictionaries, and fine-grained caching also make it ideal for GenAI agentic and vector workloads needing fast, high-volume queries.

BigQuery fits internal analytics and operational dashboards, with managed scaling for periodic workloads. But low-latency, high-concurrency use cases, like product analytics, observability, or GenAI agents will struggle with baseline query delays and costly continuous ingestion. Basic caching and SQL-limited materialized views further orient it toward batch scenarios.

With fully-managed CDC via ClickPipes, ClickHouse makes it easy to stream changes from operational databases like Postgres, MySQL or Mongo, enabling real-time analytics with minimal lag. Users benefit from unmatched interoperability, with support for over 70 file formats, external catalogs like Hive and Glue, and lakehouse formats such as Iceberg. External table engines let you query systems like Postgres, MongoDB, or S3 directly.

BigQuery emphasizes interoperability with object stores and lake formats, supporting Avro, CSV, JSON, ORC, Parquet, and Iceberg, with in-place queries via BigLake. Catalog integration is limited to AWS Glue, and CDC relies on Datastream pipelines. This makes BigQuery strong for warehouse and lakehouse setups, but with less direct multi-system reach.

Yes
OSS self-managed + ClickHouse Cloud on GCP, AWS & Azure
Yes
Query result cache for interactive queries
Yes
<1s latency for streaming data
Yes
Efficient row-level updates
Yes
1,000+ QPS per node
Yes
Separation of compute and storage
Yes
Stateless compute nodes for fast scaling
Yes
Full control over ordering & co-location
Yes
Partitioning supported
Yes
Parallel replicas distribute workloads
Yes
Native streaming ingestion
Yes
JSON with type fidelity
Yes
Async inserts for small batches
Yes
Seconds
Yes
Includes vertical scaling for high-memory queries
Yes
Custom hardware profiles supported
Yes
Manual resize supported
Yes
Predicate-level cache control (node + distributed)
Yes
Quotas, scaling limits, workload scheduling
Yes
Up to 1,000 concurrent queries per node
Yes
Elastic scaling up/down with load
Yes
Compute/storage separation
Yes
Distributed cache across nodes
Yes
Dictionary acceleration for dimension tables
Yes
Sub-second latency at scale
Yes
Up to 1,000 concurrent queries per node
Yes
Incremental & refreshable with full SQL
Yes
Merge states & projections for fast aggregates
Yes
60% better compression in benchmarks
Yes
Vectorized execution supported
Yes
Efficient joins across billions of rows
Yes
Real-time analytics with low latency
Yes
Native support via ClickStack
Yes
Continuous ingestion at scale
Yes
Low-latency SQL over events
Yes
Internal dashboards supported
Yes
Vector search supported
Yes
70+ formats including Parquet, ORC, Avro, JSON, CSV
Yes
Connect to Postgres, MongoDB, MySQL, S3, Kafka, and more
Yes
Query in-place via table engines (e.g. Postgres, S3)
Yes
Third-party catalogs supported
Yes
ClickPipes CDC for MySQL and Postgres
Yes
Open formats supported

No
GCP only
Intermediate—
Not used with streaming ingest
Intermediate—
~1s latency with extra charges; not compatible with query cache
Intermediate—
Extra charges for changes; recently streamed data can’t be modified
Yes
Depends on slot allocation; queries may queue or be rejected
Yes
Separation of compute and storage
Yes
Stateless compute supported
Yes
Stores data sorted by clustering columns
Yes
Partitioning supported
Yes
Shuffling across compute slots
Yes
Streaming supported (with extra charges)
Yes
JSON supported (but no row-level policies on JSON columns)
Yes
Inserts supported (but invalidate query cache)
No
N/A (no concept of idling)
No
N/A (slots only; no user visibility)
No
Not supported
No
N/A
No
Can only skip
Intermediate—
Requires reserved slots
Intermediate—
Concurrency limited by reserved slots; low without large reservations
Yes
Slots dynamically allocated
Yes
Compute/storage separation
Yes
Distributed cache supported
No
Not supported
Intermediate—
Hard to achieve; minimal latency typically 1–2s
Intermediate—
Concurrency limited by reserved slots; low without large reservations
Intermediate—
Significant SQL limitations
Intermediate—
Limited; only via materialized views
Intermediate—
Standard columnar compression
Yes
Vectorized execution supported
Yes
Joins supported
Intermediate—
Limited by minimum latency and cost
Intermediate—
Charges for data writes make this expensive
Intermediate—
Expensive at scale
Intermediate—
Minimum latency slows agentic responses
Yes
Internal dashboards supported
Yes
Vector search supported
Intermediate—
Limited to common formats (Avro, CSV, JSON, ORC, Parquet)
Intermediate—
Object stores only
Intermediate—
BigLake on object storage only; supports Avro, CSV, Delta Lake, Iceberg, JSON, ORC, Parquet
Intermediate—
AWS Glue only
Yes
Via Datastream
Yes
Open formats supported

Feature comparison of ClickHouse and BigQuery
Featured
Flexible deployment	Yes OSS self-managed + ClickHouse Cloud on GCP, AWS & Azure	No GCP only
Query result cache for sub-second interactivity	Yes Query result cache for interactive queries	Intermediate— Not used with streaming ingest
Real-time ingest	Yes <1s latency for streaming data	Intermediate— ~1s latency with extra charges; not compatible with query cache
Lightweight updates / row-level mutation	Yes Efficient row-level updates	Intermediate— Extra charges for changes; recently streamed data can’t be modified
High query concurrency1,000+ QPS per node	Yes 1,000+ QPS per node	Yes Depends on slot allocation; queries may queue or be rejected
Shared-storage architecture with decoupled compute and storage	Yes Separation of compute and storage	Yes Separation of compute and storage
Stateless compute nodes	Yes Stateless compute nodes for fast scaling	Yes Stateless compute supported
Control over data ordering and co-location	Yes Full control over ordering & co-location	Yes Stores data sorted by clustering columns
Allows partitioning of data for data skipping	Yes Partitioning supported	Yes Partitioning supported
Distributes query execution across nodes	Yes Parallel replicas distribute workloads	Yes Shuffling across compute slots
Streaming ingestion support	Yes Native streaming ingestion	Yes Streaming supported (with extra charges)
Native semi-structured data type with type preservation	Yes JSON with type fidelity	Yes JSON supported (but no row-level policies on JSON columns)
Row-level ingestion	Yes Async inserts for small batches	Yes Inserts supported (but invalidate query cache)
Auto-resume from idle	Yes Seconds	No N/A (no concept of idling)
Vertical and horizontal scaling	Yes Includes vertical scaling for high-memory queries	No N/A (slots only; no user visibility)
Custom hardware profiles for priority workloads	Yes Custom hardware profiles supported	No Not supported
Manual resizing	Yes Manual resize supported	No N/A
Granular cache control	Yes Predicate-level cache control (node + distributed)	No Can only skip
Control over elastic scaling and resources used	Yes Quotas, scaling limits, workload scheduling	Intermediate— Requires reserved slots
High query concurrency per node	Yes Up to 1,000 concurrent queries per node	Intermediate— Concurrency limited by reserved slots; low without large reservations
Elastic scalingUp/down with load	Yes Elastic scaling up/down with load	Yes Slots dynamically allocated
Compute compute separation	Yes Compute/storage separation	Yes Compute/storage separation
Distributed cache across warehouse	Yes Distributed cache across nodes	Yes Distributed cache supported
Dictionaries for dimension table acceleration	Yes Dictionary acceleration for dimension tables	No Not supported
Sub-second query latency at scale	Yes Sub-second latency at scale	Intermediate— Hard to achieve; minimal latency typically 1–2s
High concurrency performance	Yes Up to 1,000 concurrent queries per node	Intermediate— Concurrency limited by reserved slots; low without large reservations
Materialized view support	Yes Incremental & refreshable with full SQL	Intermediate— Significant SQL limitations
Native aggregate optimizationMerge states, projections	Yes Merge states & projections for fast aggregates	Intermediate— Limited; only via materialized views
Compression efficiency	Yes 60% better compression in benchmarks	Intermediate— Standard columnar compression
Vectorized query execution	Yes Vectorized execution supported	Yes Vectorized execution supported
Join performanceMulti-billion rows	Yes Efficient joins across billions of rows	Yes Joins supported
Real-time analyticsExternal dashboards, product analytics, customer-facing applications	Yes Real-time analytics with low latency	Intermediate— Limited by minimum latency and cost
ObservabilityLogs, metrics, traces	Yes Native support via ClickStack	Intermediate— Charges for data writes make this expensive
Streaming / continuous data ingestion	Yes Continuous ingestion at scale	Intermediate— Expensive at scale
GenAI / LLM agentic workloads	Yes Low-latency SQL over events	Intermediate— Minimum latency slows agentic responses
Internal analytics and operational dashboards	Yes Internal dashboards supported	Yes Internal dashboards supported
Vector search	Yes Vector search supported	Yes Vector search supported
File format support	Yes 70+ formats including Parquet, ORC, Avro, JSON, CSV	Intermediate— Limited to common formats (Avro, CSV, JSON, ORC, Parquet)
External table engines	Yes Connect to Postgres, MongoDB, MySQL, S3, Kafka, and more	Intermediate— Object stores only
Query external data in-place	Yes Query in-place via table engines (e.g. Postgres, S3)	Intermediate— BigLake on object storage only; supports Avro, CSV, Delta Lake, Iceberg, JSON, ORC, Parquet
Support for third-party catalogs	Yes Third-party catalogs supported	Intermediate— AWS Glue only
Change Data Capture (CDC)	Yes ClickPipes CDC for MySQL and Postgres	Yes Via Datastream
Support for open table formatse.g. Iceberg, Parquet, ORC	Yes Open formats supported	Yes Open formats supported

Built for real-time

Power always-on, low-latency, high-concurrency workloads

Predictable pricing

No surprise bills or penalties for usage spikes or need to upgrade to expensive plans to access advanced features

Lower costs

3–5x better performance per dollar than BigQuery, less spend, and more headroom.

Open source and open standards

Flexible deployments models from open source to managed cloud and BYOC, with support for external data catalogues and lake formats

Migrate your workload from BigQuery today

Cut costs, boost performance, and unlock real-time analytics with ClickHouse.
We’ll get you started on a 30 day trial and $300 credits to spend at your own pace.

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