ClickHouse and BigQuery both target analytical workloads on large datasets, and both deliver real performance. The comparison that matters for engineering decisions is not about which one is "faster" in isolation, but about the trade-offs each architecture imposes: how compute is provisioned, how costs scale with query frequency, and where latency variability comes from. Those differences are structural, not incidental.
Architecture: Two Fundamentally Different Models
BigQuery is built on Google's Dremel query engine, which decomposes a query into a tree of workers and fans out execution across thousands of ephemeral compute nodes. Storage lives on Colossus, Google's distributed file system, in a columnar format called Capacitor. The separation of storage and compute is total: when a query arrives, BigQuery allocates slots - each slot a virtual compute unit whose exact CPU and memory equivalence Google does not publicly document - from a shared pool, executes the query, then releases the resources. You never configure instances, manage memory settings, or think about which physical node holds which data. This is a genuine operational advantage for teams that do not want database infrastructure to be part of their engineering surface area.
ClickHouse flips this model. Compute and storage are co-located by default (or tightly coupled in ClickHouse Cloud). Tables use the MergeTree engine, an LSM-tree-inspired structure that writes data as immutable sorted parts and merges them in the background. Data within each part is sorted by primary key, and a sparse index stores one mark per granule (8,192 rows by default), allowing the query engine to skip irrelevant granules without scanning entire columns. Vectorized execution processes data using SIMD instructions across batches, keeping CPU caches warm and avoiding row-by-row overhead. You are running a dedicated cluster - whether self-hosted or on ClickHouse Cloud - and the compute is yours for as long as you provision it.
Query Performance and Latency Characteristics
ClickHouse consistently delivers lower and more predictable query latency than BigQuery, particularly for queries over medium-to-large datasets with well-defined filter patterns on primary key or indexed columns. A count aggregation or a filtered time-series rollup on a properly designed ClickHouse table with tens of billions of rows commonly returns in single-digit milliseconds. BigQuery can match or exceed that on some queries, but it introduces latency variance that ClickHouse does not.
The source of BigQuery's variability is the slot scheduling model. Under on-demand pricing, each project has access to up to 2,000 slots (a soft cap). Repeated benchmarks on the same query can show significant latency variation because BigQuery dynamically allocates slots based on query complexity and resource availability. BigQuery has added history-based optimizations that use past query execution to refine query plans — including adjusting initial parallelism and join strategies — which helps recurring queries run faster, but does not eliminate the fundamental variability introduced by dynamic slot scheduling. ClickHouse running on dedicated compute has no equivalent scheduling uncertainty.
For ad hoc queries against petabyte-scale datasets, BigQuery is formidable. Its ability to throw thousands of ephemeral workers at a single query means that a query scanning 10 TB of data can complete in seconds without any pre-configured parallelism on your side. ClickHouse can reach similar throughput by scaling horizontally, but it requires you to have provisioned and configured that capacity upfront. If your queries scan entire large tables infrequently, BigQuery's elasticity can win on wall-clock time without any engineering effort.
Cost Model: Per-Query vs Per-Hour
BigQuery's on-demand pricing charges $6.25 per TB scanned. Storage is billed separately at $0.02 per GB per month for active data under logical billing (the default), or $0.04/GB under physical billing; data not modified for 90 days automatically halves in price under BigQuery's long-term storage discount. The on-demand model is attractive when queries are infrequent and datasets are well-partitioned. For higher query volumes, BigQuery Editions (Standard, Enterprise, Enterprise Plus) replaced the legacy flat-rate model in July 2023 with slot-hour pricing: Standard Edition costs $0.04 per slot-hour with no commitment; Enterprise costs around $0.06 per slot-hour with autoscaling and optional 1- or 3-year discounts. Editions also support slot autoscaling so capacity scales dynamically rather than requiring fixed reservation planning.
ClickHouse's cost model is compute-based. On ClickHouse Cloud (pricing restructured in January 2025), the Basic tier starts at around $0.22 per compute unit per hour and the Scale tier (recommended for production) ranges from approximately $0.22–$0.39 per compute unit per hour depending on region and cloud provider, with storage at approximately $25 per TB per month. Self-hosted deployments shift the cost entirely to cloud instance pricing and your operational overhead. The key implication is that running thousands of queries per day against ClickHouse has a flat marginal cost: once the cluster is provisioned, additional queries do not directly add to your bill. Under BigQuery on-demand pricing, those same queries compound. A team running a multi-tenant analytics product where each end user triggers queries would face a very different bill depending on which system they chose - BigQuery costs scale with query volume and bytes scanned, ClickHouse costs scale with cluster size and hours.
BigQuery supports streaming ingestion via the legacy streaming API ($50/TB) or the newer Storage Write API ($25/TB after a 2 TiB/month free tier). For a high-throughput event ingestion pipeline, that adds up quickly and is independent of query costs. ClickHouse ingests data through batched inserts, Kafka table engines, or async insert mode, and all of these are covered by the existing compute cost.
Data Freshness and Ingestion
This is where the architectural difference translates most directly into product behavior. Data written to a ClickHouse MergeTree table is immediately queryable. There is no separate ingestion pipeline, no indexing delay, no streaming buffer to drain. Inserts and concurrent reads operate without locks; new parts are visible to queries as soon as the insert commits. For a user-facing dashboard backed by a Kafka consumer writing into ClickHouse, end-to-end latency from event to visible query result can be measured in seconds.
BigQuery streaming inserts typically make data available for querying within a few seconds, which is adequate for many batch analytics workloads. The problem appears at the boundaries: the streaming buffer is not always consistent with partition-based queries, results can be inconsistent under concurrent writes, and the cached query results feature is invalidated by streaming ingestion. For products where data freshness is a user-visible feature rather than an internal implementation detail, these limitations require workarounds - materialized views with scheduled refreshes, or accepting that some dashboard queries hit the streaming buffer while others do not.
Ecosystem and Operational Fit
BigQuery's depth within GCP is genuine. Native connectors exist for Pub/Sub, Dataflow, Looker, Vertex AI, and the broader Google data platform. IAM integration means access control flows through the same system used for every other GCP service. Data Transfer Service handles replication from dozens of SaaS sources. If your organization has committed to GCP and already uses these services, BigQuery's integration surface reduces the glue code you need to write and maintain.
ClickHouse is open-source (Apache 2.0) and integrates through a different surface: a wide ecosystem of community-maintained connectors, first-class Kafka integration via the Kafka table engine, JDBC/ODBC drivers, and HTTP interface compatibility with many BI tools. ClickHouse Cloud adds managed scaling, backups, and a control plane, but the operational surface is still higher than BigQuery. You are responsible for schema design, index planning, table ordering keys, and TTL policies. These choices have large performance implications - a poorly ordered primary key on a ClickHouse table can make queries an order of magnitude slower. BigQuery abstracts most of these decisions, at the cost of flexibility.
When Each System Fits
BigQuery is the correct choice when your organization is already running on GCP and wants analytical capability without a dedicated data engineering team managing cluster configuration. It also wins for workloads that are intermittent or unpredictable in volume - quarterly reports, ad hoc exploration of large historical datasets, compliance queries triggered by audits - where paying for always-on compute would be wasteful. The Google ecosystem integrations and the managed IAM and audit trail make BigQuery a natural fit for enterprise data governance requirements.
ClickHouse is the right choice when query latency stability matters for end users, when query volume is high enough that per-TB scanning costs become prohibitive, and when data freshness requirements demand immediate queryability after write. Teams building embedded analytics, SaaS reporting features, observability platforms, or any product where the database query sits in the hot path of a user-facing request will find ClickHouse's predictable sub-second performance more useful than BigQuery's elastic but variable throughput. The engineering investment in schema design and cluster operations is real, but the control it provides is also real.
The choice is rarely about raw speed. It is about where you want the complexity to live: in infrastructure operations and schema design (ClickHouse), or in cost modeling, slot capacity planning, and GCP vendor dependency (BigQuery).
