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Apache Druid vs BigQuery

Apache Druid logo

Apache Druid

Databases

Real-time analytics database for sub-second OLAP queries

From
Free
Rated
-
Full reviewWebsite
BigQuery logo

BigQuery

Databases

Google Cloud's serverless analytical warehouse, billed either by bytes scanned per query or by reserved compute slots.

From
Free
Rated
-
Full reviewWebsite

The short version

  • Each has a real cost: Apache Druid open-source offering lacks high-availability, distributed architecture, and enterprise security features; BigQuery on-demand billing charges for bytes read from every column a query references, so an unqualified select or a missing partition filter turns a routine query into a large bill, and the cost is discovered after the fact rather than at review time.
  • They diverge on capability: Apache Druid covers Real-time Ingestion, BigQuery covers Serverless compute.
  • Prices and features above were last checked on 30 August 2026.

Where they differ

Only the attributes on which Apache Druid and BigQuery actually diverge.

Attributes where Apache Druid and BigQuery differ
AttributeApache DruidBigQuery
Pricing modelopen-sourceusage-based
PlatformsDocker, Kubernetes, Native deployment (Java-based)Web, Cloud API
Founded19992008

Identical on both: starting price (Free), free tier (Yes), user rating (Not yet rated), category (Databases).

What each one covers

Drawn from each product's published feature list. An absence here means we hold no record of it - not that the product lacks it.

Only in Apache Druid

  • Real-time Ingestion
  • Sub-second Queries
  • Column-oriented Storage
  • Streaming Integration
  • Approximate Algorithms
  • Flexible Schemas
  • Time-based Partitioning
  • Kafka

Only in BigQuery

  • Serverless compute
  • Separation of storage and compute
  • Two pricing models
  • Partitioning and clustering
  • Materialised views
  • BigQuery ML
  • Storage Write API
  • BI Engine

What people use each for

The jobs each tool is most often brought in to do.

Apache Druid

  • Real-time analytics platforms ingesting millions of events per second from streaming sourcesnot BigQuery
  • Applications requiring sub-second queries over high-cardinality datasets (billions to trillions of rows)not BigQuery
  • Time-series and event analysis at massive scale with columnar storage efficiencynot BigQuery

BigQuery

  • A warehouse for an organisation already on Google Cloud, where identity, logging and billing are consolidated in the same placenot Apache Druid
  • Bursty analytical workloads with long idle periods, where paying per query beats keeping a cluster runningnot Apache Druid
  • Event and clickstream analytics ingested continuously through the Storage Write API and queried without a load windownot Apache Druid
  • Analytics teams with no infrastructure staff, where the absence of anything to tune or patch is worth more than dialect portabilitynot Apache Druid

Where each one falls short

Documented limitations, not opinions. Every one is a constraint you would hit in normal use.

Apache Druid

  • Open-source offering lacks high-availability, distributed architecture, and enterprise security features
  • Requires native integration with Apache Kafka or Amazon Kinesis for real-time ingestion; custom integrations need development
  • High-concurrency query support (hundreds of thousands QPS) requires significant cluster infrastructure investment

BigQuery

  • On-demand billing charges for bytes read from every column a query references, so an unqualified select or a missing partition filter turns a routine query into a large bill, and the cost is discovered after the fact rather than at review time.
  • There is no way to join tables that live in different regions, so a data estate split across regions for residency reasons has to be reconciled with copies and the storage and transfer that implies.
  • It is not built for point lookups; retrieving a single row has latency measured in hundreds of milliseconds or more, so BigQuery cannot serve an application's read path and always needs a second store in front of it.
  • Frequent small mutations run into DML concurrency limits and the cost of rewriting storage blocks, so a workload that updates individual rows continuously behaves badly compared with an append-only design.
  • The compute exists only inside Google Cloud, so while tables can be exported, the accumulated GoogleSQL, scheduled queries, authorised views, ML models and IAM structure do not move, and switching warehouses is a rewrite of the analytical layer.

Pricing, plan by plan

Apache Druid

Free

No published plan breakdown. See the Apache Druid review.

BigQuery

Free
  • Free TierFree
    • 1TB queries/month
    • 10GB storage/month
    • Standard support
  • On-demand$6.25/TB
    • Pay per query
    • Pay per storage
    • All features

Which should you pick?

Choose Apache Druid if

  • You need real-time ingestion.
  • You want to start without paying.
  • You work on Docker, Kubernetes, Native deployment (Java-based).
  • You also want sub-second queries.
Apache Druid review

Choose BigQuery if

  • You need serverless compute.
  • You want to start without paying.
  • You work on Web, Cloud API.
  • You also want separation of storage and compute.
BigQuery review

Questions people ask

Is Apache Druid or BigQuery better?
Neither clearly leads. Apache Druid starts at Free and BigQuery at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Apache Druid or BigQuery?
Apache Druid starts at Free and BigQuery at Free.
Does Apache Druid or BigQuery run on more platforms?
Apache Druid runs on Docker, Kubernetes, Native deployment (Java-based). BigQuery runs on Web, Cloud API.
Can I use Apache Druid for free?
Both have a free tier, so you can try either at no cost before committing.
What is Apache Druid best used for?
Apache Druid is most often used for real-time analytics platforms ingesting millions of events per second from streaming sources, applications requiring sub-second queries over high-cardinality datasets (billions to trillions of rows), time-series and event analysis at massive scale with columnar storage efficiency. Of those, real-time analytics platforms ingesting millions of events per second from streaming sources and applications requiring sub-second queries over high-cardinality datasets (billions to trillions of rows) are not what BigQuery is typically brought in for.
What can Apache Druid do that BigQuery cannot?
Apache Druid covers Real-time Ingestion, Sub-second Queries, Column-oriented Storage, Streaming Integration. BigQuery covers Serverless compute, Separation of storage and compute, Two pricing models, Partitioning and clustering.

Answered from the vendors’ own pages

Apache Druid: Is Apache Druid free to use?

Apache Druid is an open-source project with no licensing fees. It is licensed under CC BY-SA 4.0, and the Druid name and logo are trademarks of The Apache Software Foundation.

Source
BigQuery: How is BigQuery actually billed?

Storage is billed separately from compute. Compute is either on-demand, priced by the bytes a query reads from the referenced columns, or capacity-based, where you reserve autoscaling slots. Most cost surprises come from on-demand queries that scan more than expected.

Apache Druid: Can I use Apache Druid for commercial purposes?

Yes, Apache Druid is open-source software available for commercial use at no cost. The CC BY-SA 4.0 license permits commercial deployment.

Source
BigQuery: How do I control query cost?

Partition and cluster tables so queries prune data, select only the columns needed, use materialised views for repeated aggregations, and set maximum bytes billed on queries so a runaway scan fails instead of billing.

Apache Druid: Where do I find pricing for commercial support or services?

No pricing or support tiers are published on the Apache Druid homepage. For commercial support options, contact the Apache Druid community or consult additional resources beyond the project website.

Source
BigQuery: Can I use it without being on Google Cloud?

The service only runs on Google Cloud. BigQuery Omni can query data held in S3 or Azure storage, but the compute is still Google's and the account relationship is still with Google.

BigQuery: Is it suitable for serving application queries?

No. Latency for single-row reads is far too high. BigQuery is an analytical warehouse and application read paths need a transactional database or a cache in front of it.

BigQuery: When should I move from on-demand to capacity pricing?

When on-demand spend becomes both large and predictable, or when unpredictable spend is a bigger problem than query queueing. The switch trades a variable bill for a fixed one plus contention between workloads.

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