Machine Learning · head to head
BigQuery ML vs QuestDB
QuestDB
Databases
Fast open source time-series database for high throughput ingestion
- From
- Free
- Rated
- -
The short version
- Each has a real cost: BigQuery ML not available in BigQuery's Standard edition, so the cheapest tier cannot use it; QuestDB open-source edition lacks high-availability, distributed architecture, and enterprise security features
- They diverge on capability: BigQuery ML covers SQL-based ML, QuestDB covers High Throughput Ingestion.
- Prices and features above were last checked on 30 August 2026.
Where they differ
Only the attributes on which BigQuery ML and QuestDB actually diverge.
| Attribute | BigQuery ML | QuestDB |
|---|---|---|
| Pricing model | usage-based | open-source |
| Platforms | Web | Docker, Kubernetes, Cloud (AWS, Azure, GCP) |
| Category | Machine Learning | Databases |
| Founded | 2008 | 2014 |
Identical on both: starting price (Free), free tier (Yes), user rating (Not yet rated).
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 BigQuery ML
- SQL-based ML
- AutoML Tables
- Model export
- Prediction functions
- Feature preprocessing
- BigQuery
- Vertex AI
- TensorFlow
Only in QuestDB
- High Throughput Ingestion
- SQL Support
- Time-series Optimization
- SIMD Vectorization
- Column-oriented Storage
- Built-in Web Console
- InfluxDB Line Protocol
- PostgreSQL
Both cover
- Web support
What people use each for
The jobs each tool is most often brought in to do.
BigQuery ML
- Training models in SQL without exporting datanot QuestDB
- Linear and logistic regression on warehouse datanot QuestDB
- K-means clustering and matrix factorisation for recommendationsnot QuestDB
- Time series forecasting with ARIMA_PLUSnot QuestDB
- Running imported ONNX, TensorFlow or XGBoost models against BigQuery datanot QuestDB
QuestDB
- Time-series analytics ingesting up to 20M rows/second from IoT sensors or financial data feedsnot BigQuery ML
- Real-time dashboarding with 32ms time-to-first-row latency for minute-level analyticsnot BigQuery ML
- Applications requiring multi-tier storage (hot ingest, real-time SQL, cold Parquet archive)not BigQuery ML
Where each one falls short
Documented limitations, not opinions. Every one is a constraint you would hit in normal use.
BigQuery ML
- Not available in BigQuery's Standard edition, so the cheapest tier cannot use it
- Billed through BigQuery compute and storage rather than as its own product, so training cost tracks data scanned
- Remote models incur extra Agent Platform charges on top
- Externally trained model types such as boosted trees and AutoML run through Agent Platform rather than inside BigQuery
QuestDB
- Open-source edition lacks high-availability, distributed architecture, and enterprise security features
- Enterprise edition pricing not published; requires contacting sales for custom quote
- Ingestion limit of 20M rows/sec platform-dependent; may not scale to extreme throughput requirements
Pricing, plan by plan
BigQuery ML
Free- Free TierFree
- 10GB storage
- 1TB queries
- On-Demand$5/TB
- Pay per TB scanned
- ML training costs
QuestDB
FreeNo published plan breakdown. See the QuestDB review.
Which should you pick?
Choose BigQuery ML if
- You need sql-based ml.
- You want to start without paying.
- You also want automl tables.
Choose QuestDB if
- You need high throughput ingestion.
- You want to start without paying.
- You work on Docker, Kubernetes, Cloud (AWS, Azure, GCP).
- You also want sql support.
Questions people ask
- Is BigQuery ML or QuestDB better?
- Neither clearly leads. BigQuery ML starts at Free and QuestDB at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
- Which is cheaper, BigQuery ML or QuestDB?
- BigQuery ML starts at Free and QuestDB at Free.
- Does BigQuery ML or QuestDB run on more platforms?
- BigQuery ML runs on Web. QuestDB runs on Docker, Kubernetes, Cloud (AWS, Azure, GCP).
- Can I use BigQuery ML for free?
- Both have a free tier, so you can try either at no cost before committing.
- What is BigQuery ML best used for?
- BigQuery ML is most often used for training models in sql without exporting data, linear and logistic regression on warehouse data, k-means clustering and matrix factorisation for recommendations, time series forecasting with arima_plus. Of those, training models in sql without exporting data and linear and logistic regression on warehouse data are not what QuestDB is typically brought in for.
- What can BigQuery ML do that QuestDB cannot?
- BigQuery ML covers SQL-based ML, AutoML Tables, Model export, Prediction functions. QuestDB covers High Throughput Ingestion, SQL Support, Time-series Optimization, SIMD Vectorization. Both handle Web support.
Answered from the vendors’ own pages
BigQuery ML: How much does Google Cloud BigQuery ML cost?
BigQuery ML pricing is not specified separately on Google Cloud's pricing page. It follows the same pay-as-you-go model as BigQuery, charging per terabyte of data scanned during analysis. Customers receive $300 in free credits and can use 20+ products free up to monthly limits.
SourceQuestDB: How much does QuestDB Enterprise cost?
QuestDB does not publish specific pricing for the Enterprise tier. Customers must contact QuestDB via their enterprise contact form to receive a custom quote.
SourceBigQuery ML: Does Google Cloud offer a free trial?
Yes, new customers get $300 in free credits and all customers can use 20+ Google Cloud products free up to their monthly usage limits.
SourceQuestDB: Does QuestDB offer a free version?
Yes, QuestDB Open Source is completely free and recommended for evaluation, prototyping, and pilot projects. Enterprise features, high availability, security, and dedicated support require the paid Enterprise tier.
SourceQuestDB: What deployment options does QuestDB offer?
QuestDB offers open source deployment, Enterprise deployment, and Bring Your Own Cloud (BYOC) deployment. Pricing details for BYOC and Enterprise tiers are not published and require direct contact with sales.
SourceRelated pages
More on BigQuery ML
Other head to heads
- BigQuery ML vs AWS SageMaker
- BigQuery ML vs Azure Machine Learning
- BigQuery ML vs DataRobot
- BigQuery ML vs Databricks
- BigQuery ML vs SAS
- BigQuery ML vs scikit-learn
- BigQuery ML vs Snowflake
- BigQuery ML vs Weka
- BigQuery ML vs MATLAB
- BigQuery ML vs Palantir Foundry
- BigQuery ML vs Apache Spark MLlib
- BigQuery ML vs Hugging Face
- BigQuery ML vs Kubeflow
- BigQuery ML vs Langwatch
- BigQuery ML vs LlamaIndex
- BigQuery ML vs Milvus
- BigQuery ML vs Neptune.ai
- BigQuery ML vs Amazon Redshift ML
- BigQuery ML vs TimescaleDB
- BigQuery ML vs PostgreSQL
- BigQuery ML vs Cockroach Labs
- BigQuery ML vs Amazon Aurora
- BigQuery ML vs Airtable
- BigQuery ML vs Firebolt
- BigQuery ML vs Apache Flink
- BigQuery ML vs DuckDB
- BigQuery ML vs OpenSearch
- BigQuery ML vs ClickHouse
- BigQuery ML vs NATS
- BigQuery ML vs Canary Labs
- BigQuery ML vs Chroma
- BigQuery ML vs Cloudinary
- BigQuery ML vs Convex
- BigQuery ML vs Dgraph
- BigQuery ML vs Dragonfly
- BigQuery ML vs Apache Druid
- QuestDB vs AWS SageMaker
- QuestDB vs Azure Machine Learning
- QuestDB vs DataRobot
- QuestDB vs Databricks
- QuestDB vs SAS
- QuestDB vs scikit-learn
- QuestDB vs Snowflake
- QuestDB vs Weka
- QuestDB vs MATLAB
- QuestDB vs Palantir Foundry
- QuestDB vs Apache Spark MLlib
- QuestDB vs Hugging Face
- QuestDB vs Kubeflow
- QuestDB vs Langwatch
- QuestDB vs LlamaIndex
- QuestDB vs Milvus
- QuestDB vs Neptune.ai
- QuestDB vs Amazon Redshift ML
- QuestDB vs TimescaleDB
- QuestDB vs PostgreSQL
- QuestDB vs Cockroach Labs
- QuestDB vs Amazon Aurora
- QuestDB vs Airtable
- QuestDB vs Firebolt
- QuestDB vs Apache Flink
- QuestDB vs DuckDB
- QuestDB vs OpenSearch
- QuestDB vs ClickHouse
- QuestDB vs NATS
- QuestDB vs Canary Labs
- QuestDB vs Chroma
- QuestDB vs Cloudinary
- QuestDB vs Convex
- QuestDB vs Dgraph
- QuestDB vs Dragonfly
- QuestDB vs Apache Druid