Machine Learning · head to head
BigQuery ML vs Knack
BigQuery ML
Machine Learning
Machine learning in BigQuery using SQL
- From
- Free
- Free plan
- Yes
Knack
Databases
Hosted no-code database and web app builder priced by stored records and storage, with unlimited user accounts on every plan.
- From
- $59/month
- Free plan
- No
The short version
- BigQuery ML has a free plan, then $5/mo; Knack starts at $59/mo.
- Only BigQuery ML lists SQL-based ML; only Knack lists visual database builder.
- Where BigQuery ML falls short: not available in BigQuery's Standard edition, so the cheapest tier cannot use it.
- Prices and features on this page were last checked on 13 November 2026.
Where they differ
Only the attributes on which BigQuery ML and Knack actually diverge.
| Attribute | BigQuery ML | Knack |
|---|---|---|
| Starting price | Free | $59/month |
| Pricing model | Usage-based | Subscription |
| Free tier | Yes | No |
| Category | Machine Learning | Databases |
Identical on both: platforms (Web).
Not yet recorded: founded for Knack.
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 Knack
- Visual database builder
- User logins and roles
- Page builder
- Unlimited users
- E-commerce
- Automation flows
- Scheduled tasks
- REST API and webhooks
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 Knack
- Linear and logistic regression on warehouse datanot Knack
- K-means clustering and matrix factorisation for recommendationsnot Knack
- Time series forecasting with ARIMA_PLUSnot Knack
- Running imported ONNX, TensorFlow or XGBoost models against BigQuery datanot Knack
Knack
- A member or client portal where thousands of external people need their own login and see only their own recordsnot BigQuery ML
- Replacing a shared spreadsheet used by a team that has outgrown it but cannot justify a developer to build a bespoke internal toolnot BigQuery ML
- Inventory, asset or case tracking for a small operations team that needs roles, forms and a record of who changed whatnot BigQuery ML
- A registration or booking application for an association, school or non-profit where the user count is large and the record count stays modestnot 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
Knack
- Plans are capped by stored records, so an application that logs events or keeps history rather than current state climbs tiers on data volume alone, and archiving old records becomes a permanent maintenance chore rather than a one-off
- API access is capped per day, at 1,000 calls on the entry plan and 10,000 at the Corporate tier, so a scheduled sync or a busy automation can exhaust the day's quota before the working day ends and integrations then fail silently
- The application itself cannot be exported or self-hosted; records can be downloaded but the pages, roles, flows and business logic exist only inside Knack, so outgrowing the platform means rebuilding the app rather than moving it
- Customisation beyond the builder is limited to injected CSS and JavaScript that Knack's support does not cover, so any non-standard interface behaviour becomes yours to maintain against a DOM the vendor can change without warning
- Compliance capabilities that regulated buyers assume are baseline, HIPAA and GovCloud options, advanced SSO, IP restrictions and private environments, are gated to the Corporate and Enterprise tiers, so a small regulated pilot cannot legitimately start on an entry plan
Pricing, plan by plan
BigQuery ML
Free- Free TierFree
- 10GB storage
- 1TB queries
- On-Demand$5/tb
- Pay per TB scanned
- ML training costs
Knack
$59/month- Starter$59/mo
- 20,000 database records
- Unlimited users, tables, fields
- 25 AI credits
- Pro$130/mo
- 50,000 database records
- Remove Knack branding
- E-commerce payments
- Corporate$300/mo
- 125,000-2.5M database records
- App restore functionality
- Advanced SSO
- EnterpriseOn request
- Dedicated account manager
- HIPAA/GovCloud-based options
- Custom pricing
Which should you pick?
Choose BigQuery ML if
- You need sql-based ml.
- You want to start without paying.
- You also want automl tables.
Questions people ask
- Is BigQuery ML or Knack better?
- Neither clearly leads. BigQuery ML starts at Free and Knack at $59/month, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
- Which is cheaper, BigQuery ML or Knack?
- BigQuery ML has a free tier; the other does not. Paid plans start at Free for BigQuery ML and $59/month for Knack.
- Does BigQuery ML or Knack run on more platforms?
- Both run on Web, so platform support will not decide this one for you.
- Can I use BigQuery ML for free?
- Yes. BigQuery ML has a free tier, so you can try it without paying. Knack starts at $59/month.
- 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 Knack is typically brought in for.
- What can BigQuery ML do that Knack cannot?
- BigQuery ML covers SQL-based ML, AutoML Tables, Model export, Prediction functions. Knack covers Visual database builder, User logins and roles, Page builder, Unlimited users.
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.
SourceKnack: Is Knack charged per user?
No. User accounts are unlimited on all plans. Plans are bounded by stored record count, file storage, API calls per day and automation transactions.
BigQuery 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.
SourceKnack: What happens when I exceed the record limit?
You move to a higher tier or reduce stored records. Because the limit is on data rather than people, apps that accumulate history hit it while apps that track a fixed set of entities generally do not.
Knack: Can I export my application or self-host it?
You can export record data. The application itself, its pages, roles, permissions and flows, is not exportable and cannot be self-hosted, so leaving means rebuilding elsewhere.
Knack: Is it suitable for public-facing portals?
That is its strongest case, because logins are unlimited and role-based permissions are built in. The constraint is record volume and API throughput, not audience size.
Knack: Can I use it for regulated data?
HIPAA-aligned and government cloud options exist but only on the Enterprise tier, along with private server environments. Lower tiers should not be used for regulated workloads.
Related 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 BentoML
- BigQuery ML vs ClearML
- BigQuery ML vs Cohere
- BigQuery ML vs Dask
- BigQuery ML vs Groq
- BigQuery ML vs Haystack
- BigQuery ML vs Amazon Redshift ML
- BigQuery ML vs Airtable
- BigQuery ML vs BigQuery
- BigQuery ML vs Firebase Realtime Database
- BigQuery ML vs Dremio
- BigQuery ML vs etcd
- BigQuery ML vs DuckDB
- BigQuery ML vs Immuta
- BigQuery ML vs Trino
- BigQuery ML vs Turso
- BigQuery ML vs FaunaDB
- BigQuery ML vs Ninox
- BigQuery ML vs Privacera
- BigQuery ML vs PostgreSQL
- BigQuery ML vs Apache Kafka
- BigQuery ML vs RabbitMQ
- BigQuery ML vs Amazon Aurora
- BigQuery ML vs ClickHouse
- BigQuery ML vs Elasticsearch
- Knack vs AWS SageMaker
- Knack vs Azure Machine Learning
- Knack vs DataRobot
- Knack vs Databricks
- Knack vs SAS
- Knack vs scikit-learn
- Knack vs Snowflake
- Knack vs Weka
- Knack vs MATLAB
- Knack vs Palantir Foundry
- Knack vs Apache Spark MLlib
- Knack vs BentoML
- Knack vs ClearML
- Knack vs Cohere
- Knack vs Dask
- Knack vs Groq
- Knack vs Haystack
- Knack vs Amazon Redshift ML
- Knack vs Airtable
- Knack vs BigQuery
- Knack vs Firebase Realtime Database
- Knack vs Dremio
- Knack vs etcd
- Knack vs DuckDB
- Knack vs Immuta
- Knack vs Trino
- Knack vs Turso
- Knack vs FaunaDB
- Knack vs Ninox
- Knack vs Privacera
- Knack vs PostgreSQL
- Knack vs Apache Kafka
- Knack vs RabbitMQ
- Knack vs Amazon Aurora
- Knack vs ClickHouse
- Knack vs Elasticsearch