Machine Learning · head to head
BigQuery ML vs Heap
The short version
- Each has a real cost: BigQuery ML not available in BigQuery's Standard edition, so the cheapest tier cannot use it; Heap no built-in A/B testing or feature flags; requires integration with separate tools for experimentation
- They diverge on capability: BigQuery ML covers SQL-based ML, Heap covers Autocapture.
- Prices and features above were last checked on 30 August 2026.
Where they differ
Only the attributes on which BigQuery ML and Heap actually diverge.
| Attribute | BigQuery ML | Heap |
|---|---|---|
| Pricing model | usage-based | Unknown |
| Platforms | Web | Web, iOS, Android |
| Category | Machine Learning | Technology |
| Founded | 2008 | 2013 |
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 Heap
- Autocapture
- Retroactive analytics
- Session replay
- Funnel analysis
- User segmentation
- Path analysis
- Data science
- Virtual events
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 Heap
- Linear and logistic regression on warehouse datanot Heap
- K-means clustering and matrix factorisation for recommendationsnot Heap
- Time series forecasting with ARIMA_PLUSnot Heap
- Running imported ONNX, TensorFlow or XGBoost models against BigQuery datanot Heap
Heap
- User behavior analysisnot BigQuery ML
- Conversion optimizationnot BigQuery ML
- Product adoptionnot BigQuery ML
- Customer journey mappingnot BigQuery ML
- A/B testing analysisnot 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
Heap
- No built-in A/B testing or feature flags; requires integration with separate tools for experimentation
- Group analytics and advanced features require a sales conversation, not self-serve
- Cloud-only deployment; no self-hosted option for data security or compliance requirements
- Session replay lacks developer debugging tools compared to PostHog
- Pricing for Growth and Pro plans requires direct sales contact; no transparency on how pricing scales
Pricing, plan by plan
BigQuery ML
Free- Free TierFree
- 10GB storage
- 1TB queries
- On-Demand$5/TB
- Pay per TB scanned
- ML training costs
Heap
Free- FreeFree
- Up to 10,000 monthly sessions
- Basic charts
- 6 months data history
- Growth$undefined/custom
- Custom session pricing
- Sense AI assistant
- 12 months data history
- Pro$undefined/custom
- Custom session pricing
- Account analytics
- Engagement matrix
- Premier$undefined/custom
- Custom session pricing
- Data warehouse integration
- Unlimited projects
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 Heap if
- You need autocapture.
- You want to start without paying.
- You work on Web, iOS, Android.
- You also want retroactive analytics.
Questions people ask
- Is BigQuery ML or Heap better?
- Neither clearly leads. BigQuery ML starts at Free and Heap at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
- Which is cheaper, BigQuery ML or Heap?
- BigQuery ML starts at Free and Heap at Free.
- Does BigQuery ML or Heap run on more platforms?
- BigQuery ML runs on Web. Heap runs on Web, iOS, Android.
- 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 Heap is typically brought in for.
- What can BigQuery ML do that Heap cannot?
- BigQuery ML covers SQL-based ML, AutoML Tables, Model export, Prediction functions. Heap covers Autocapture, Retroactive analytics, Session replay, Funnel analysis.
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.
SourceHeap: What does Heap's autocapture feature do?
Heap's autocapture is a single code snippet that automatically captures every click, swipe, tap, pageview, and form fill on your website and apps without requiring manual event setup. Once installed, Heap captures the entire digital experience of every user on every platform with no ongoing engineering maintenance needed.
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.
SourceHeap: What are Heap's pricing plans and how much do they cost?
Heap offers a Free plan for up to 10,000 monthly sessions. Growth, Pro, and Premier plans use custom session-based pricing that requires contacting sales for a quote. Free includes basic charts and 6 months data history. Growth adds the Sense AI assistant. Pro adds account analytics. Premier adds data warehouse integration and dedicated customer success management.
SourceHeap: Does Heap include session replay and A/B testing?
Heap includes integrated session replay showing exactly what users did on your site. However, Heap does not include built-in A/B testing or feature flags. Teams requiring these capabilities must use separate tools or integrate with third-party platforms.
SourceHeap: What integrations does Heap support?
Heap supports over 100 integrations connecting to business tools including marketing platforms, CRMs, and data warehouses. This allows insights to reach relevant teams and ensures data flows to other business systems automatically.
SourceHeap: Does Heap offer self-hosting or is it cloud-only?
Heap is cloud-only and does not offer self-hosted options. Organizations requiring on-premises deployment should consider alternatives like PostHog which supports self-hosting alongside its cloud product.
SourceHeap: What is Sense and how does it help with analytics?
Sense Chat is Heap's AI assistant that enables users to access analytics without extensive technical knowledge. It allows teams to ask questions about user behavior and get answers directly without lengthy onboarding or technical expertise, making insights more accessible to non-technical stakeholders.
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 Mixpanel
- BigQuery ML vs PostHog
- BigQuery ML vs Amplitude
- BigQuery ML vs Pendo
- BigQuery ML vs Userpilot
- BigQuery ML vs Segment
- BigQuery ML vs RescueTime
- BigQuery ML vs Lovable
- BigQuery ML vs Coda
- BigQuery ML vs Istio
- BigQuery ML vs Linear
- BigQuery ML vs Terraform
- BigQuery ML vs Intercom
- BigQuery ML vs LaunchDarkly
- BigQuery ML vs Monday.com
- Heap vs AWS SageMaker
- Heap vs Azure Machine Learning
- Heap vs DataRobot
- Heap vs Databricks
- Heap vs SAS
- Heap vs scikit-learn
- Heap vs Snowflake
- Heap vs Weka
- Heap vs MATLAB
- Heap vs Palantir Foundry
- Heap vs Apache Spark MLlib
- Heap vs Hugging Face
- Heap vs Kubeflow
- Heap vs Langwatch
- Heap vs LlamaIndex
- Heap vs Milvus
- Heap vs Neptune.ai
- Heap vs Amazon Redshift ML
- Heap vs Mixpanel
- Heap vs PostHog
- Heap vs Amplitude
- Heap vs Pendo
- Heap vs Userpilot
- Heap vs Segment
- Heap vs RescueTime
- Heap vs Lovable
- Heap vs Coda
- Heap vs Istio
- Heap vs Linear
- Heap vs Terraform
- Heap vs Intercom
- Heap vs LaunchDarkly
- Heap vs Monday.com


