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Machine Learning · head to head

BigQuery ML vs LatchBio

BigQuery ML logo

BigQuery ML

Machine Learning

Machine learning in BigQuery using SQL

From
Free
Free plan
Yes
Full reviewWebsite
LatchBio logo

LatchBio

AI

AI agents that run bioinformatics analyses, from a company that pivoted from workflow hosting

From
On request
Free plan
No
Full reviewWebsite

The short version

  • Only BigQuery ML has a free tier, so it costs nothing to try first.
  • Each has a real cost: BigQuery ML not available in BigQuery's Standard edition, so the cheapest tier cannot use it; LatchBio the company has pivoted from hosted bioinformatics workflows to AI agents, so organisations that adopted the earlier SDK and workflow product are now on a line that is no longer the strategic focus.
  • They diverge on capability: BigQuery ML covers SQL-based ML, LatchBio covers Agent driven analysis.
  • Prices and features above were last checked on 31 August 2026.

Where they differ

Only the attributes on which BigQuery ML and LatchBio actually diverge.

Attributes where BigQuery ML and LatchBio differ
AttributeBigQuery MLLatchBio
Starting priceFreeOn request
Pricing modelusage-basedquote
Free tierYesNo
CategoryMachine LearningAI
Founded2008Unknown

Identical on both: platforms (Web), 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 LatchBio

  • Agent driven analysis
  • Multi assay support
  • Published benchmarks
  • White labelled portals
  • Common file format handling
  • Managed compute

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 LatchBio
  • Linear and logistic regression on warehouse datanot LatchBio
  • K-means clustering and matrix factorisation for recommendationsnot LatchBio
  • Time series forecasting with ARIMA_PLUSnot LatchBio
  • Running imported ONNX, TensorFlow or XGBoost models against BigQuery datanot LatchBio

LatchBio

  • A small biotech with sequencing data and no bioinformatician who needs a first pass differential expression or cell type annotation quicklynot BigQuery ML
  • An assay or kit vendor that wants to give customers an analysis portal under its own branding rather than shipping raw filesnot BigQuery ML
  • A wet lab scientist who wants to explore a dataset interactively before commissioning a formal analysis from a core facilitynot BigQuery ML
  • A research group benchmarking agent produced analyses against its own established pipeline to decide how much routine work can be delegatednot 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

LatchBio

  • The company has pivoted from hosted bioinformatics workflows to AI agents, so organisations that adopted the earlier SDK and workflow product are now on a line that is no longer the strategic focus.
  • Agent generated analysis code has to be validated by someone who could have written it, which inverts the value proposition for the small labs that most need it and cannot check the output.
  • No pricing is published, so the cost of routine agent analysis at team scale cannot be estimated without contacting sales, and compute billed through the platform makes the bill usage dependent.
  • Regulated use is a poor fit: an agent that writes non deterministic analysis code is difficult to validate under GxP, so this is a research tool rather than something that will sit in a regulated diagnostic or submission workflow.
  • The vendor publishes its own benchmarks, which is more than most competitors do, but self reported benchmarks on selected datasets are not a substitute for testing the agent on your own data before committing.

Pricing, plan by plan

BigQuery ML

Free
  • Free TierFree
    • 10GB storage
    • 1TB queries
  • On-Demand$5/TB
    • Pay per TB scanned
    • ML training costs

LatchBio

On request
  • LatchBio Platform$undefined/year
    • Quoted through a sales process
    • Interactive sandbox available for trying the agent before contracting
    • Enterprise and white labelled portal deployments priced separately

Which should you pick?

Choose BigQuery ML if

  • You need sql-based ml.
  • You want to start without paying.
  • You also want automl tables.
BigQuery ML review

Choose LatchBio if

  • You need agent driven analysis.
  • You also want multi assay support.
LatchBio review

Questions people ask

Is BigQuery ML or LatchBio better?
Neither clearly leads. BigQuery ML starts at Free and LatchBio at On request, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, BigQuery ML or LatchBio?
BigQuery ML has a free tier; the other does not. Paid plans start at Free for BigQuery ML and On request for LatchBio.
Does BigQuery ML or LatchBio 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. LatchBio starts at On request.
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 LatchBio is typically brought in for.
What can BigQuery ML do that LatchBio cannot?
BigQuery ML covers SQL-based ML, AutoML Tables, Model export, Prediction functions. LatchBio covers Agent driven analysis, Multi assay support, Published benchmarks, White labelled portals.

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.

Source
LatchBio: Is LatchBio still selling the original workflow platform?

The company is now led by agent.bio and its agent products. Buyers evaluating the older Python SDK workflow platform should confirm current support directly.

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.

Source
LatchBio: Can I try it before buying?

Yes. There is an interactive sandbox where you can upload data and run the agent before entering a sales conversation.

LatchBio: Is it suitable for regulated work?

Not really. Non deterministic agent generated code is hard to validate under GxP, so treat it as a research and exploration tool.

LatchBio: What data types does it handle?

Single cell RNA sequencing, spatial transcriptomics, epigenomics, proteomics and pharmacology data, ingesting FastQ, H5AD and spreadsheet formats.

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