Small business▼−4Since September 2026: 13% → 10%, −4 points. Inside the 11-point floor: within noise. Read over the models both editions asked.
10%from 13% in September 2026
Inside the floor by 7 pointsRank 2 → 3 of 131
1 category · RankedGoogle Colab
20Judge labels
11First choices
0Negative labels
12 / 14Models named it
1Category
October 2026 Edition. Every number here is derived from the raw labels under vendor table v2026-10.8, every buyer segment counted.
Best standing
9% in ML platforms for mid-market buyers
Rank 5 of 107 in the mid-market standingaccepted challenger
0 of 14 models made it the first choice on the direct prompt; 0% of its 12 labels there were negative.
What the models named instead of Google Colab →By buyer segmentRead the same way at every buyer size.
Small business2 of 139
9%8 labels
under the label threshold
Mid-market5 of 107
9%12 labels
accepted challenger
Enterprisenot named
0%
No model named it for this buyer.
In ml platforms · each standing computed within its segment · bars are 0 to 100 · the accent bar is the product's own best reading
Standing by category
Every category where a model named Google Colab for a mid-market B2B company. Share is first choices across the direct, paraphrase, budget and scale prompts; rank is within every product named in that category.
| Category | Function | Share | Rank | Negative rate | Labels | Quadrant | Since September 2026 |
|---|---|---|---|---|---|---|---|
| ML platforms | Data platform | 9% | 5 of 107 | 0% | 12 | accepted challenger | ▲+4Since September 2026: 4% → 8%, +4 points. Inside the 11-point floor: within noise. Read over the models both editions asked. |
Movement
Since September 2026, Google Colab held within the floor in its one category: no change cleared 11 points.
Best category · ML platforms · by segment
Mid-market▲+4Since September 2026: 4% → 8%, +4 points. Inside the 11-point floor: within noise. Read over the models both editions asked.
8%from 4% in September 2026
Inside the floor by 7 pointsRank 9 → 5 of 97
All categories · Mid-market
| Category | September 2026 | Now | Change | Reading | Rank |
|---|---|---|---|---|---|
| ML platforms | 4% | 8% | ▲+4Since September 2026: 4% → 8%, +4 points. Inside the 11-point floor: within noise. Read over the models both editions asked. | Within noise | Rank 9 → 5 of 107 |
Shares here are read over the models both editions asked, so they can differ by a point or two from the standing above, which counts every model in this edition.
The floor is 11 points of share, measured: how far the models move a leader on their own when the same questions are asked twice with nothing changed. A larger change is movement; a smaller one is noise, and both are shown. Movement is read over the twelve models both editions asked; GPT-6 Luna, Muse Glimmer 30B joined this edition and are in the standing but not yet in the comparison. How the floor is measured · The editions
By model
How each model treated Google Colab across every prompt where it was named for a mid-market B2B company. Fourteen models, six prompts per category.ShowHide
By model
How each model treated Google Colab across every prompt where it was named for a mid-market B2B company. Fourteen models, six prompts per category.
ShowHide| Model | First choice | Alternative | Mention | Negative | Labels |
|---|---|---|---|---|---|
| Claude Haiku 4.5 | 0 | 1 | 0 | 0 | 1 |
| GPT-5.4 mini | 0 | 0 | 0 | 0 | 0 |
| Gemini 3.5 Flash | 0 | 1 | 0 | 0 | 1 |
| Perplexity Sonar | 0 | 0 | 1 | 0 | 1 |
| Grok 4.20 | 1 | 0 | 1 | 0 | 2 |
| Mistral Small | 0 | 0 | 0 | 0 | 0 |
| DeepSeek V4 Flash | 1 | 0 | 0 | 0 | 1 |
| Llama 4 Maverick | 0 | 1 | 0 | 0 | 1 |
| Qwen 3.7 Flash | 1 | 0 | 0 | 0 | 1 |
| Kimi K2 | 0 | 1 | 0 | 0 | 1 |
| GLM 4.7 FlashX | 1 | 0 | 1 | 0 | 2 |
| MiniMax M2.5 | 0 | 0 | 0 | 0 | 0 |
| GPT-6 Luna | 0 | 0 | 0 | 0 | 0 |
| Muse Glimmer 30B | 1 | 0 | 0 | 0 | 1 |
By framing
Which of the six questions produced the naming. By model says how often; this says asked what. The first-choice count on the right carries the marks of the models that produced it.
FramingLabels by classFirst choices
Direct0 labelsNone
Paraphrase0 labelsNone
Comparative1 labelNone
Budget-constrained18 labels11
Scale-constrained0 labelsNone
Negative1 labelNone
First choiceAlternativeMentionNegative20 labels in all, every segment counted; 11 of the 11 first choices count toward share, since the comparative and negative framings do not. The bar is one segment per label class, to scale within the framing.
What the models said for it
Verbatim evidence the judge attached to positive labels.
“Best Overall Free Option: Google Colab ... Google Colab + TensorFlow/PyTorch is the best combination of zero upfront cost and flexibility.” Qwen 3.7 Flash · ML platforms · budget prompt · first choice
“Starts at $0 (free tier), which is why everyone uses it... Hard to beat for learning and small projects.” Muse Glimmer 30B · ML platforms · budget prompt · first choice
“Google Colab stands out as the best machine learning platform for a company with a limited budget.” Grok 4.20 · ML platforms · budget prompt · first choice
“Google Colab + Vertex AI is arguably the best budget combination” DeepSeek V4 Flash · ML platforms · budget prompt · first choice
And against it
Verbatim evidence attached to negative labels. A warning on a product with few labels is a warning; on a product with many, it is one voice among them.
No model argued against it.
Named alongside
The products named in the same answers as Google Colab, over the 20 answers that named it. Took the first choice instead counts the answers where the other product was the first choice and Google Colab was named but was not.ShowHide
Named alongside
The products named in the same answers as Google Colab, over the 20 answers that named it. Took the first choice instead counts the answers where the other product was the first choice and Google Colab was named but was not.
ShowHideProductSame answerTook the first choice insteadHead to head
A head-to-head page exists where both products are among a category's top eight. The other rows are the same fact without a page behind them, so they link to the product instead.
What carried it into the answer
The sites and pages cited by the answers that named Google Colab. A fact about retrieval, not a lever on the model.
Citations exist only for the models that return a source list, five of the fourteen in this edition, so these counts come from 15 of the 20 answers that named Google Colab and are not a share of its labels.
Domains cited
eweek.com11
g2.com11
checkthat.ai9
byteplus.com7
spotsaas.com7
us.fitgap.com6
deepusecase.com5
it-ai-index.com5
sourceforge.net5
guideflow.com4
Seventy of the seventy domain citations in answers naming Google Colab came from somebody else's page.
Pages cited
Pages are listed as the models cited them.
Search and answers
colab.research.google.com is not in Google's top ten for any of the category's searches. In the answers, Google Colab takes 9% of first choices and Azure Machine Learning takes 21%.ShowHide
Search and answers
colab.research.google.com is not in Google's top ten for any of the category's searches. In the answers, Google Colab takes 9% of first choices and Azure Machine Learning takes 21%.
ShowHideIn search
Google, US estimatesPosition for “machine learning platform”
–
not in the top ten
Position for “machine learning platforms”
–
not in the top ten
- Searches for its name, Google
- 301,000 a month (“google colab”)
- AI search demand for its name, est.
- 900 a month
- Organic visits to its site
- about 240,528 a month
- Searches its site ranks for
- 19,371 · 658 in the top three
- Sites linking to it
- 69,110
- Paid Google search
- No ads found in the estimate; that does not mean it runs none
Ads on Google
None found in Google's ad transparency records for the US
In answers
This editionShare of first choices
9%
rank 5 of 107 in ML platforms
Segment leader
21%
Azure Machine Learning
- First choices
- 11 across its categories
- Named in
- 20 answers
- Its own site cited
- in 15 of the answers that named it
Search figures are US estimates from DataForSEO, read September 28, 2026; AI search demand is its modeled, directional estimate, not a count of queries to any assistant. The answers are this edition's. Two measurements side by side: neither is read as the cause of the other.
Names read as Google Colab
What the judge wrote, as written, with how often. The vendor table decides that these count as Google Colab; a claim can dispute any of them.
Colab 1Google Colab Pro 1
Follow Google Colab
An email the morning each edition publishes: where this product moved, where it held, and by how much against the noise floor. One address, confirmed by a click; a stop link in every email.
Already following? Everything you follow, with a stop for each.
Is this your product?
Claim this page
Claiming is free and changes nothing in the data. A claimed page shows a verified contact who is told when each edition publishes and when Google Colab's standing changes by more than the noise floor; the right to propose corrections to the vendor table, meaning names the judge wrote that should or should not read as Google Colab, applied by version and listed in the change log; and a one-line description supplied by the vendor and marked as such.
What a new claim receivesHide what a new claim receives
A new claim receives the current edition's vendor brief for Google Colab by email, built from the raw record of the edition. It shows:
- where Google Colab is named, by buyer and by framing, and which cells hold its first choices;
- the claims the models make when they name it, ranked, with the strongest and the weakest quoted;
- its vocabulary against the segment leader's, and the pages the models cited;
- who was chosen in the answers that did not name Google Colab, and every reason the record gives;
- a battlecard for each top rival: the head-to-head split, why they win, and the reservation quoted against them;
- one page of published figures cleared to show a buyer.