Small business▲+4Since September 2026: 0% → 4%, +4 points. Inside the 11-point floor: within noise. Read over the models both editions asked.
4%from 0% in September 2026
Inside the floor by 7 pointsRank 23 → 8 of 131
2 categories · RankedHugging Face
29Judge labels
3First choices
6Negative labels
12 / 14Models named it
2Categories
October 2026 Edition. Every number here is derived from the raw labels under vendor table v2026-10.8, every buyer segment counted.
Best standing
5% in ML platforms for small business buyers
Rank 26 of 107 in the mid-market standingcriticized challenger
0 of 14 models made it the first choice on the direct prompt; 27% of its 11 labels there were negative.
What the models named instead of Hugging Face →By buyer segmentRead the same way at every buyer size.
Small business8 of 139
5%14 labels
accepted challenger
Mid-market26 of 107
0%11 labels
criticized challenger
Enterprise46 of 65
0%2 labels
under the label threshold
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 Hugging Face 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 | 0% | 26 of 107 | 27% | 11 | criticized challenger | ▼−2Since September 2026: 2% → 0%, −2 points. Inside the 11-point floor: within noise. Read over the models both editions asked. |
| AI security platforms | Security operations | 0% | 105 of 233 | 0% | 1 | under 10 labels · led by SentinelOne Singularity at 11% |
Movement
Since September 2026, Hugging Face held within the floor in all two categories: no change cleared 11 points.
Best category · ML platforms · by segment
Mid-market▼−2Since September 2026: 2% → 0%, −2 points. Inside the 11-point floor: within noise. Read over the models both editions asked.
0%from 2% in September 2026
Inside the floor by 9 pointsRank 14 → 24 of 97
Enterprise=heldSince September 2026: 0% → 0%, ±0 points. Inside the 11-point floor: within noise. Read over the models both editions asked.
0%from 0% in September 2026
Inside the floor by 11 pointsRank 45 → 40 of 55
All categories · Mid-market
| Category | September 2026 | Now | Change | Reading | Rank |
|---|---|---|---|---|---|
| ML platforms | 2% | 0% | ▼−2Since September 2026: 2% → 0%, −2 points. Inside the 11-point floor: within noise. Read over the models both editions asked. | Within noise | Rank 14 → 26 of 107 |
| AI security | 0% | 0% | newNew since September 2026: not ranked then, 0% now. | New this edition | Rank 105 of 233, unchanged |
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 Hugging Face 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 Hugging Face 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 | 0 | 1 | 0 | 1 |
| GPT-5.4 mini | 0 | 0 | 0 | 0 | 0 |
| Gemini 3.5 Flash | 0 | 0 | 1 | 0 | 1 |
| Perplexity Sonar | 0 | 0 | 0 | 0 | 0 |
| Grok 4.20 | 0 | 0 | 0 | 1 | 1 |
| Mistral Small | 0 | 0 | 0 | 0 | 0 |
| DeepSeek V4 Flash | 0 | 1 | 1 | 0 | 2 |
| Llama 4 Maverick | 0 | 0 | 0 | 0 | 0 |
| Qwen 3.7 Flash | 0 | 1 | 1 | 0 | 2 |
| Kimi K2 | 0 | 1 | 0 | 0 | 1 |
| GLM 4.7 FlashX | 0 | 1 | 0 | 0 | 1 |
| MiniMax M2.5 | 0 | 0 | 0 | 1 | 1 |
| GPT-6 Luna | 0 | 1 | 0 | 0 | 1 |
| Muse Glimmer 30B | 0 | 0 | 0 | 1 | 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
Direct1 label1
Paraphrase1 label1
Comparative11 labelsNone
Budget-constrained6 labels1
Scale-constrained1 labelNone
Negative9 labelsNone
First choiceAlternativeMentionNegative29 labels in all, every segment counted; 3 of the 3 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.
“A standout for the open-model ecosystem and deployment, but it isn't a like-for-like replacement for a full cloud data-and-MLOps platform.” GPT-6 Luna · ML platforms · comparative prompt · alternative
“For Compliance & Risk: IBM watsonx or Hugging Face (with integrations) for governance and audit trails.” Qwen 3.7 Flash · AI security · comparative prompt · alternative
“Open-source frameworks (scikit-learn, PyTorch, TensorFlow, Hugging Face)” Kimi K2 · ML platforms · negative prompt · alternative
“Consider open-source tools like MLflow, Ray, or Hugging Face instead” DeepSeek V4 Flash · ML platforms · negative prompt · alternative
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.
“a vulnerability class that has been repeatedly exploited in the wild on Hugging Face since at least March 2024” Muse Glimmer 30B · ML platforms · negative prompt · soft negative
“Recent breaches ... risky for sensitive data due to supply chain vulnerabilities” Grok 4.20 · ML platforms · negative prompt · soft negative
“Hugging Face: Critical security flaws” MiniMax M2.5 · ML platforms · negative prompt · soft negative
Named alongside
The products named in the same answers as Hugging Face, over the 29 answers that named it. Took the first choice instead counts the answers where the other product was the first choice and Hugging Face was named but was not.ShowHide
Named alongside
The products named in the same answers as Hugging Face, over the 29 answers that named it. Took the first choice instead counts the answers where the other product was the first choice and Hugging Face 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 Hugging Face. 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 23 of the 29 answers that named Hugging Face and are not a share of its labels.
Domains cited
checkthat.ai9
g2.com9
learn.g2.com8
devopsschool.com6
huggingface.coYour site6
it-ai-index.com6
linkedin.com6
deepusecase.com5
igmguru.com5
thirstysprout.com5
Fifty-nine of the sixty-five domain citations in answers naming Hugging Face came from somebody else's page.
Pages cited
Pages are listed as the models cited them.
Search and answers
huggingface.co is not in Google's top ten for any of the category's searches. In the answers, Hugging Face takes 0% of first choices and Azure Machine Learning takes 21%.ShowHide
Search and answers
huggingface.co is not in Google's top ten for any of the category's searches. In the answers, Hugging Face takes 0% 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
- 135,000 a month (“hugging face”)
- AI search demand for its name, est.
- 1,844 a month
- Organic visits to its site
- about 1,133,354 a month
- Searches its site ranks for
- 88,440 · 12,863 in the top three
- Sites linking to it
- 134,182
- Paid Google search
- No ads found in the estimate; that does not mean it runs none
In answers
This editionShare of first choices
0%
rank 26 of 107 in ML platforms
Segment leader
21%
Azure Machine Learning
- First choices
- 3 across its categories
- Named in
- 29 answers
- Its own site cited
- in 23 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.
What it publishes
ShowHide
What it publishes
ShowHideAddresses on huggingface.co
27,007
subdomains included
Content
1,054
counted in the table below
Documentation
54
Product · Integration
1 · 4
| Kind | Pages | Last 90 days | 2025-10 to 2026-09 | Latest | Categories named |
|---|---|---|---|---|---|
| Blog | 882 | 3 | 2026-09-29 | ||
| Comparison | 42 | 3 | 2026-09-24 | ||
| Conference or event | 27 | 0 | 2022-12-20 | ||
| Template or tool | 27 | undated | |||
| Report or ebook | 23 | undated | |||
| Case study | 18 | 0 | 2025-06-22 | ||
| News or press | 18 | 2 | 2026-09-23 | ||
| Glossary or explainer | 13 | 1 | 2026-09-21 | ||
| Podcast or video | 3 | undated | |||
| Webinar or virtual event | 1 | undated |
Most recent
- 2026-09-29Community blog
- 2026-09-24Jev vs laya hosted api or open weights 2026 guide comparison
- 2026-09-23Nemotron diarization blog
- 2026-09-23Likes news or press
- 2026-09-23All news or press
- 2026-09-23Jev ai vs djev vs laya vs openjev vs semif which d comparison
How it is countedHide how it is counted
Every page huggingface.co exposes, subdomains included. Kind is read from the address and title. The last 90 days, the latest date and the twelve months count pages by when they were published, from the site's feeds, a date in the address, or the page's own publication date, read from up to a hundred of its most recently changed pages; a page that says only when it last changed is counted in its kind but not in when, so the recent counts are a floor, and a kind none of whose pages gives a publication date reads undated. An event counts as online when its address or title says so (webinar, on demand, virtual or online summit); a conference, summit, trade show, expo or roadshow that does not say so is counted as a conference or event, which on a vendor's site is mostly in person. Read September 29, 2026.
Names read as Hugging Face
What the judge wrote, as written, with how often. The vendor table decides that these count as Hugging Face; a claim can dispute any of them.
Hugging Face Hub and Inference Endpoints 1
Follow Hugging Face
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.
The company
Hugging Face is its own company.ShowHide
The company
Hugging Face is its own company.
ShowHideIn its own words
Stated by the vendor, not checked- Positioning
- The AI community building the future.
- For
- The machine learning community
- Price stated
- Starting at $20/user/month; Starting at $0.60/hour for GPU huggingface.co
- Starting price
- 20/user/month huggingface.co
- Hosting
- US
- Customers named
- Ai2AI at MetaAmazonGoogleIntelMicrosoftGrammarlyWriter
- Not stated on the pages read
- Free plan or trial, integrations, certifications
What Hugging Face's own pages state, read September 28, 2026: huggingface.co, huggingface.co/pricing, huggingface.co/datasets/Pixeltable/platform-comparison-fixtures. A claimed page can correct any of them.
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 Hugging Face'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 Hugging Face, 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 Hugging Face by email, built from the raw record of the edition. It shows:
- where Hugging Face 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 Hugging Face, 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.