Machine Relations Index

AI Infrastructure · Machine Relations Index category

Which sources AI engines cite for AI Infrastructure questions

Vendor-owned sources earn 33.9% of classified citations in AI Infrastructure answers (999 of 2,950), as of November 2, 2026.

In the November 2026 release of the Machine Relations Index, these are the sources AI answer engines cited most often for each of the 6 AI Infrastructure question shapes, each ranked on that shape's own observed runs. This category has 6 observed question-shape segments in the current public release. Each segment keeps its own observed-run denominator and dates; this page does not average rates across shapes. Observed runs are answer runs, not questions: every segment repeats a small set of distinct questions across engines and dates, so a run count is not a count of independent questions.

Top sources by question shape

The sources engines cite most, shape by shape

A rate is the share of the shape's observed answer runs that cited the source; rank is the source's place on that shape's full leaderboard.

Best tools

Example pattern: best AI infrastructure tools. 251 observed runs across 13 dates.

RankSourceSource roleCitation rateCited runs
#1 of 364MediumCommunity and social platform27.49%69 of 251
#2 of 364Arxiv.orgAcademic and government source20.72%52 of 251
#3 of 364Firecrawl.devOther observed source20.32%51 of 251
#4 of 364Salttechno.aiOther observed source11.16%28 of 251
#5 of 364YouTubeSearch or media platform11.16%28 of 251

Full Best tools leaderboard, 364 sources

How buyers choose

Example pattern: how to choose AI infrastructure tools. 270 observed runs across 13 dates.

RankSourceSource roleCitation rateCited runs
#1 of 416Arxiv.orgAcademic and government source22.22%60 of 270
#2 of 416MediumCommunity and social platform15.19%41 of 270
#3 of 416Gmicloud.aiOther observed source12.59%34 of 270
#4 of 416YouTubeSearch or media platform11.85%32 of 270
#5 of 416Github.comCommunity and social platform10.74%29 of 270

Full How buyers choose leaderboard, 416 sources

Is it worth it

Example pattern: is AI infrastructure software worth it. 269 observed runs across 13 dates.

RankSourceSource roleCitation rateCited runs
#1 of 404Atlan.comVendor-owned source15.61%42 of 269
#2 of 404MediumCommunity and social platform15.24%41 of 269
#3 of 404RedditCommunity and social platform14.13%38 of 269
#4 of 404Gmicloud.aiOther observed source12.27%33 of 269
#5 of 404Arxiv.orgAcademic and government source11.15%30 of 269

Full Is it worth it leaderboard, 404 sources

Problem-first research

Example pattern: how to solve AI infrastructure problems. 269 observed runs across 13 dates.

RankSourceSource roleCitation rateCited runs
#1 of 459Arxiv.orgAcademic and government source12.64%34 of 269
#2 of 459Dev.toCommunity and social platform11.52%31 of 269
#3 of 459RedditCommunity and social platform10.41%28 of 269
#4 of 459LinkedInCommunity and social platform8.92%24 of 269
#5 of 459Openai.comVendor-owned source8.55%23 of 269

Full Problem-first research leaderboard, 459 sources

Top lists

Example pattern: top AI infrastructure platforms. 272 observed runs across 13 dates.

RankSourceSource roleCitation rateCited runs
#1 of 440Spheron.networkOther observed source29.78%81 of 272
#2 of 440Nvidia.comVendor-owned source15.44%42 of 272
#3 of 440Signoz.ioOther observed source13.24%36 of 272
#4 of 440Cloudzero.comOther observed source11.76%32 of 272
#5 of 440Introl.comOther observed source11.76%32 of 272

Full Top lists leaderboard, 440 sources

Comparisons

Example pattern: AI infrastructure platform A vs B. 270 observed runs across 13 dates.

RankSourceSource roleCitation rateCited runs
#1 of 360Databricks.comVendor-owned source22.96%62 of 270
#2 of 360Ibm.comVendor-owned source18.89%51 of 270
#3 of 360YouTubeSearch or media platform14.44%39 of 270
#4 of 360Arxiv.orgAcademic and government source14.07%38 of 270
#5 of 360Gmicloud.aiOther observed source11.85%32 of 270

Full Comparisons leaderboard, 360 sources

Observed shapes6

Canonical segment URLs exist only for observed category/shape pairs.

Published6

Published segments met the release evidence floor.

Collecting0

Collecting segments expose sample progress, not mature leaderboards.

ReleaseNovember 2, 2026

Methodology v2.1.

Question shapes

Separate denominators by segment

Question shapeStateObserved runsDistinct questionsRun datesDistinct cited domainsCanonical segment
Best toolsPublished2517 (5 paraphrase groups)13364Open leaderboard
How buyers choosePublished2707 (5 paraphrase groups)13416Open leaderboard
Is it worth itPublished2697 (5 paraphrase groups)13404Open leaderboard
Problem-first researchPublished2697 (5 paraphrase groups)13459Open leaderboard
Top listsPublished2727 (5 paraphrase groups)13440Open leaderboard
ComparisonsPublished2707 (5 paraphrase groups)13360Open leaderboard

By answer engine

Whole-window rates on each engine

Each link ranks sources with a published AI Infrastructure segment by that source's citation rate on one engine across the whole window. It is not a rate inside AI Infrastructure alone. A collecting engine has no published ranking for this category.
Cite this
Citation
Machine Relations Index (MRI), Machine Relations Research — machinerelations.ai. AI Infrastructure, observed Machine Relations Index category, 2026-05-10 to 2026-11-02. Release mri_score_v2.1+2026-11-02+f9aaeca0db15. https://machinerelations.ai/index/categories/ai-infrastructure. CC BY 4.0.
APA
Parrott, J. (2026). AI Infrastructure in the Machine Relations Index (Version mri_score_v2.1+2026-11-02+f9aaeca0db15) [Data set]. Machine Relations Research. https://machinerelations.ai/index/categories/ai-infrastructure https://doi.org/10.5281/zenodo.23652423
BibTeX
@misc{parrott2026mriaiinfrastructure,
  author = {Parrott, Jaxon},
  title = {AI Infrastructure in the Machine Relations Index},
  year = {2026},
  version = {mri\_score\_v2.1+2026-11-02+f9aaeca0db15},
  publisher = {Machine Relations Research},
  url = {https://machinerelations.ai/index/categories/ai-infrastructure},
  doi = {10.5281/zenodo.23652423},
  license = {CC-BY-4.0},
  note = {Machine Relations Index (MRI), Machine Relations Research — machinerelations.ai}
}

CITATION.cff

Release and boundary

No pooled category score

Published November 2, 2026. The observation window runs May 10, 2026 to November 2, 2026. Methodology v2.1.

Use the data
curl -fsSL "https://machinerelations.ai/data/machine-relations-index.json" -o machine-relations-index.json
curl -fsSL "https://machinerelations.ai/data/mri-release-manifest.json" -o mri-release-manifest.json
curl -fsSL "https://huggingface.co/datasets/machine-relations/machine-relations-index/resolve/main/domains.csv" -o domains.csv
curl -fsSL -L "https://doi.org/10.5281/zenodo.23652423" -o mri-zenodo.html
import json
from urllib.request import urlopen

index_url = "https://machinerelations.ai/data/machine-relations-index.json"
with urlopen(index_url) as response:
    index = json.load(response)
print(index["generated"], index["total_domains"])

This page is a directory of observed segments. A domain's overall citation rate is not a category rate, and MRI does not publish an averaged category score in this slice.

AI Infrastructure buyers on AuthorityTech’s publication intelligence is a downstream practitioner projection of these segments. It is not source evidence, citation provenance, or an identity link for the neutral dataset.