CitableHub
CitableHub
Answer-first summary52/100 GEO

Modal

Serverless cloud for AI workloads.

AI InfrastructureFair citabilityVisit site →

Your Citability Score

52/100
Identity75/100
Evidence45/100
Trust15/100
Freshness50/100
Classification80/100
What to improve to rank higher
  • Identity: Add: Founder / Team, Social links.
  • Evidence: Add: At least one evidence link, Multiple evidence sources, Demo URL.
  • Trust: Add: Contact information, Support URL, Privacy policy, Terms of service.
  • Freshness: Add: Has version number, Launch date provided, Reviewed within 90 days.
  • Classification: Add: At least 2 tags.

Promotion (Boost) does not change this score — it only changes ordering. This number reflects real, verifiable citability.

Frequently asked questions

What is Modal?
Serverless cloud for AI workloads.
What does Modal do?
Serverless cloud platform optimized for running AI/ML workloads with GPU access, auto-scaling, and simple Python interface.
Who is Modal for?
ML engineers, AI application developers, and data teams building production model inference, training, and batch pipelines.
Is Modal verified?
Modal is listed on CitableHub with a citability score of 52/100, computed from verifiable profile evidence.
Modal logo

Modal

Invited

Serverless cloud for AI workloads.

AI InfrastructureCH-VER-967498Listed September 11, 2026
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AI-Extractable Summary
What:Serverless cloud for AI workloads.
For whom:ML engineers, AI application developers, and data teams building production model inference, training, and batch pipelines
Key outcome:Deploy and scale AI workloads in minutes while reducing infrastructure management and idle compute costs
Category:AI Infrastructure

Structured for AI systems to extract and cite.

Citability Score

52/100
75
Identity
45
Evidence
15
Trust
50
Freshness
80
Classification
52
Impressions
0
Clicks
0
Likes
0
GQI Earned

Citable Outcome

Deploy and scale AI workloads in minutes while reducing infrastructure management and idle compute costs.

About

Serverless cloud platform optimized for running AI/ML workloads with GPU access, auto-scaling, and simple Python interface.

Target Audience: ML engineers, AI application developers, and data teams building production model inference, training, and batch pipelines.
Not ideal for: Teams that need a no-code AI builder or simple static hosting rather than programmable infrastructure for custom workloads.

What makes it different

  • Python-first developer experience for AI and data workloads
  • Serverless access to GPUs and CPUs with scale-to-zero billing
  • Runs containerized workloads without managing servers, clusters, or Kubernetes
  • One platform for inference, training, batch jobs, and scheduled pipelines

Tags & Classification

model inferencegpu trainingllm servingbatch processingscheduled jobs
ml engineersai developersdata scientistsplatform engineers
softwarehealthcarefinancial servicesecommerce
Platform: PlatformModel: Developer Tool

Links & Transparency

Website

Cite this Project

BibTeX
@misc{citablehub_modal,
  title = {Modal},
  url = {https://citablehub.com/p/modal},
  note = {Listed September 11, 2026. CitableHub ID: CH-VER-967498},
  year = {2026}
}
APA
Modal. (2026). CitableHub Software Index. https://citablehub.com/p/modal.
MLA
"Modal." CitableHub, 2026, https://citablehub.com/p/modal.