Mobagel

MoBagel is an AI and automated machine learning (AutoML) company founded in 2015 and headquartered in San Jose, California, with offices across seven global locations. Its flagship Decanter AI platform lets business users build machine learning models and generate predictions without a data science background, spanning AutoML, automated time-series forecasting (AutoTSF), MLOps, DataOps, and a generative-BI suite (Generative BI, Generative ETL, Generative Expert) now surfaced through the 8ndpoint product and a family of vertical AI agents for marketing, supply chain, finance, and manufacturing. MoBagel exposes Decanter AI programmatically through an official first-party Python SDK (decanter-ai-sdk) that authenticates with an API key and drives data upload, model training, and prediction. Backed by 500 Global.

Mobagel is profiled on the APIs.io network. Tagged areas include Company, Artificial Intelligence, Machine-Learning, AutoML, and Data Science.

Mobagel’s developer surface includes support and 8 more developer resources.

🌐 Visit website 📡 Source on GitHub

13.2/100 emerging ▬ flat Agent 0/100 human only saas Full breakdown ↓
scored 2026-09-08 · rubric v0.20.0

0 APIs

Company Artificial Intelligence Machine-Learning AutoML Data Science Predictive Analytics Business Intelligence MLOps

Kin Score

Kin Score How this is scored →
scored 2026-09-08 · rubric v0.20.0

Composite quality — 13.2/100 · emerging

Contract Quality 0.0 / 25

Developer Ergonomics 2.4 / 20

Access Clarity 5.5 / 20

Operational Transparency 0.3 / 13

Contract Governance 0.0 / 12

Discoverability 5.0 / 10

Agent readiness — 0/100 · human only

Machine-Readable Contract 0 / 18

Agentic Access Contract 0 / 10

Documented Reversibility 0 / 6

MCP Server 0 / 12

Machine-Readable Auth 0 / 10

Idempotency 0 / 9

Stable Error Semantics 0 / 8

Request/Response Examples 0 / 7

Rate-Limit Signaling 0 / 7

Typed Event Surface 0 / 6

Agent Skills 0 / 5

Well-Known Catalog 0 / 4

Consent & Bot Identity 0 / 3

A2A Agent Card 0 / 8

Dry-Run / Simulate Mode 0 / 4

Delegated User Identity 0 / 6

Protected Resource Metadata 0 / 5

Registration Without a Human 0 / 6

Agentic Commerce Well-Known Document 0 / 5

Create-or-Update Ergonomics could not be measured. We hold no machine-readable contract for this provider to read, so there is nothing to measure a write surface against. Excluded rather than scored zero: never-measured and measured-empty are different facts. Publishing an OpenAPI is what makes this facet — and several others — scorable at all.

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Security Posture 1

Authentication, domain security, vulnerability disclosure, and trust-center signals.

Mobagel Domain Security

TLSv1.3 · HSTS · DMARC

SECURITY

Resources

Get Started 1

Portal, sign-up, and the first successful call

Login

Build 3

SDKs, sample code, and the tooling you integrate with

Packages

Packages

SDKs

SDKs

GitHubOrganization

GitHubOrganization

Access & Security 1

Authentication, authorization, and security posture

DomainSecurity

DomainSecurity

Operate 1

Status, limits, changes, and where to get help

Support

Support

Commercial 2

Pricing, plans, and the legal terms of use

TermsOfService

TermsOfService

PrivacyPolicy

PrivacyPolicy

Company 1

The organization behind the API

Website

Website

Source (apis.yml)

aid: mobagel
name: Mobagel
description: MoBagel is an AI and automated machine learning (AutoML) company founded in 2015 and headquartered in San Jose,
  California, with offices across seven global locations. Its flagship Decanter AI platform lets business users build machine
  learning models and generate predictions without a data science background, spanning AutoML, automated time-series forecasting
  (AutoTSF), MLOps, DataOps, and a generative-BI suite (Generative BI, Generative ETL, Generative Expert) now surfaced through
  the 8ndpoint product and a family of vertical AI agents for marketing, supply chain, finance, and manufacturing. MoBagel
  exposes Decanter AI programmatically through an official first-party Python SDK (decanter-ai-sdk) that authenticates with
  an API key and drives data upload, model training, and prediction. Backed by 500 Global.
url: https://raw.githubusercontent.com/api-evangelist/mobagel/refs/heads/main/apis.yml
x-type: company
x-source: vc-portfolio
x-backed-by:
- 500-global
x-tier: stub
x-tier-reason: portfolio-lead
deliveryModel:
  model: saas
  open_source: false
  commercial: true
  callable_host: false
  label: Hosted service · you call their endpoint
  confidence: medium
  source:
  - pricing
  generated: '2026-08-28'
  method: derived
accessModel:
  pricing: unknown
  onboarding: unknown
  trial: false
  try_now: false
  public: false
  label: Unknown
  confidence: low
  source: []
  generated: '2026-07-22'
  method: derived
specificationVersion: '0.23'
created: '2026-07-17'
modified: '2026-07-20'
tags:
- Company
- Artificial Intelligence
- Machine-Learning
- AutoML
- Data Science
- Predictive Analytics
- Business Intelligence
- MLOps
tags_raw:
- Company
- Artificial Intelligence
- Machine Learning
- AutoML
- Data Science
- Predictive Analytics
- Business Intelligence
- MLOps
image: https://cdn.prod.website-files.com/67205451fb764a57c45b16fd/6837ce4db49cc91f524bfb72_logo%20m.png
apis: []
maintainers:
- FN: Kin Lane
  email: kin@apievangelist.com
- FN: APIs.json
  email: info@apis.io
common:
- type: DomainSecurity
  url: security/mobagel-domain-security.yml
- type: Website
  url: https://mobagel.com
- type: Packages
  url: packages/mobagel-packages.yml
- type: SDKs
  url: packages/mobagel-packages.yml
- type: GitHubOrganization
  url: https://github.com/MoBagel
- type: Login
  url: https://app.8ndpoint.com
- type: Support
  url: https://us.mobagel.com/contact
- type: TermsOfService
  url: https://us.mobagel.com/terms-of-service
- type: PrivacyPolicy
  url: https://us.mobagel.com/privacy-policy
x-enrichment:
  date: '2026-07-20'
  status: enriched
  artifacts_added: 2
  pass: local-v1

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