Kubeflow website screenshot

Kubeflow

Kubeflow is an open-source machine learning platform for Kubernetes, designed to make deployments of ML workflows on Kubernetes simple, portable, and scalable. It provides tools for training, serving, tuning, and managing ML models across the full lifecycle.

Kubeflow publishes 4 APIs on the APIs.io network, including Experiments API, Health API, Pipelines API, and 1 more. Tagged areas include Artificial Intelligence, Deep Learning, Kubernetes, Machine-Learning, and MLOps.

Kubeflow’s developer surface includes authentication, documentation, getting-started guide, engineering blog, and 13 more developer resources.

🌐 Visit website 📡 Source on GitHub
34.9/100 thin ▬ flat Agent 23/100 agent aware self hosted · Apache-2.0 Full breakdown ↓
scored 2026-09-14 · rubric v0.22.0
AccessFreemiumSelf serve⚡ Free to try
1 APIs 1 MCP Servers
Artificial IntelligenceDeep LearningKubernetesMachine-LearningMLOpsModel ServingModel TrainingOpen-Source

Kin Score

Kin Score Kin Score How this is scored →
scored 2026-09-14 · rubric v0.22.0
Composite quality — 34.9/100 · thin
Contract Quality 9.5 / 20
Developer Ergonomics 6.1 / 16
Access Clarity 2.5 / 16
Operational Transparency 3.0 / 10
Contract Governance 0.0 / 10
Discoverability 4.7 / 8
Open Source Surface 6.5 / 10
Create-or-Update Ergonomics 0.0 / 10
Agent readiness — 23/100 · agent aware
Machine-Readable Contract 18 / 18
Agentic Access Contract 10 / 10
Documented Reversibility 0 / 6
MCP Server 12 / 12
Machine-Readable Auth 10 / 10
Idempotency 0 / 9
Stable Error Semantics 0 / 8
Request/Response Examples 0 / 7
Rate-Limit Signaling 7 / 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
Open Source Surface applies to this provider. This product is open source and we read its repository directly, so Open Source Surface carries 10 points of the composite. It is scored from what the repository actually publishes — a security policy, a contribution guide, a release history, a code of conduct — read live from the provider rather than inferred from our own catalog pointers. This facet adds; nothing was taken away to make room for it. An open-source project is not excused from the commercial facets, because exemption would strip it of the points it does earn. If we have the wrong repository, or this product is not open source, say so on your provider repo and we will drop the facet rather than have you publish against it.
Create-or-Update Ergonomics applies to this provider. This API accepts writes, so it carries 10 points of the composite. It is scored from the published contracts themselves: whether a caller can create-or-update in one call, whether the write accepts a key the caller already holds, and whether the response says which branch ran. Without that, every write needs a search-and-branch in front of it, and the first time that check is skipped a duplicate record is created. Scored against the observed mean rather than raw — a provider at the catalog average is unchanged by this facet, not penalised by it.
The six quality facets above are damped to 80 points between them, because the conditional facet above carries the other 20. That is why each facet's contribution is shown against a damped maximum: raising a quality facet moves the composite by 80% of its nominal weight, not 100%. The full arithmetic is at apis.io/rating/.
Improve this rating by publishing the missing artifacts — every area above can be raised, and the full rubric is at apis.io/rating/. Every facet and dimension name above is a link: it opens that measurement's own page — what it means, the exact checks that feed it, how the whole catalog distributes on it, and the providers at the top of it. This rating is computed from github.com/api-evangelist/kubeflow: open an issue to ask a question, or submit a pull request to add artifacts. Submit an artifact on GitHub — free → Manage your own listing — the Influence plan, $499/mo →

APIs 8

Individual APIs this provider publishes, each with its own machine-readable definition.

Kubeflow Metadata API

API for tracking and managing metadata, artifacts, and lineage for ML workflows running on Kubeflow.

Katib API

Katib is the Kubeflow component for hyperparameter tuning, neural architecture search, and AutoML, exposing a Kubernetes-native API for defining and running tuning experiments.

Kubeflow Notebooks API

API for managing Jupyter notebook server instances within a Kubeflow cluster, providing isolated, browser-based development environments.

Kubeflow Central Dashboard API

API supporting the Kubeflow central dashboard and UI components, which provide a unified interface to all installed Kubeflow components.

Kubeflow Experiments API

Group runs and recurring jobs into logical experiments

Kubeflow Health API

Health and auth checks

Kubeflow Pipelines API

Manage pipeline definitions

Kubeflow PipelineVersions API

Manage versions of pipelines

Scroll for all 8

Open Collections 6

Open, tool-agnostic API collections (OpenAPI-derived and Bruno).

API Collection

OPEN COLLECTION

Kubeflow Pipelines Experiments API

OPEN COLLECTION

Kubeflow Pipelines Experiments Health API

OPEN COLLECTION

Kubeflow Experiments Pipelines API

OPEN COLLECTION

Kubeflow Pipelines Experiments PipelineVersions API

OPEN COLLECTION

Kubeflow Pipelines API

OPEN COLLECTION

MCP Servers 1

Model Context Protocol servers that expose these APIs to AI agents.

Kubeflow MCP Server

MCP SERVER

Pricing Plans 1

Published pricing tiers and plan structures.

Kubeflow Plans Pricing

3 plans

PLANS

Rate Limits 1

Documented rate limits and quota policies.

Kubeflow Rate Limits

5 limits

RATE LIMITS

FinOps 1

Cost, billing, and metering signals for API financial operations.

Kubeflow Finops

FINOPS

Security Posture 2

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

Kubeflow Authentication

http · 1 scheme

SECURITY

Kubeflow Domain Security

TLSv1.3 · HSTS

SECURITY

Agentic Access 1

Recommended x-agentic-access execution contracts for AI agents.

Kubeflow Agentic Access

13 operations · 5 acting

13 operations · 5 acting

AGENTIC

Resources

Get Started 1

Portal, sign-up, and the first successful call

GettingStarted
GettingStarted

Documentation 3

Reference material describing how the API behaves

MCPDocumentation
MCPDocumentation
 ContributionGuide
ContributionGuide
 Documentation
Documentation

Agent Surfaces 2

MCP servers, agent skills, and machine-readable catalogs

MCPServer
MCPServer
 AgenticAccess
AgenticAccess

Build 2

SDKs, sample code, and the tooling you integrate with

CodeOfConduct
CodeOfConduct
 GitHubOrg
GitHubOrg

Access & Security 2

Authentication, authorization, and security posture

DomainSecurity
DomainSecurity
 Authentication
Authentication

Operate 3

Status, limits, changes, and where to get help

IssueTracker
IssueTracker
 Releases
Releases
 Community
Community

Commercial 1

Pricing, plans, and the legal terms of use

License
License

Company 3

The organization behind the API

LinkedIn
LinkedIn
 Website
Website
 Blog
Blog

Source (apis.yml)

apis.yml Raw ↑ 
aid: kubeflow
name: Kubeflow
description: Kubeflow is an open-source machine learning platform for Kubernetes, designed to make deployments of ML workflows
  on Kubernetes simple, portable, and scalable. It provides tools for training, serving, tuning, and managing ML models across
  the full lifecycle.
type: Index
deliveryModel:
  model: self-hosted
  license: Apache-2.0
  open_source: true
  commercial: false
  callable_host: false
  label: Self-hosted open source · you run it yourself
  confidence: high
  source:
  - license
  - openapi
  generated: '2026-08-28'
  method: derived
accessModel:
  pricing: freemium
  onboarding: self-serve
  trial: false
  try_now: true
  public: false
  label: Freemium · Self-serve signup
  confidence: medium
  source:
  - plans
  - authentication
  - security
  generated: '2026-09-03'
  method: derived
image: https://kinlane-images.s3.amazonaws.com/shared/apis-json/icons/kubeflow.png
tags:
- Artificial Intelligence
- Deep Learning
- Kubernetes
- Machine-Learning
- MLOps
- Model Serving
- Model Training
- Open-Source
tags_raw:
- AI
- Deep Learning
- Kubernetes
- Machine Learning
- MLOps
- Model Serving
- Model Training
- Open Source
url: https://raw.githubusercontent.com/api-evangelist/kubeflow/refs/heads/main/apis.yml
created: '2024-01-15'
modified: '2026-08-15'
specificationVersion: '0.23'
apis:
- aid: kubeflow:metadata-api
  name: Kubeflow Metadata API
  description: API for tracking and managing metadata, artifacts, and lineage for ML workflows running on Kubeflow.
  humanURL: https://www.kubeflow.org/docs/components/pipelines/concepts/metadata/
  tags:
  - Artifacts
  - Metadata
  - ML Tracking
  properties:
  - type: Documentation
    url: https://www.kubeflow.org/docs/components/pipelines/concepts/metadata/
  - type: GitHubRepository
    url: https://github.com/google/ml-metadata
- aid: kubeflow:katib-api
  name: Katib API
  description: Katib is the Kubeflow component for hyperparameter tuning, neural architecture search, and AutoML, exposing
    a Kubernetes-native API for defining and running tuning experiments.
  humanURL: https://www.kubeflow.org/docs/components/katib/
  tags:
  - AutoML
  - Hyperparameter Tuning
  - Neural Architecture Search
  properties:
  - type: Documentation
    url: https://www.kubeflow.org/docs/components/katib/reference/
  - type: GitHubRepository
    url: https://github.com/kubeflow/katib
- aid: kubeflow:notebooks-api
  name: Kubeflow Notebooks API
  description: API for managing Jupyter notebook server instances within a Kubeflow cluster, providing isolated, browser-based
    development environments.
  humanURL: https://www.kubeflow.org/docs/components/notebooks/
  tags:
  - Development Environment
  - Jupyter
  - Notebooks
  properties:
  - type: Documentation
    url: https://www.kubeflow.org/docs/components/notebooks/
  - type: GitHubRepository
    url: https://github.com/kubeflow/kubeflow/tree/master/components/notebook-controller
- aid: kubeflow:central-dashboard
  name: Kubeflow Central Dashboard API
  description: API supporting the Kubeflow central dashboard and UI components, which provide a unified interface to all installed
    Kubeflow components.
  humanURL: https://www.kubeflow.org/docs/components/central-dash/
  tags:
  - Dashboard
  - Management
  - UI
  properties:
  - type: Documentation
    url: https://www.kubeflow.org/docs/components/central-dash/
  - type: GitHubRepository
    url: https://github.com/kubeflow/kubeflow/tree/master/components/centraldashboard
- aid: kubeflow:kubeflow-experiments-api
  name: Kubeflow Experiments API
  description: Group runs and recurring jobs into logical experiments
  humanURL: https://www.kubeflow.org/docs/components/pipelines/
  baseURL: https://your-kubeflow-instance/pipeline
  tags:
  - Experiments
  properties:
  - type: OpenAPI
    url: openapi/kubeflow-experiments-api-openapi.yml
  - type: Documentation
    url: https://www.kubeflow.org/docs/components/pipelines/v2/reference/api/kubeflow-pipeline-api-spec/
  - type: GitHubRepository
    url: https://github.com/kubeflow/pipelines
  - type: Documentation
    url: https://kserve.github.io/website/modelserving/v1beta1/
  - type: GitHubRepository
    url: https://github.com/kserve/kserve
- aid: kubeflow:kubeflow-health-api
  name: Kubeflow Health API
  description: Health and auth checks
  humanURL: https://www.kubeflow.org/docs/components/pipelines/
  baseURL: https://your-kubeflow-instance/pipeline
  tags:
  - Health
  properties:
  - type: OpenAPI
    url: openapi/kubeflow-health-api-openapi.yml
  - type: Documentation
    url: https://www.kubeflow.org/docs/components/pipelines/v2/reference/api/kubeflow-pipeline-api-spec/
  - type: GitHubRepository
    url: https://github.com/kubeflow/pipelines
  - type: Documentation
    url: https://kserve.github.io/website/modelserving/v1beta1/
  - type: GitHubRepository
    url: https://github.com/kserve/kserve
- aid: kubeflow:kubeflow-pipelines-api
  name: Kubeflow Pipelines API
  description: Manage pipeline definitions
  humanURL: https://www.kubeflow.org/docs/components/pipelines/
  baseURL: https://your-kubeflow-instance/pipeline
  tags:
  - Pipelines
  properties:
  - type: OpenAPI
    url: openapi/kubeflow-pipelines-api-openapi.yml
  - type: Documentation
    url: https://www.kubeflow.org/docs/components/pipelines/v2/reference/api/kubeflow-pipeline-api-spec/
  - type: GitHubRepository
    url: https://github.com/kubeflow/pipelines
  - type: Documentation
    url: https://kserve.github.io/website/modelserving/v1beta1/
  - type: GitHubRepository
    url: https://github.com/kserve/kserve
- aid: kubeflow:kubeflow-pipelineversions-api
  name: Kubeflow PipelineVersions API
  description: Manage versions of pipelines
  humanURL: https://www.kubeflow.org/docs/components/pipelines/
  baseURL: https://your-kubeflow-instance/pipeline
  tags:
  - PipelineVersions
  properties:
  - type: OpenAPI
    url: openapi/kubeflow-pipelineversions-api-openapi.yml
  - type: Documentation
    url: https://www.kubeflow.org/docs/components/pipelines/v2/reference/api/kubeflow-pipeline-api-spec/
  - type: GitHubRepository
    url: https://github.com/kubeflow/pipelines
  - type: Documentation
    url: https://kserve.github.io/website/modelserving/v1beta1/
  - type: GitHubRepository
    url: https://github.com/kserve/kserve
common:
- type: MCPServer
  name: Kubeflow MCP Server
  url: https://github.com/kubeflow/mcp-server
- type: MCPDocumentation
  name: Kubeflow MCP Server Documentation
  url: https://github.com/kubeflow/mcp-server/blob/main/README.md
- type: IssueTracker
  url: https://github.com/google/ml-metadata/issues
- type: Releases
  url: https://github.com/google/ml-metadata/releases
- type: CodeOfConduct
  url: https://github.com/google/.github/blob/master/CODE_OF_CONDUCT.md
- type: ContributionGuide
  url: https://github.com/google/ml-metadata/blob/master/CONTRIBUTING.md
- type: License
  name: Apache-2.0
  url: https://github.com/google/ml-metadata/blob/master/LICENSE
- type: AgenticAccess
  url: agentic-access/kubeflow-agentic-access.yml
- type: DomainSecurity
  url: security/kubeflow-domain-security.yml
- type: Authentication
  url: authentication/kubeflow-authentication.yml
- type: LinkedIn
  url: https://www.linkedin.com/company/kubeflow
- type: Website
  url: https://www.kubeflow.org
- type: Documentation
  url: https://www.kubeflow.org/docs/
- type: GettingStarted
  url: https://www.kubeflow.org/docs/started/
- type: Blog
  url: https://blog.kubeflow.org/
- type: GitHubOrg
  url: https://github.com/kubeflow
- type: Community
  url: https://www.kubeflow.org/docs/about/community/
maintainers:
- FN: Kin Lane
  email: kin@apievangelist.com

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