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 8 API contracts indexed on the APIs.io network, including Experiments API, Health API, Pipelines API, and 5 more. Tagged areas include Artificial Intelligence, Deep Learning, Kubernetes, Machine Learning, and MLOps.

The Kubeflow catalog on APIs.io includes 1 Spectral governance ruleset.

Kubeflow’s developer surface includes authentication, documentation, getting-started guide, engineering blog, and 17 more developer resources.

🌐 Visit website 📡 Profile data on GitHub

32.1/100 thin ▬ flat Agent 23/100 agent aware Front door AI 6/6 dominant Heartbeat 100/100 alive self hosted · Apache-2.0 Full breakdown ↓
scored 2026-11-13 · rubric v0.28.0 · Show this score on your site →

AccessFreemiumSelf serve⚡ Free to try

1 published contract 1 MCP Servers

Artificial Intelligence Deep Learning Kubernetes Machine Learning MLOps Model Serving Model Training Open Source

What this lets a business do 1

Business capabilities this provider's published APIs can perform, derived from its own contracts. Browse all capabilities →

Information & Data Management

BC-610

ExperimentsPipeline VersionsPipelines

POST /apis/v2beta1/pipelines POST /apis/v2beta1/experiments GET /apis/v2beta1/pipelines/{pipeline_id}/versions

Kin Score

Kin Score How this is scored →
scored 2026-11-13 · rubric v0.28.0

Composite quality — 32.1/100 · thin

Contract Quality 7.0 / 16

Developer Ergonomics 5.3 / 13

Access Clarity 0.8 / 13

Operational Transparency 2.2 / 8

Contract Governance 1.1 / 8

Discoverability 3.7 / 7

Regulatory Posture 0.8 / 15

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

Pagination 5 / 5

Distinct Operations 3 / 3

MCP Tool Quality 6 / 6

Regulatory Posture applies to this provider. Its tags matched the Horizontal (data, software, accessibility, platform) regime, so Regulatory Posture carries 15 points of the composite. If this regime is wrong for your business, say so on your provider repo — the applicability map is public and we will correct it.

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 65 points between them, because both conditional facets apply and carry 35 points together. That is why each facet's contribution is shown against a damped maximum: raising a quality facet moves the composite by 65% of its nominal weight, not 100%. The full arithmetic is at apis.io/rating/.

Show this score on your site.

It updates itself when the score changes. Paste this where you link to your API docs:

<a href="https://apis.io/providers/kubeflow/"><img src="https://apis.io/badge/kubeflow/card.svg" alt="Kubeflow on APIs.io — Kin Score by API Evangelist"></a>

Other styles, a README pill and a shields.io endpoint are on the badge page.

Raise your score.

Agent readiness — checks not met, largest first (exact, from this profile):

Idempotency +9 pts — OpenAPI mutating operations declare an idempotency key — a header, a request-body property, or a named de-duplication token whose own description says so (verified); else common[].type matches /idempoten/i (documented) Ask about this →
Stable Error Semantics +8 pts — OpenAPI $refs a shared error schema across 4xx/5xx responses (verified), else common[].type includes ErrorCatalog / ErrorCodes / DeclineCodes Ask about this →
A2A Agent Card +8 pts — a2a/-a2a.yml manifest, graded on conformance.grade against A2A 1.0.0 Ask about this →
Request/Response Examples +7 pts — share of operations carrying request/response examples; else an Examples / Code Samples link Ask about this →
Registration Without a Human +6 pts — a `registration_endpoint` in the served RFC 8414 / OIDC discovery document Ask about this →
Delegated User Identity +6 pts — a served discovery document listing authorization_code in grant_types_supported (served), else an oauth2 authorizationCode flow or openIdConnect declared in an OpenAPI (documented) Ask about this →

8 more unmet checks in the Agent Readiness table above.

Kin Score — facets with the most composite points still available (weight × headroom):

Access Clarity scores 6.4 — up to +18.7 composite points Ask about this →
Contract Quality scores 42.9 — up to +14.3 composite points Ask about this →
Developer Ergonomics scores 40.5 — up to +11.9 composite points Ask about this →

Each check links to what satisfies it. Publish the artifact, or tell us where it already lives, and the score moves on the next scoring run.

Not sure how this applies to your stack? Ask about any one item. Kin Lane reads every question, looks at your profile and your situation, and replies with what a fix would take — scoped to that one area, priced up front, yours whether you ship it or not. Ask a question →

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 the provider profile at artifacts.apis.io/kubeflow/apis.yml: open an issue on github.com/api-evangelist/kubeflow to ask a question or point us at an artifact you publish. Submit an artifact on GitHub — free → Manage your own listing — the Influence plan, $499/mo →

Markets this provider is in provisional

The Kin Score Market layer reads a provider inside its market: its position, and how deep its integrations go. Depth is only comparable within a position. Provisional and unweighted: not part of the Kin Score or agent readiness.

Market	Position	Depth	Registered MCP server	Declares compatibility
MLOps and AI/ML Infrastructure (LF AI & Data, MLPerf, Kubeflow)	standard	40.9 (thin)	-	-
OpenSSF Best Practices Badge Holders	standard	40.9 (thin)	-	-

Registered MCP server and declares compatibility are report only — not weighted. Undeclared means we found nothing we can read, so there is no number: we never infer integrations.

Standards implemented 2

Standards this provider implements or carries. De facto ones are profiled and measured by the API Commons; formal ones come from the standards directory and are linked by the provider's own tags. The evidence column says how each claim was established — not that it was made.

Model Context Protocol (MCP)

measured — observed by APIs.io (documented)

OpenAPI

measured — observed by APIs.io (published)

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 Pipeline Versions API

Manage versions of pipelines

Scroll for all 8

Open Collections 5

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

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

Kubeflow API pricing

Not yet checked against Kubeflow's own pricing page. Compiled by API Evangelist on 2026-05-04; confirm before you rely on a figure. A free plan or tier is listed.

Plan	Price
Free freemium	Free
Professional paid	Free
Enterprise enterprise	—

Plan details, limits and sources →

Rate limits

Free Tier Default: 10 requests per minute
Free Tier Monthly Quota: 1000 requests per month
Professional Tier Default: 100 requests per minute

Compiled by API Evangelist, not yet checked against Kubeflow's documentation. All 5 limits →

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

Spectral Rules 1

Spectral governance rulesets for linting and validating these APIs. Counts are the severity each rule declares, not results of running them.

Kubeflow API Rules

56 rules · 54 error-severity 1 warn-severity 1 info-severity

SPECTRAL

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 (API Evangelist recommendations) 1

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

Kubeflow Agentic Access

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 4

Status, limits, changes, and where to get help

RateLimits

IssueTracker

Releases

Community

Commercial 3

Pricing, plans, and the legal terms of use

Plans

Plans

FinOps

FinOps

License

License

Company 3

The organization behind the API

LinkedIn

Website

Website

Blog

Blog

Other 1

Properties that don't map to a standard resource type

CapabilityMap

CapabilityMap

Source (apis.yml)

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
  gated: none
  gated_source:
  - onboarding
  label: Freemium · Self-serve signup
  confidence: medium
  source:
  - plans
  - authentication
  - security
  generated: '2026-11-13'
  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
- Machine-Learning
- 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:
  - Dashboards
  - Management
  - UI
  tags_raw:
  - 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-pipeline-versions-api
  name: Kubeflow Pipeline Versions API
  description: Manage versions of pipelines
  humanURL: https://www.kubeflow.org/docs/components/pipelines/concepts/metadata/
  baseURL: https://your-kubeflow-instance/pipeline
  tags:
  - PipelineVersions
  tags_raw:
  - Pipeline Versions
  properties:
  - type: OpenAPI
    url: openapi/kubeflow-pipeline-versions-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: Plans
  url: plans/kubeflow-plans-pricing.yml
- type: RateLimits
  url: rate-limits/kubeflow-rate-limits.yml
- type: FinOps
  url: finops/kubeflow-finops.yml
- type: CapabilityMap
  url: capabilities/kubeflow-capability-edges.yml
  name: Kubeflow Business Capability Map
- 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

Kubeflow API — frequently asked questions

Does Kubeflow have an API?

Yes. APIs.io lists 8 APIs from Kubeflow, including Kubeflow Metadata API, Katib API and Kubeflow Notebooks API, and 5 more.

Is there an OpenAPI specification for the Kubeflow API?

Yes. APIs.io indexes 4 OpenAPI specifications for Kubeflow, each linked for download from this page.

How do I authenticate with the Kubeflow API?

Kubeflow uses bearer-token authentication (http · 1 scheme). The details are profiled on APIs.io as Kubeflow Authentication.

How much does the Kubeflow API cost?

APIs.io lists 3 pricing plans for Kubeflow, with each plan's price, limits and inclusions under Pricing on this page.

Does Kubeflow have an MCP server?

Yes. APIs.io lists 1 MCP server for Kubeflow.

Is the Kubeflow API ready for AI agents?

Kubeflow has a Kin Score of 32.1 out of 100 (thin) and an Agent Readiness score of 22.7 (agent-aware), scored by API Evangelist on 2026-11-13.

Work with this as data

Every provider here is available over the APIs.io API and to AI agents over MCP.

MCP server

One button, every client — Claude, Cursor, VS Code and the rest.

https://apis.io/mcp

Tools for providers

9 MCP tools reach this

find_providersBrowse and filter every provider in the catalog.
get_provider_artifactsEvery artifact this provider publishes, grouped by type.
get_provider_operationsEvery operation across all of their OpenAPIs — one call instead of parsing every spec.
get_provider_toolsEvery MCP tool they ship, with the operation each wraps.
get_provider_evidenceHow each part of their score was established. Free — the basis for a claim should not sit behind it.
get_provider_ratingPRO — composite, band, trend and facet scores.
apis_io_searchSTART HERE — APIs, providers and tags for one query, each with its total.
resolveTurn a domain, URL or GitHub org into the provider it belongs to.
find_cohortsEvery scored population of providers in the catalog.

All 92 tools →

Call it yourself

curl for this page

This provider

curl "https://apis.io/api/v1/providers/kubeflow"

All providers

curl "https://apis.io/api/v1/providers?limit=25"

Every operation they expose

curl "https://apis.io/api/v1/providers/kubeflow/operations?limit=25"

How their score was established

curl "https://apis.io/api/v1/providers/kubeflow/evidence"

Calls without X-API-Key need no key. Calls with it need a paid plan — get a key.

Get an API key

Free tier, no form to fill in. Signing in shares your email address with us — we store it to create your key and to recognise you if you sign in with another provider. See our Privacy Policy and Terms.

GitHub Google LinkedIn

A second provider on the same verified email joins the account you already have.