Kubeflow Pipelines

Kubeflow Pipelines is a platform for building and deploying portable, scalable machine learning workflows based on Docker containers. It provides a way to orchestrate complex ML workflows with dependencies, enabling data scientists and ML engineers to deploy production-ready ML systems on Kubernetes.

Kubeflow Pipelines publishes 8 API contracts indexed on the APIs.io network, including Experiments API, Health API, and 6 more. Tagged areas include Data Science, Kubernetes, Machine Learning, MLOps, and Orchestration.

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

Kubeflow Pipelines’ developer surface includes authentication, documentation, getting-started guide, engineering blog, changelog, and 18 more developer resources.

🌐 Visit website 📡 Profile data on GitHub

39.3/100 developing ▬ flat Agent 21/100 agent aware 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

Data Science Kubernetes Machine Learning MLOps Orchestration Pipelines Workflows

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 VersionsPipelinesRuns

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

Kin Score

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

Composite quality — 39.3/100 · developing

Contract Quality 7.4 / 16

Developer Ergonomics 5.3 / 13

Access Clarity 1.7 / 13

Operational Transparency 3.0 / 8

Contract Governance 1.1 / 8

Discoverability 3.7 / 7

Regulatory Posture 1.6 / 15

Open Source Surface 10.0 / 10

Create-or-Update Ergonomics 0.0 / 10

Agent readiness — 21/100 · agent aware

Machine-Readable Contract 18 / 18

Agentic Access Contract 10 / 10

Documented Reversibility 0 / 6

MCP Server 0 / 12

Machine-Readable Auth 10 / 10

Idempotency 0 / 9

Stable Error Semantics 0 / 8

Request/Response Examples 7 / 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/.

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

MCP Server +12 pts — deployment.mode + probe verdict on the mcp/ manifest, not the pointer; an unprobed slug falls back to authorship Ask about this →
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 →
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 12.8 — up to +17.4 composite points Ask about this →
Contract Quality scores 45.6 — up to +13.6 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-pipelines/apis.yml: open an issue on github.com/api-evangelist/kubeflow-pipelines 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
Cloud Native (Kubernetes, CNCF, FinOps)	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 1

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.

OpenAPI

measured — observed by APIs.io (published)

APIs 8

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

Kubeflow Pipelines Python SDK

Python SDK for building, compiling, and submitting ML pipelines. Provides decorators and utilities to define pipeline components and workflows using Python.

Kubeflow Pipelines Go Client

Go client library for interacting with the Kubeflow Pipelines API programmatically from Go applications.

Kubeflow Pipelines Metadata API

API for tracking and managing metadata about ML artifacts, executions, and lineage information throughout the ML pipeline lifecycle, backed by ML Metadata (MLMD).

Kubeflow Pipelines Experiments API

Group runs and recurring jobs into experiments

Kubeflow Pipelines Health API

Health and auth checks

Kubeflow Pipelines API

Manage pipeline definitions

Kubeflow Pipelines Runs API

Pipeline runs

Kubeflow Pipelines Pipeline Versions API

Manage versions of pipelines

Scroll for all 8

Open Collections 6

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

Kubeflow Pipelines REST Experiments API

OPEN COLLECTION

Kubeflow Pipelines REST Experiments Health API

OPEN COLLECTION

Kubeflow REST Experiments Pipelines API

OPEN COLLECTION

Kubeflow Pipelines REST Experiments PipelineVersions API

OPEN COLLECTION

Kubeflow Pipelines REST Experiments Runs API

OPEN COLLECTION

Kubeflow Pipelines REST API

OPEN COLLECTION

Kubeflow Pipelines API pricing

Not yet checked against Kubeflow Pipelines'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 Pipelines's documentation. All 5 limits →

Pricing Plans 1

Published pricing tiers and plan structures.

Kubeflow Pipelines Plans Pricing

3 plans

PLANS

Rate Limits 1

Documented rate limits and quota policies.

Kubeflow Pipelines Rate Limits

5 limits

RATE LIMITS

Terms of Service 1

What its API terms permit and prohibit, quoted from its own page.

Kubeflow Pipelines

6 quoted statements, read from Kubeflow Pipelines's own page on 2026-11-09.

TERMS

Data License 1

How its data and outputs may be reused, quoted from its own terms.

Kubeflow Pipelines

2 quoted statements, read from Kubeflow Pipelines's own page on 2026-11-09.

LICENSE

FinOps 1

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

Kubeflow Pipelines 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 Pipelines API Rules

62 rules · 60 error-severity 1 warn-severity 1 info-severity

SPECTRAL

Security Posture 2

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

Kubeflow Pipelines Authentication

http · 1 scheme

SECURITY

Kubeflow Pipelines Domain Security

TLSv1.3 · HSTS · DNSSEC

SECURITY

Agentic Access (API Evangelist recommendations) 1

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

Kubeflow Pipelines Agentic Access

13 operations · 6 acting

AGENTIC

Resources

Get Started 1

Portal, sign-up, and the first successful call

GettingStarted

GettingStarted

Documentation 2

Reference material describing how the API behaves

ContributionGuide

ContributionGuide

Documentation

Documentation

Agent Surfaces 1

MCP servers, agent skills, and machine-readable catalogs

AgenticAccess

AgenticAccess

Build 3

SDKs, sample code, and the tooling you integrate with

CodeOfConduct

CodeOfConduct

GitHubOrg

GitHubOrg

GitHubRepository

GitHubRepository

Access & Security 3

Authentication, authorization, and security posture

SecurityPolicy

SecurityPolicy

DomainSecurity

DomainSecurity

Authentication

Authentication

Operate 4

Status, limits, changes, and where to get help

RateLimits

IssueTracker

Community

ChangeLog

Commercial 5

Pricing, plans, and the legal terms of use

Plans

FinOps

License

UsageTerms

DataLicense

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-pipelines
name: Kubeflow Pipelines
description: Kubeflow Pipelines is a platform for building and deploying portable, scalable machine learning workflows based
  on Docker containers. It provides a way to orchestrate complex ML workflows with dependencies, enabling data scientists
  and ML engineers to deploy production-ready ML systems on Kubernetes.
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-pipelines.png
tags:
- Data Science
- Kubernetes
- Machine Learning
- MLOps
- Orchestration
- Pipelines
- Workflows
tags_raw:
- Data Science
- Kubernetes
- Machine Learning
- MLOps
- Orchestration
- Pipelines
- Workflows
- Machine-Learning
url: https://raw.githubusercontent.com/api-evangelist/kubeflow-pipelines/refs/heads/main/apis.yml
created: '2024-01-01'
modified: '2026-09-16'
specificationVersion: '0.23'
apis:
- aid: kubeflow-pipelines:python-sdk
  name: Kubeflow Pipelines Python SDK
  description: Python SDK for building, compiling, and submitting ML pipelines. Provides decorators and utilities to define
    pipeline components and workflows using Python.
  humanURL: https://kubeflow-pipelines.readthedocs.io/
  baseURL: https://pypi.org/project/kfp/
  tags:
  - Client Library
  - DSL
  - Python
  - SDK
  properties:
  - type: Documentation
    url: https://kubeflow-pipelines.readthedocs.io/
  - type: GitHubRepository
    url: https://github.com/kubeflow/pipelines/tree/master/sdk/python
  - type: Examples
    url: https://github.com/kubeflow/pipelines/tree/master/samples
- aid: kubeflow-pipelines:go-client
  name: Kubeflow Pipelines Go Client
  description: Go client library for interacting with the Kubeflow Pipelines API programmatically from Go applications.
  humanURL: https://github.com/kubeflow/pipelines/tree/master/backend/api/go_client
  tags:
  - Client Library
  - Go
  - SDK
  properties:
  - type: Documentation
    url: https://pkg.go.dev/github.com/kubeflow/pipelines/backend/api/go_client
  - type: GitHubRepository
    url: https://github.com/kubeflow/pipelines/tree/master/backend/api/go_client
- aid: kubeflow-pipelines:metadata-api
  name: Kubeflow Pipelines Metadata API
  description: API for tracking and managing metadata about ML artifacts, executions, and lineage information throughout the
    ML pipeline lifecycle, backed by ML Metadata (MLMD).
  humanURL: https://www.kubeflow.org/docs/components/pipelines/concepts/metadata/
  tags:
  - Artifacts
  - Data Lineage
  - Metadata
  - ML Metadata
  tags_raw:
  - Artifacts
  - Lineage
  - Metadata
  - ML Metadata
  properties:
  - type: Documentation
    url: https://www.kubeflow.org/docs/components/pipelines/concepts/metadata/
  - type: GitHubRepository
    url: https://github.com/google/ml-metadata
- aid: kubeflow-pipelines:kubeflow-pipelines-experiments-api
  name: Kubeflow Pipelines Experiments API
  description: Group runs and recurring jobs into experiments
  humanURL: https://www.kubeflow.org/docs/components/pipelines/reference/api/kubeflow-pipeline-api-spec/
  baseURL: https://your-kubeflow-host/pipeline
  tags:
  - Experiments
  properties:
  - type: OpenAPI
    url: openapi/kubeflow-pipelines-experiments-api-openapi.yml
  - type: Documentation
    url: https://www.kubeflow.org/docs/components/pipelines/reference/api/kubeflow-pipeline-api-spec/
- aid: kubeflow-pipelines:kubeflow-pipelines-health-api
  name: Kubeflow Pipelines Health API
  description: Health and auth checks
  humanURL: https://www.kubeflow.org/docs/components/pipelines/reference/api/kubeflow-pipeline-api-spec/
  baseURL: https://your-kubeflow-host/pipeline
  tags:
  - Health
  properties:
  - type: OpenAPI
    url: openapi/kubeflow-pipelines-health-api-openapi.yml
  - type: Documentation
    url: https://www.kubeflow.org/docs/components/pipelines/reference/api/kubeflow-pipeline-api-spec/
- aid: kubeflow-pipelines:kubeflow-pipelines-pipelines-api
  name: Kubeflow Pipelines API
  description: Manage pipeline definitions
  humanURL: https://www.kubeflow.org/docs/components/pipelines/reference/api/kubeflow-pipeline-api-spec/
  baseURL: https://your-kubeflow-host/pipeline
  tags:
  - Pipelines
  properties:
  - type: OpenAPI
    url: openapi/kubeflow-pipelines-pipelines-api-openapi.yml
  - type: Documentation
    url: https://www.kubeflow.org/docs/components/pipelines/reference/api/kubeflow-pipeline-api-spec/
- aid: kubeflow-pipelines:kubeflow-pipelines-runs-api
  name: Kubeflow Pipelines Runs API
  description: Pipeline runs
  humanURL: https://www.kubeflow.org/docs/components/pipelines/reference/api/kubeflow-pipeline-api-spec/
  baseURL: https://your-kubeflow-host/pipeline
  tags:
  - Runs
  properties:
  - type: OpenAPI
    url: openapi/kubeflow-pipelines-runs-api-openapi.yml
  - type: Documentation
    url: https://www.kubeflow.org/docs/components/pipelines/reference/api/kubeflow-pipeline-api-spec/
- aid: kubeflow-pipelines:kubeflow-pipelines-pipeline-versions-api
  name: Kubeflow Pipelines Pipeline Versions API
  description: Manage versions of pipelines
  humanURL: https://kubeflow-pipelines.readthedocs.io/
  baseURL: https://pypi.org/project/kfp/
  tags:
  - PipelineVersions
  tags_raw:
  - Pipeline Versions
  properties:
  - type: OpenAPI
    url: openapi/kubeflow-pipelines-pipeline-versions-api-openapi.yml
  - type: Documentation
    url: https://www.kubeflow.org/docs/components/pipelines/reference/api/kubeflow-pipeline-api-spec/
common:
- type: Plans
  url: plans/kubeflow-pipelines-plans-pricing.yml
- type: RateLimits
  url: rate-limits/kubeflow-pipelines-rate-limits.yml
- type: FinOps
  url: finops/kubeflow-pipelines-finops.yml
- type: CapabilityMap
  url: capabilities/kubeflow-pipelines-capability-edges.yml
  name: Kubeflow Pipelines Business Capability Map
- type: IssueTracker
  url: https://github.com/kubeflow/pipelines/issues
- type: SecurityPolicy
  url: https://github.com/kubeflow/pipelines/blob/master/SECURITY.md
- type: CodeOfConduct
  url: https://github.com/kubeflow/pipelines/blob/master/CODE_OF_CONDUCT.md
- type: ContributionGuide
  url: https://github.com/kubeflow/pipelines/blob/master/CONTRIBUTING.md
- type: License
  name: Apache-2.0
  url: https://github.com/kubeflow/pipelines/blob/master/LICENSE
- type: AgenticAccess
  url: agentic-access/kubeflow-pipelines-agentic-access.yml
- type: DomainSecurity
  url: security/kubeflow-pipelines-domain-security.yml
- type: Authentication
  url: authentication/kubeflow-pipelines-authentication.yml
- type: LinkedIn
  url: https://www.linkedin.com/company/kubeflow
- type: Website
  url: https://www.kubeflow.org/docs/components/pipelines/
- type: Documentation
  url: https://www.kubeflow.org/docs/components/pipelines/
- type: GettingStarted
  url: https://www.kubeflow.org/docs/components/pipelines/getting-started/
- type: GitHubOrg
  url: https://github.com/kubeflow
- type: GitHubRepository
  url: https://github.com/kubeflow/pipelines
- type: Blog
  url: https://blog.kubeflow.org/
- type: Community
  url: https://www.kubeflow.org/docs/about/community/
- type: ChangeLog
  url: https://github.com/kubeflow/pipelines/releases
- type: UsageTerms
  url: terms/kubeflow-pipelines-terms-of-service.yml
- type: DataLicense
  url: data-license/kubeflow-pipelines-data-license.yml
maintainers:
- FN: Kin Lane
  email: kin@apievangelist.com
x-parent: kubeflow
x-relationship: product

Kubeflow Pipelines API — frequently asked questions

Does Kubeflow Pipelines have an API?

Yes. APIs.io lists 8 APIs from Kubeflow Pipelines, including Kubeflow Pipelines Python SDK, Kubeflow Pipelines Go Client and Kubeflow Pipelines Metadata API, and 5 more.

Is there an OpenAPI specification for the Kubeflow Pipelines API?

Yes. APIs.io indexes 5 OpenAPI specifications for Kubeflow Pipelines, each linked for download from this page.

How do I authenticate with the Kubeflow Pipelines API?

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

How much does the Kubeflow Pipelines API cost?

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

Is the Kubeflow Pipelines API ready for AI agents?

Kubeflow Pipelines has a Kin Score of 39.3 out of 100 (developing) and an Agent Readiness score of 20.9 (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-pipelines"

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

Every operation they expose

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

How their score was established

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

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