MLflow website screenshot

MLflow

MLflow is an Apache 2.0 open-source platform for the end-to-end ML and GenAI lifecycle: tracking, model registry, deployment, evaluation, traces, prompts, and GenAI gateway. The tracking server exposes a REST API under `/api/2.0/mlflow`.

MLflow publishes 8 APIs on the APIs.io network, including Artifacts API, Experiments API, Metrics API, and 5 more. Tagged areas include Machine Learning, MLOps, Generative AI, Experiment Tracking, and Open Source.

MLflow’s developer surface includes authentication, developer portal, engineering blog, and 16 more developer resources.

38.4/100 thin ▬ flat Agent 20/100 agent aware Front door AI 6/6 dominant open core · Apache-2.0 Full breakdown ↓
scored 2026-10-04 · rubric v0.23.0
1 published contract
Machine LearningMLOpsGenerative AIExperiment TrackingOpen 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
ArtifactsExperimentsMetricsModel VersionsRegistered ModelsRuns
POST /api/2.0/mlflow/model-versions/create POST /api/2.0/mlflow/registered-models/create GET /api/2.0/mlflow/artifacts/list POST /api/2.0/mlflow/experiments/create

Kin Score

Kin Score Kin Score How this is scored →
scored 2026-10-04 · rubric v0.23.0
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/.
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/mlflow: 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.

MLflow REST API

The MLflow tracking server exposes a REST API for experiments, runs, parameters, metrics, tags, registered models, model versions, model aliases, and artifacts, plus newer endpo...

MLflow AI Gateway API

The MLflow AI Gateway provides a unified HTTP interface to LLM providers with routing, rate-limiting, and secret management features.

MLflow Artifacts API

List and obtain presigned URLs for run artifacts.

MLflow Experiments API

Manage MLflow experiments.

MLflow Metrics API

Log and retrieve metric history for runs.

MLflow Model Versions API

Manage versions of registered models.

MLflow Registered Models API

Manage registered models in the MLflow Model Registry.

MLflow Runs API

Create and manage runs within an experiment.

Scroll for all 8

Open Collections 8

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

API Collection

OPEN COLLECTION

MLflow Tracking REST API

OPEN COLLECTION

Scroll for all 8

Pricing Plans 1

Published pricing tiers and plan structures.

Mlflow Plans Pricing

1 plans

PLANS

Rate Limits 1

Documented rate limits and quota policies.

Mlflow Rate Limits

1 limits

RATE LIMITS

FinOps 1

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

Security Posture 2

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

Mlflow Authentication

http · 1 scheme

SECURITY

Mlflow Domain Security

TLSv1.3

SECURITY

Agentic Access 1

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

Mlflow Agentic Access

23 operations · 17 acting

23 operations · 17 acting

AGENTIC

Resources

Get Started 1

Portal, sign-up, and the first successful call

Documentation 1

Reference material describing how the API behaves

Agent Surfaces 2

MCP servers, agent skills, and machine-readable catalogs

Build 1

SDKs, sample code, and the tooling you integrate with

Access & Security 3

Authentication, authorization, and security posture

Operate 3

Status, limits, changes, and where to get help

Commercial 3

Pricing, plans, and the legal terms of use

Company 3

The organization behind the API

Other 2

Properties that don't map to a standard resource type

Source (apis.yml)

apis.yml Raw ↑
aid: mlflow
url: https://raw.githubusercontent.com/api-evangelist/mlflow/refs/heads/main/apis.yml
name: MLflow
kind: opensource
description: 'MLflow is an Apache 2.0 open-source platform for the end-to-end ML and GenAI lifecycle: tracking, model registry,
  deployment, evaluation, traces, prompts, and GenAI gateway. The tracking server exposes a REST API under `/api/2.0/mlflow`.'
deliveryModel:
  model: open-core
  license: Apache-2.0
  license_evidence:
    repo: mlflow/mlflow
    spdx: Apache-2.0
    stars: 28134
  open_source: true
  commercial: true
  callable_host: false
  label: Open core · an OSS project plus a commercial hosted product
  confidence: medium
  source:
  - openapi
  - pricing
  - repository-license
  generated: '2026-09-25'
  method: derived
accessModel:
  pricing: unknown
  onboarding: unknown
  trial: false
  try_now: false
  public: false
  label: Unknown
  confidence: low
  source:
  - plans
  - authentication
  - security
  generated: '2026-09-03'
  method: derived
image: https://kinlane-images.s3.amazonaws.com/shared/apis-json/icons/mlflow.png
x-docs-platform: docusaurus
x-docs-platform-confidence: medium
x-docs-platform-checked: 2026-10-04
tags:
- Machine Learning
- MLOps
- Generative AI
- Experiment Tracking
- Open Source
tags_raw:
- ML
- MLOps
- GenAI
- Experiment Tracking
- Open Source
- Machine-Learning
- Open-Source
created: '2026-05-08'
modified: '2026-05-08'
specificationVersion: '0.23'
apis:
- aid: mlflow:mlflow-tracking-rest-api
  name: MLflow REST API
  humanURL: https://mlflow.org/docs/latest/rest-api.html
  baseURL: http://{tracking_server}/api/2.0/mlflow
  tags:
  - REST
  - Experiments
  - Runs
  - Model Registry
  - Artifacts
  description: The MLflow tracking server exposes a REST API for experiments, runs, parameters, metrics, tags, registered
    models, model versions, model aliases, and artifacts, plus newer endpoints for traces, prompts, datasets, and the AI gateway.
    POST requests use `application/json`. Pagination via `max_results` / `page_token`. Authentication is deployment-specific
    (basic auth, OIDC, or none).
  properties:
  - type: Documentation
    url: https://mlflow.org/docs/latest/rest-api.html
  - type: SDKs
    title: Python (mlflow)
    url: https://mlflow.org/docs/latest/python_api/index.html
  - type: SDKs
    title: Java
    url: https://mlflow.org/docs/latest/java_api/index.html
  - type: SDKs
    title: R
    url: https://mlflow.org/docs/latest/R-api.html
- aid: mlflow:mlflow-gateway-api
  name: MLflow AI Gateway API
  humanURL: https://mlflow.org/docs/latest/llms/gateway/index.html
  baseURL: http://{gateway}/gateway
  tags:
  - Generative AI
  - LLM Gateway
  - REST
  tags_raw:
  - GenAI
  - LLM Gateway
  - REST
  description: The MLflow AI Gateway provides a unified HTTP interface to LLM providers with routing, rate-limiting, and secret
    management features.
  properties:
  - type: Documentation
    url: https://mlflow.org/docs/latest/llms/gateway/index.html
- aid: mlflow:mlflow-artifacts-api
  name: MLflow Artifacts API
  description: List and obtain presigned URLs for run artifacts.
  humanURL: https://mlflow.org/docs/latest/rest-api.html
  baseURL: http://{tracking_server}/api/2.0/mlflow
  tags:
  - Artifacts
  properties:
  - type: OpenAPI
    url: openapi/mlflow-artifacts-api-openapi.yml
- aid: mlflow:mlflow-experiments-api
  name: MLflow Experiments API
  description: Manage MLflow experiments.
  humanURL: https://mlflow.org/docs/latest/rest-api.html
  baseURL: http://{tracking_server}/api/2.0/mlflow
  tags:
  - Experiments
  properties:
  - type: OpenAPI
    url: openapi/mlflow-experiments-api-openapi.yml
- aid: mlflow:mlflow-metrics-api
  name: MLflow Metrics API
  description: Log and retrieve metric history for runs.
  humanURL: https://mlflow.org/docs/latest/rest-api.html
  baseURL: http://{tracking_server}/api/2.0/mlflow
  tags:
  - Metrics
  properties:
  - type: OpenAPI
    url: openapi/mlflow-metrics-api-openapi.yml
- aid: mlflow:mlflow-model-versions-api
  name: MLflow Model Versions API
  description: Manage versions of registered models.
  humanURL: https://mlflow.org/docs/latest/rest-api.html
  baseURL: http://{tracking_server}/api/2.0/mlflow
  tags:
  - Model Versions
  properties:
  - type: OpenAPI
    url: openapi/mlflow-model-versions-api-openapi.yml
- aid: mlflow:mlflow-registered-models-api
  name: MLflow Registered Models API
  description: Manage registered models in the MLflow Model Registry.
  humanURL: https://mlflow.org/docs/latest/rest-api.html
  baseURL: http://{tracking_server}/api/2.0/mlflow
  tags:
  - Registered Models
  properties:
  - type: OpenAPI
    url: openapi/mlflow-registered-models-api-openapi.yml
- aid: mlflow:mlflow-runs-api
  name: MLflow Runs API
  description: Create and manage runs within an experiment.
  humanURL: https://mlflow.org/docs/latest/rest-api.html
  baseURL: http://{tracking_server}/api/2.0/mlflow
  tags:
  - Runs
  properties:
  - type: OpenAPI
    url: openapi/mlflow-runs-api-openapi.yml
common:
- type: CapabilityMap
  url: capabilities/mlflow-capability-edges.yml
  name: MLflow Business Capability Map
- type: IssueTracker
  url: https://github.com/mlflow/mlflow/issues
- type: Releases
  url: https://github.com/mlflow/mlflow/releases
- type: SecurityPolicy
  url: https://github.com/mlflow/mlflow/blob/master/SECURITY.md
- type: ContributionGuide
  url: https://github.com/mlflow/mlflow/blob/master/CONTRIBUTING.md
- type: AgenticAccess
  url: agentic-access/mlflow-agentic-access.yml
- type: DomainSecurity
  url: security/mlflow-domain-security.yml
- type: Authentication
  url: authentication/mlflow-authentication.yml
- type: LinkedIn
  url: https://www.linkedin.com/company/mlflow-org
- type: Website
  url: https://mlflow.org/
- type: Portal
  url: https://mlflow.org/docs/latest/
- type: SourceCode
  url: https://github.com/mlflow/mlflow
- type: License
  url: https://github.com/mlflow/mlflow/blob/master/LICENSE.txt
- type: CommercialOffering
  title: Databricks Managed MLflow
  url: https://www.databricks.com/product/managed-mlflow
- type: Plans
  url: plans/mlflow-plans-pricing.yml
- type: RateLimits
  url: rate-limits/mlflow-rate-limits.yml
- type: FinOps
  url: finops/mlflow-finops.yml
- type: LlmsText
  url: https://mlflow.org/llms.txt
- url: https://mlflow.org/blog/rss.xml
  type: Blog
maintainers:
- FN: Kin Lane
  email: kin@apievangelist.com

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