ZenML
ZenML is an open-source MLOps and LLMOps framework that unifies machine learning and generative AI workflows through a single orchestration, versioning, and governance layer. It provides a Python SDK, CLI, REST API, and server for managing pipelines, stacks, artifacts, models, and deployments across any infrastructure backend, with 60+ integrations spanning orchestrators, ML frameworks, GenAI tools, cloud storage, and experiment tracking platforms.
Solid contracts, transparent operations, and an easy start — typically complete on four or five facets with one clear soft spot.
API Evangelist profiles ZenML the way a machine reads it — 62 machine-readable artifacts across 1 API, pulled from the provider's own public surface and indexed so a developer, an analyst, or an AI agent can evaluate it against every other provider on the network.
Every provider in the network is reduced to the same set of machine-readable artifacts — OpenAPI contracts, event specifications, GraphQL schemas, runnable collections, pricing and rate-limit signals, security posture, OAuth scopes, and the agent surfaces (MCP servers and skills) that let software drive the API on its own. We profile them because the interface is the part of a company you can actually inspect: it is a truer signal of what a provider does than any marketing page. From those artifacts we compute the Kin Score — ZenML scores 57.8/100 (strong), with a separate agent-readiness read of 28/100 (agent aware). The full breakdown is below, followed by every artifact we hold — each card links through to its machine-readable definition on apis.io.
Kin Score
This is the API Evangelist rating — a single, repeatable read computed from the artifacts on this page. Green fill is points earned; the red track is points possible, so every bar shows earned-versus-possible at a glance. Every facet and dimension name is a link: it opens that measurement's page on APIs.io, where the rating runs across the whole catalog — the exact checks that feed it, how every profiled provider distributes on it, and who is at the top of it.
Kin Score
How this is scored →scored 2026-09-08 · rubric v0.20.0
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How we profile ZenML
Each block below is one kind of artifact we hold for ZenML. For each we say what it is and why it earns a place in the profile, then list every one we've indexed — capped at two rows, scroll within the panel for the rest.
APIs 14
Each API is captured as its own OpenAPI definition — every operation, parameter, and response. This is the single most useful machine-readable description of what an API does, and it's what lets us score, lint, mock, and generate against it without asking the provider for anything.
Individual APIs this provider publishes, each with its own machine-readable definition.
ZenML Pro REST API
The ZenML Pro REST API extends the OSS API with managed control-plane features for teams, including organization and tenant management, role-based access control, audit logs, an...
ZenML Artifacts API
Artifact metadata and versions produced by pipeline runs
ZenML Auth API
Authentication and token management
ZenML Deployments API
Pipeline deployments
ZenML Models API
Registered models and their versions
ZenML Pipeline Runs API
Pipeline run instances and their steps
ZenML Pipelines API
ML pipeline definitions
ZenML Projects API
Project workspaces
ZenML Schedules API
Scheduled pipeline runs
ZenML Secrets API
Encrypted secret storage
ZenML Service Connectors API
Connectors to external infrastructure providers
ZenML Stack Components API
Individual stack components such as orchestrators, artifact stores, and experiment trackers
ZenML Stacks API
ZenML stacks and their components
ZenML Users API
User accounts
Scroll within the panel for all 14 ·
Postman Collections 1
A runnable collection turns the contract into something a developer can execute in seconds. We profile them because the fastest way to trust an API is to make a real call against it.
Ready-to-run Postman collections for exercising this provider's APIs.
ZenML OSS REST API
POSTMANOpen Collections 15
Open, tool-agnostic collections carry the same runnable value as Postman without locking you to one client — the portable, forkable form of the same exercise.
Open, tool-agnostic API collections (OpenAPI-derived and Bruno).
API Collection
OPEN COLLECTIONZenML OSS REST Artifacts API
OPEN COLLECTIONZenML OSS REST Artifacts Auth API
OPEN COLLECTIONZenML OSS REST Artifacts Deployments API
OPEN COLLECTIONZenML OSS REST Artifacts Models API
OPEN COLLECTIONZenML OSS REST Artifacts Pipeline Runs API
OPEN COLLECTIONZenML OSS REST Artifacts Pipelines API
OPEN COLLECTIONZenML OSS REST Artifacts Projects API
OPEN COLLECTIONZenML OSS REST Artifacts Schedules API
OPEN COLLECTIONZenML OSS REST Artifacts Secrets API
OPEN COLLECTIONZenML OSS REST Artifacts Service Connectors API
OPEN COLLECTIONZenML OSS REST Artifacts Stack Components API
OPEN COLLECTIONZenML OSS REST Artifacts Stacks API
OPEN COLLECTIONZenML OSS REST Artifacts Users API
OPEN COLLECTIONZenML OSS REST API
OPEN COLLECTIONScroll within the panel for all 15 ·
Arazzo Workflows 12
Real integrations are rarely a single call. Arazzo describes the multi-step sequences — auth, then create, then confirm — so both a human and an agent can follow the choreography, not just the endpoints.
Multi-step API workflows described with the Arazzo specification.
ZenML Audit Pipeline Runs
Walk from a named pipeline to its run history and drill into the most recent run.
ARAZZOZenML Authenticate and List Pipelines
Exchange credentials for a token, confirm the session identity, and list pipelines.
ARAZZOZenML Bootstrap Project Pipeline
Identify the caller, resolve a project workspace, register a pipeline, and confirm it.
ARAZZOZenML Inspect Run Artifacts
Select a pipeline run, confirm it succeeded, and inspect an artifact produced in the deployment.
ARAZZOZenML Inspect Stack Topology
Pick a stack, read its component wiring, and cross-reference the component catalog.
ARAZZOZenML Monitor Pipeline Run
Find the latest run of a pipeline, poll its status to completion, and branch on success or failure.
ARAZZOZenML Provision Pipeline
Resolve a project, register a new pipeline in it, and confirm the pipeline was created.
ARAZZOZenML Provision Secret
Confirm the caller identity, create a scoped secret, and confirm it appears in the secret store.
ARAZZOZenML Register Model
Register a new model in the model control plane and enumerate its versions.
ARAZZOZenML Register Stack
Discover available stack components, assemble them into a new stack, and confirm the stack was created.
ARAZZOZenML Trace Deployment Runs
Select a pipeline deployment, resolve its pipeline, and read the latest run it produced.
ARAZZOZenML Track Scheduled Pipeline
Resolve a schedule, find the run it produced for its pipeline, and read that run.
ARAZZOScroll within the panel for all 12 ·
MCP Servers 1
Model Context Protocol servers expose these APIs directly to AI agents. We profile them because agent-native access is the fastest-growing way this provider's capabilities actually get used.
Model Context Protocol servers that expose these APIs to AI agents.
MCP Server
MCP SERVERPricing Plans 1
Pricing is part of the interface. Machine-readable plans tell you what a tier costs and includes before you commit — one of the things the Kin Score reads for access clarity — renamed from commercial clarity in rubric 0.12, because a free statutory interface has access terms and no commercial ones.
Published pricing tiers and plan structures.
Zenml Plans Pricing
PLANSRate Limits 1
Rate limits are the difference between a demo that works and a production integration that doesn't fall over. Publishing them is an operational-transparency signal — and a hard requirement for any agent that plans its own throughput.
Documented rate limits and quota policies.
Zenml Rate Limits
RATE LIMITSFinOps 1
Cost, billing, and metering signals let a buyer model the financial operations of an API before it's live. We profile them for the same reason we profile pricing: the money is part of the contract.
Cost, billing, and metering signals for API financial operations.
Zenml Finops
FINOPSSemantic Vocabularies 1
JSON-LD contexts give the data shared meaning across APIs. We profile them because semantics are what let a machine reconcile 'customer' here with 'customer' somewhere else.
JSON-LD contexts and semantic vocabularies used across these APIs.
Zenml Context
JSON-LDSpectral Rules 2
Governance rulesets we run against this provider's specs — the automated checks behind parts of the score. Profiling them makes the quality bar explicit and re-runnable, not a matter of opinion.
ZenML API Rules
SPECTRALZenML API Rules
SPECTRALJSON Schema 5
Standalone JSON Schema definitions describe the data models behind the API. We profile them so the shapes are validatable on their own — useful long after a single request is forgotten.
Standalone JSON Schema definitions for this provider's data models.
ZenML Artifact
JSON SCHEMAZenML Model
JSON SCHEMAZenML Pipeline Run
JSON SCHEMAZenML Pipeline
JSON SCHEMAZenML Stack
JSON SCHEMAJSON Structure 2
JSON Structure captures the data shapes in a form built for tooling — a complement to JSON Schema that keeps the model machine-legible.
JSON Structure definitions describing this provider's data shapes.
Zenml Pipeline Run Structure
JSON STRUCTUREZenml Pipeline Structure
JSON STRUCTUREExamples 3
Real request and response payloads are what turn a spec from abstract into obvious — and they're one of the twelve things an agent needs to call an API correctly on the first try.
Example request and response payloads for these APIs.
Zenml Create Stack Example
EXAMPLEZenml List Pipelines Example
EXAMPLESecurity Posture 2
Authentication, domain security, vulnerability disclosure, and trust-center signals — the evidence that a provider takes security seriously enough to document it. We profile it because you can't govern what you can't see.
Authentication, domain security, vulnerability disclosure, and trust-center signals.
Agentic Access 1
An x-agentic-access contract marks which operations are safe for an agent to run on its own and which need a human in the loop. It is the difference between an API an agent can use and one it can use safely.
Recommended x-agentic-access execution contracts for AI agents.
Resources
Every other property we hold for ZenML — documentation, portals, status pages, policies, and corporate surface — grouped by the job it does, following the integrator's arc from getting started to running in production.
Get Started 2
Portal, sign-up, and the first successful call
Documentation 2
Reference material describing how the API behaves
Agent Surfaces 4
MCP servers, agent skills, and machine-readable catalogs
Design & Contract 13
Pagination, idempotency, versioning, errors, and events
Scroll within the panel for all 13 ·
Build 6
SDKs, sample code, and the tooling you integrate with
Access & Security 3
Authentication, authorization, and security posture
Operate 5
Status, limits, changes, and where to get help
Commercial 4
Pricing, plans, and the legal terms of use
Company 3
The organization behind the API
Other 3
Properties that don't map to a standard resource type
← All providers · Data indexed from github.com/api-evangelist/zenml · machine-readable index on apis.io
This is an independent, third-party profile of ZenML, published by API Evangelist. We do not operate, host, resell, or support these APIs, and we are not affiliated with or endorsed by the company unless stated above. Everything here is built from publicly available information — the company's own site, developer portal, documentation, public repositories, and the specifications it publishes for public use. Nothing is obtained by breaching a system, defeating an access control, or using credentials.
The Kin Score and Agent Readiness rating are independently calculated assessments of a company's public API artifacts, scored against a published rubric. They are not certifications, endorsements, security assessments, or audits.
Corrections, re-scores, and removal are free — no partnership or purchase required, and you do not need to justify the request. A removed company is recorded as unrated, never scored zero for having asked. Acknowledgement within one business day; removal within two.
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