Comparison

MLflow vs ZenML

On the evidence we track, MLflow leads this comparison with a composite score of 76/100. Scores are only directly comparable because these tools share a category; the full breakdown and every source is below.

Machine formatsJSONMarkdownGraphQLor send Accept: application/json
MLflow76
ZenML56
Score
Vioscale score
MLflow76 / 100medium · 63%
ZenML56 / 100medium · 58%
Pricing
Free tier
MLflow
ZenML
Model
Price level
MLflowfree
ZenMLfree
Transparent
MLflow
ZenML
Integrations
Count
MLflow15
ZenML6
Reliability
Status page
MLflow
ZenML
Adoption
Dependent repos
MLflow5,089
ZenML44
Github stars
MLflow27,687
ZenML5,564
Package downloads weekly
MLflow9,993,245
ZenML
Activity
Commits last 30d
MLflow100
ZenML23
Release
Cadence days
MLflow10
ZenML14
History
MLflow20 items
License
Spdx
Language
Primary
MLflowPython
ZenMLPython

Capabilities

Feature-by-feature on the axes that matter for mlops & llmops tools. “-” means undocumented, not absent.

Core
Tool role
MLflowEnd-to-end ML platform
ZenMLEnd-to-end ML platform
Deployment
Self-hostable / OSS core
MLflow
ZenML
Managed cloud available
MLflow
ZenML
On-prem / VPC deployment
MLflow
ZenML
Observability
LLM tracing / observability
MLflow
ZenML
Evaluation (offline / LLM-judge / human)
MLflow
ZenML
Dev
Prompt management + versioning
MLflow
ZenML-
Tracking
Experiment tracking / model registry
MLflow
ZenML
Serving
Model serving / inference endpoint
MLflow
ZenML
Interop
OpenTelemetry / OpenLLMetry compatible
MLflow
ZenML-
Framework-agnostic
MLflow
ZenML
Gateway
Multi-provider model support
MLflow
ZenML-
Data
No-train-on-customer-data guarantee
MLflow-
ZenML-

What each one is

The product in its own terms, so the numbers below have context.

MLflow

Leader

A comprehensive, open-source platform that provides experiment tracking, model registry, LLM tracing, prompt management, and deployment capabilities across the complete machine learning and AI lifecycle

Independently observed

ZenML

An MLOps framework that provides reproducible machine learning pipelines with automatic logging, versioning, and observability. Includes agent runtime capabilities (Kitaru) for building replayable agent workflows, deployable on your existing infrastructure without vendor lock-in.

Independently observed

Pricing

List pricing as published by each vendor, with the date we read it. Always verify at the source before you buy.

MLflow

Leader
Open sourceFree tier
as of verify ↗

ZenML

Open sourceFree tier

Free open-source tier. Paid ZenML Pro tier (details require account login)

  • Open SourceFree
    • Unlimited pipeline executions
    • Full orchestration capabilities
    • Automatic logging and versioning
    • No vendor lock-in
  • ProPricing not publicly available
    • Managed cloud hosting
    • Unified dashboard and observability
    • Advanced security controls
    • Enterprise integrations
as of verify ↗

Platform & deployment

Where each product runs and how it can be hosted. A dash means undocumented, not unsupported.

Platforms
Web
MLflow
ZenML
CLI
MLflow
ZenML
Deployment
Cloud / SaaS
MLflow
ZenML
Self-hosted
MLflow
ZenML
On-premise
MLflow
ZenML
Hybrid
MLflow
ZenML

Integrations

What each product connects to. Counts come from the vendor's own integration directory where one exists.

In common (1)
  • Kubernetes

MLflow

Leader
28 total - 27 not shared
  • Databricks
  • AWS S3
  • Google Cloud Storage
  • Azure Storage
  • AzureML
  • LangChain
  • Pydantic AI
  • Anthropic Claude
  • OpenAI
  • Google Gemini
  • SAP AI Core
  • JFrog
  • Aliyun
  • PostgreSQL
  • MySQL
  • MSSQL
  • Claude Code
  • OpenAI Codex
  • Gemini
  • Anthropic
  • Ollama
  • OpenClaw
  • Qwen Code
  • LiteLLM
  • +3 more
Independently observed

ZenML

6 total - 5 not shared
  • Git
  • GitHub
  • GitLab
  • Docker
  • Poetry
Independently observed

Comparison generated from independently-sourced facts. Every value links to its source and retrieval date. See the method.