Compare ZenML vs

DVC versions your data. ZenML runs your pipelines anywhere.

DVC is excellent at versioning datasets and models alongside Git, with strong experiment workflows and reproducible pipelines. ZenML is an open-source MLOps framework that orchestrates the full lifecycle: pipelines, artifact/metadata tracking, and integrations across environments. If you've standardized on DVC for reproducibility but need production orchestration and lifecycle coordination, ZenML is the next layer up.

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Open-source and vendor-neutral

ZenML is fully open-source, giving you complete control over your ML infrastructure.
Avoid platform lock-in — run the same pipelines across any cloud or on-prem environment.
Benefit from a transparent, community-driven development process.

Dashboard mockup showing local-to-production workflow

Composable stack architecture

Choose your own orchestrator, experiment tracker, artifact store, and model deployer.
Swap infrastructure components without rewriting pipeline code.
Integrate new tools instantly as they emerge without waiting for vendor support.

Code-first, Python-native workflows

Define pipelines in pure Python with simple decorators — no YAML or DSL to learn.
Start locally with pip install and scale to production on any cloud.
Version control your entire ML workflow alongside your application code.

Dashboard mockup showing productionalization workflow

“ZenML has proven to be a critical asset in our machine learning toolbox, and we are excited to continue leveraging its capabilities to drive ADEO's machine learning initiatives to new heights”

François Serra

ML Engineer / ML Ops / ML Solution architect at ADEO Services

Feature-by-feature comparison

Explore in Detail What Makes ZenML Unique

Feature

ZenML

DVC

Workflow Orchestration	ZenML is built to orchestrate multi-step ML pipelines across environments using swappable stack components, not just reproduce local command graphs.	DVC provides lightweight DAG execution via dvc.yaml + dvc repro, but it's primarily local and relies on external schedulers/CI for production orchestration.
Integration Flexibility	ZenML's stack architecture is designed to plug in best-of-breed tools (orchestrators, trackers, deployers, registries) without rewriting pipelines.	DVC integrates well with Git and storage backends, but doesn't offer a composable stack of interchangeable orchestration/deployment/monitoring components.
Vendor Lock-In	ZenML is cloud-agnostic by design: it can run on multiple clouds or on-prem by swapping stack components.	DVC core is Apache-2.0 OSS and can use many storage systems as remotes; DVC Studio is optional for teams that want a hosted UI.
Setup Complexity	ZenML can start locally quickly and scales by adding stack components incrementally as teams grow.	DVC is easy to bootstrap in an existing repo: install the CLI, dvc init, and optionally configure a remote; no server component is required.
Learning Curve	ZenML's learning curve pays off for teams building repeatable production pipelines with stacks, artifacts, and metadata concepts.	DVC's mental model maps closely to Git + build tools: version artifacts with pointers, define stages in dvc.yaml, and reproduce with dvc repro.
Scalability	ZenML scales by delegating execution to production orchestrators and managed services while keeping pipeline code portable across environments.	DVC scales well for large datasets via remotes and caching, but production workload orchestration typically requires pairing DVC with CI systems or other orchestrators.
Cost Model	ZenML's OSS core is free; managed offerings shift cost from infra/time to subscription for teams needing centralized governance and collaboration.	DVC core is free OSS (Apache-2.0); DVC Studio offers a free tier for individuals/small teams with paid options for larger teams.
Collaboration	ZenML emphasizes team collaboration through shared stacks, centralized metadata/lineage, and integration points for registries and dashboards.	DVC collaboration is Git-native (branches/PRs) and enhanced by DVC Studio, which adds experiment visualization, sharing, and team features.
ML Frameworks	ZenML integrates across many ML libraries and platforms while standardizing how artifacts/metadata flow through pipelines.	DVC is framework-agnostic (stages are commands), and DVCLive provides built-in integrations across many popular ML frameworks.
Monitoring	ZenML connects the training pipeline to production concerns including deployment and downstream monitoring integrations as part of an MLOps stack.	DVC focuses on tracking training-time experiments, metrics, and artifacts; it does not provide production inference monitoring out of the box.
Governance	ZenML's centralized metadata, lineage, and reproducibility primitives are designed to support governance requirements across environments and teams.	DVC provides strong auditability via Git history, but enterprise governance (RBAC, policy enforcement, audit workflows) depends on Git hosting and optional Studio features.
Experiment Tracking	ZenML can integrate with dedicated experiment trackers and also tracks pipeline runs and artifacts in a metadata store for end-to-end lineage.	Experiment tracking is a core DVC strength: experiments are stored as custom Git refs, runnable via dvc exp run, and enhanced with DVCLive + DVC Studio.
Reproducibility	ZenML provides reproducibility through tracked artifacts, caching, and lineage in a metadata store, even when execution moves across environments.	Reproducibility is foundational: DVC pipelines capture dependencies/outputs and reproduce results with dvc repro, with data/models versioned via cache + remotes.
Auto-Retraining	ZenML is designed for scheduled/triggered pipelines using orchestrators and CI/CD integrations, enabling automated retraining patterns.	DVC can participate in auto-retraining when paired with CI schedulers and CML, but does not provide a native always-on retraining orchestrator.

Code comparison

ZenML and DVC side by side

ZenML

from zenml import pipeline, step

@step
def load_data():
    # Load and preprocess your data
    ...
    return train_data, test_data

@step
def train_model(train_data):
    # Train using ANY ML framework
    ...
    return model

@step
def evaluate(model, test_data):
    # Evaluate and log metrics
    ...
    return metrics

@pipeline
def ml_pipeline():
    train, test = load_data()
    model = train_model(train)
    evaluate(model, test)

DVC

import csv
import numpy as np
import dvc.api
from dvclive import Live
from sklearn.linear_model import SGDClassifier
from sklearn.metrics import accuracy_score
import joblib

# Stream a versioned dataset snapshot from DVC remote
with dvc.api.open("data/train.csv", rev="v1.1.0") as f:
    rows = list(csv.DictReader(f))

X = np.array([[float(r["f1"]), float(r["f2"])] for r in rows])
y = np.array([int(r["label"]) for r in rows])

clf = SGDClassifier(loss="log_loss", random_state=42)
with Live() as live:
    for _ in range(5):
        clf.partial_fit(X, y, classes=np.unique(y))
        live.log_metric("train/accuracy", float(accuracy_score(y, clf.predict(X))))
        live.next_step()

joblib.dump(clf, "model.joblib")

Open-Source and Vendor-Neutral

ZenML is fully open-source and vendor-neutral, letting you avoid the significant licensing costs and platform lock-in of proprietary enterprise platforms. Your pipelines remain portable across any infrastructure, from local development to multi-cloud production.

Lightweight, Code-First Development

ZenML offers a pip-installable, Python-first approach that lets you start locally and scale later. No enterprise deployment, platform operators, or Kubernetes clusters required to begin — build production-grade ML pipelines in minutes, not weeks.

Composable Stack Architecture

ZenML's composable stack lets you choose your own orchestrator, experiment tracker, artifact store, and deployer. Swap components freely without re-platforming — your pipelines adapt to your toolchain, not the other way around.