| Workflow Orchestration | ML-native pipelines with portable execution via stacks, while still supporting Kubernetes-based orchestration when needed | Kubernetes-native workflow engine with mature DAG/steps execution, retries, scheduling, and strong operational controls on K8s |
| Integration Flexibility | Composable stack with 50+ MLOps integrations — swap orchestrators, trackers, and deployers without code changes | Runs virtually any containerized tool, but integrations are DIY — teams wire credentials, storage, and conventions manually |
| Vendor Lock-In | Cloud-agnostic by design — stacks make it easy to switch infrastructure providers and tools as needs change | Runs on any Kubernetes cluster and is CNCF-governed open source — lock-in is primarily to Kubernetes itself, not a specific cloud |
| Setup Complexity | pip install zenml — start locally and scale up by swapping stacks, avoiding immediate Kubernetes dependency | Requires Kubernetes cluster plus Argo installation, RBAC config, artifact repository, and optional database for full value |
| Learning Curve | Python-first and ML-first — reduces cognitive load for ML engineers who don't want to become Kubernetes experts | Assumes Kubernetes fluency (CRDs, pods, namespaces, service accounts, storage) — ML teams often need platform help to adopt it |
| Scalability | Delegates compute to scalable backends — Kubernetes, Spark, cloud ML services — for unlimited horizontal scaling | Built for parallel job orchestration on Kubernetes with parallelism limits, retries, and workflow offloading for large DAGs |
| Cost Model | Open-source core is free — pay only for your own infrastructure, with optional managed cloud for enterprise features | Apache-2.0 and CNCF-governed with no per-seat or per-run pricing — costs are Kubernetes infrastructure and operations |
| Collaboration | Code-native collaboration through Git, CI/CD, and code review — ZenML Pro adds RBAC, workspaces, and team dashboards | UI and SSO support for multi-user setups, but collaboration is centered on workflow execution and logs — not ML experiment sharing |
| ML Frameworks | Use any Python ML framework — TensorFlow, PyTorch, scikit-learn, XGBoost, LightGBM — with native materializers and tracking | Framework-agnostic at the runtime level — if it runs in a container on Kubernetes, Argo can orchestrate it |
| Monitoring | Integrates Evidently, WhyLogs, and other monitoring tools as stack components for automated drift detection and alerting | Monitors workflow execution well (statuses, logs, Prometheus metrics), but no production model monitoring or ML drift detection built in |
| Governance | ZenML Pro provides RBAC, SSO, workspaces, and audit trails — self-hosted option keeps all data in your own infrastructure | Kubernetes-centric governance with namespacing, RBAC, and workflow archiving — but ML-specific audit trails require external systems |
| Experiment Tracking | Native metadata tracking plus seamless integration with MLflow, Weights & Biases, Neptune, and Comet for rich experiment comparison | No built-in experiment tracking — teams embed MLflow or W&B inside containers and standardize conventions manually |
| Reproducibility | Automatic artifact versioning, code-to-Git linking, and containerized execution guarantee reproducible pipeline runs | Workflows are rerunnable, but reproducibility depends on pinned containers, data versioning, and discipline — not enforced by default |
| Auto-Retraining | Schedule pipelines via any orchestrator or use ZenML Pro event triggers for drift-based automated retraining workflows | CronWorkflows and webhook triggers enable automated retraining runs — model promotion and registry logic left to your stack |
from zenml import pipeline, step, Model
from zenml.integrations.mlflow.steps import (
mlflow_model_deployer_step,
)
import pandas as pd
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_squared_error
import numpy as np
@step
def ingest_data() -> pd.DataFrame:
return pd.read_csv("data/dataset.csv")
@step
def train_model(df: pd.DataFrame) -> RandomForestRegressor:
X, y = df.drop("target", axis=1), df["target"]
model = RandomForestRegressor(n_estimators=100)
model.fit(X, y)
return model
@step
def evaluate(model: RandomForestRegressor, df: pd.DataFrame) -> float:
X, y = df.drop("target", axis=1), df["target"]
preds = model.predict(X)
return float(np.sqrt(mean_squared_error(y, preds)))
@step
def check_drift(df: pd.DataFrame) -> bool:
# Plug in Evidently, Great Expectations, etc.
return detect_drift(df)
@pipeline(model=Model(name="my_model"))
def ml_pipeline():
df = ingest_data()
model = train_model(df)
rmse = evaluate(model, df)
drift = check_drift(df)
# Runs on any orchestrator, logs to MLflow,
# tracks artifacts, and triggers retraining — all
# in one portable, version-controlled pipeline
ml_pipeline()from hera.workflows import Steps, Workflow, WorkflowsService, script
@script()
def preprocess() -> str:
print("s3://ml-artifacts/datasets/processed.csv")
@script()
def train(data_uri: str) -> str:
print("s3://ml-artifacts/models/model.pkl")
@script()
def evaluate(model_uri: str):
print(f"evaluating {model_uri}")
with Workflow(
generate_name="ml-train-eval-",
entrypoint="steps",
namespace="argo",
workflows_service=WorkflowsService(
host="https://localhost:2746"
),
) as w:
with Steps(name="steps"):
data = preprocess()
model = train(arguments={"data_uri": data.result})
evaluate(arguments={"model_uri": model.result})
w.create()
# Requires Kubernetes cluster + Argo installation.
# No built-in experiment tracking, model registry,
# artifact lineage, or reproducibility guarantees.
# ML lifecycle layers must be built separately.
