Choosing a sandbox runtime for long-lived coding agents
How we evaluated sandbox providers for coding agents, what we chose for now, and the trade-offs behind that decision.
Run the same workloads on any cloud to gain strategic flexibility
- ZenML does not tie your work to one cloud.
- Define infrastructure as stack components independent of your code.
- Run any code on any stack with minimum fuss.
50+ integrations with the most popular cloud and open-source tools
- From experiment trackers like MLflow and Weights & Biases to model deployers like Seldon and BentoML, ZenML has integrations for tools across the lifecycle.
- Flexibly run workflows across all clouds or orchestration tools such as Airflow or Kubeflow.
- AWS, GCP, and Azure integrations all supported out of the box.
Avoid getting locked in to a vendor
- Avoid tangling up code with tooling libraries that make it hard to transition.
- Easily set up multiple stacks for different teams with different requirements.
- Switch between tools and platforms seamlessly.
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
Explore in Detail What Makes ZenML Unique
| Feature | ZenML | Alteryx |
|---|---|---|
| Workflow Orchestration | Yes Purpose-built ML pipeline orchestration with pluggable backends — Airflow, Kubeflow, Kubernetes, and more | Not supported Visual workflow execution on Alteryx Engine or Server — designed for analytics automation, not ML pipeline lifecycle |
| Integration Flexibility | Yes Composable stack with 50+ MLOps integrations — swap orchestrators, trackers, and deployers without code changes | Not supported Strong data source connectors (100+) but limited MLOps ecosystem integration — ML tools require custom Python/API code |
| Vendor Lock-In | Yes Open-source Python pipelines run anywhere — switch clouds, orchestrators, or tools without rewriting code | Not supported Proprietary .yxmd workflow format locked to the Alteryx engine — workflows cannot run outside the Alteryx ecosystem |
| Setup Complexity | Yes pip install zenml — start building pipelines in minutes with zero infrastructure, scale when ready | Not supported Windows desktop install plus Server administration (controller/worker architecture, MongoDB, licensing) for enterprise deployment |
| Learning Curve | Yes Python-native API with decorators — familiar to any ML engineer or data scientist who writes Python | Yes Exceptionally approachable drag-and-drop interface designed for business analysts and citizen data scientists |
| Scalability | Yes Delegates compute to scalable backends — Kubernetes, Spark, cloud ML services — for unlimited horizontal scaling | Yes AMP engine with multi-threading, in-database pushdown to Snowflake/Databricks, and Server worker scaling |
| Cost Model | Yes Open-source core is free — pay only for your own infrastructure, with optional managed cloud for enterprise features | Not supported Per-seat licensing across Starter, Professional, and Enterprise tiers — pricing varies by edition and deployment model |
| Collaboration | Yes Code-native collaboration through Git, CI/CD, and code review — ZenML Pro adds RBAC, workspaces, and team dashboards | Yes Server Gallery for sharing workflows, collections, version history, and analytic apps with role-based access control |
| ML Frameworks | Yes Use any Python ML framework — TensorFlow, PyTorch, scikit-learn, XGBoost, LightGBM — with native materializers and tracking | Yes R-based predictive tools plus Intelligence Suite for AutoML — Python tool enables scikit-learn and other frameworks inside workflows |
| Monitoring | Yes Integrates Evidently, WhyLogs, and other monitoring tools as stack components for automated drift detection and alerting | Not supported No native model monitoring or drift detection — Plans offers data health alerting but ML model performance tracking is absent |
| Governance | Yes ZenML Pro provides RBAC, SSO, workspaces, and audit trails — self-hosted option keeps all data in your own infrastructure | Yes Enterprise-grade governance with ISO 27001, SOC 2, RBAC, SSO, audit logs, and new lineage integrations with Atlan and Collibra |
| Experiment Tracking | Yes Native metadata tracking plus seamless integration with MLflow, Weights & Biases, Neptune, and Comet for rich experiment comparison | Not supported No built-in experiment tracking — workflow version history exists on Server but structured ML experiment comparison is absent |
| Reproducibility | Yes Automatic artifact versioning, code-to-Git linking, and containerized execution guarantee reproducible pipeline runs | Yes Deterministic workflow files are repeatable — though Python/R environment drift across machines can affect consistency |
| Auto-Retraining | Yes Schedule pipelines via any orchestrator or use ZenML Pro event triggers for drift-based automated retraining workflows | Yes Server scheduling and API-triggered workflow runs enable periodic retraining — but no ML-signal-based automatic triggers |
Code comparison
ZenML and Alteryx side by side
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()# Alteryx Designer — Python Tool in Workflow
from ayx import Alteryx
import pandas as pd
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_squared_error
import numpy as np
import pickle
# Read data from upstream Alteryx tools
df = Alteryx.read("#1")
X = df.drop(columns=["target"])
y = df["target"]
model = RandomForestRegressor(n_estimators=100)
model.fit(X, y)
predictions = model.predict(X)
rmse = np.sqrt(mean_squared_error(y, predictions))
# Save model artifact manually
with open("model.pkl", "wb") as f:
pickle.dump(model, f)
# Output results to downstream Alteryx tools
results = pd.DataFrame({
"metric": ["rmse"], "value": [rmse]
})
Alteryx.write(results, 1)
# Retraining requires scheduling this workflow
# on Alteryx Server; no built-in experiment
# tracking, model registry, or drift detection01.
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.
02.
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.
03.
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.
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E2E Platform Showdown
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