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 | Vertex AI |
|---|---|---|
| Workflow Orchestration | Yes Purpose-built ML pipeline orchestration with pluggable backends — Airflow, Kubeflow, Kubernetes, Vertex AI, and more |
Yes Vertex AI Pipelines is a managed, production-grade orchestrator for containerized ML workflows on GCP with console visibility and lifecycle tracking |
| Integration Flexibility | Yes Composable stack with 50+ MLOps integrations — swap orchestrators, trackers, and deployers without code changes |
Not supported Deep integration within GCP via Google Cloud Pipeline Components, but no cloud-agnostic integration model for non-GCP tools |
| Vendor Lock-In | Yes Open-source Python pipelines run anywhere — switch clouds, orchestrators, or tools without rewriting code |
Not supported Runs inside a GCP project/region with GCP identity and GCS storage — migration typically means re-platforming the entire pipeline stack |
| Setup Complexity | Yes pip install zenml — start building pipelines in minutes with zero infrastructure, scale when ready |
Yes Managed service eliminates infrastructure setup — configure GCP project, IAM, and storage to get production-grade pipelines running |
| Learning Curve | Yes Python-native API with decorators — familiar to any ML engineer or data scientist who writes Python |
Not supported Requires learning KFP component/pipeline DSL, compilation workflows, containerization patterns, and GCP resource concepts |
| Scalability | Yes Delegates compute to scalable backends — Kubernetes, Spark, cloud ML services — for unlimited horizontal scaling |
Yes Enterprise-scale workloads on GCP — orchestrates large training/processing jobs using Google-managed Vertex, BigQuery, and Dataflow services |
| Cost Model | Yes Open-source core is free — pay only for your own infrastructure, with optional managed cloud for enterprise features |
Yes Documented per-run pipeline fee ($0.03/run) plus underlying compute costs — Google provides cost labeling and billing export for transparency |
| Collaboration | Yes Code-native collaboration through Git, CI/CD, and code review — ZenML Pro adds RBAC, workspaces, and team dashboards |
Yes Collaborative use through shared GCP projects, IAM-based access control, and console-based visibility into runs and metadata |
| ML Frameworks | Yes Use any Python ML framework — TensorFlow, PyTorch, scikit-learn, XGBoost, LightGBM — with native materializers and tracking |
Yes Broad framework support via custom containers and prebuilt container images for common frameworks including PyTorch and TensorFlow |
| Monitoring | Yes Integrates Evidently, WhyLogs, and other monitoring tools as stack components for automated drift detection and alerting |
Yes Vertex AI Model Monitoring provides scheduled monitoring jobs with alerting when model quality metrics cross defined thresholds |
| Governance | Yes ZenML Pro provides RBAC, SSO, workspaces, and audit trails — self-hosted option keeps all data in your own infrastructure |
Yes Enterprise governance via GCP IAM, network controls, billing attribution, and VPC support for pipeline-launched resources |
| Experiment Tracking | Yes Native metadata tracking plus seamless integration with MLflow, Weights & Biases, Neptune, and Comet for rich experiment comparison |
Yes Vertex AI Experiments tracks hyperparameters, environments, and results with SDK and console support built on Vertex ML Metadata |
| Reproducibility | Yes Automatic artifact versioning, code-to-Git linking, and containerized execution guarantee reproducible pipeline runs |
Yes Pipeline templates plus Vertex ML Metadata record artifacts and lineage graphs — strong primitives for reproducing ML workflows on GCP |
| Auto-Retraining | Yes Schedule pipelines via any orchestrator or use ZenML Pro event triggers for drift-based automated retraining workflows |
Yes Vertex AI scheduler API supports one-time or recurring pipeline runs for continuous training patterns within GCP |
Code comparison
ZenML and Vertex AI 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()from kfp import dsl, compiler
from google.cloud import aiplatform
PROJECT_ID = "my-gcp-project"
REGION = "europe-west1"
PIPELINE_ROOT = "gs://my-bucket/pipeline-root"
@dsl.component
def preprocess(input_uri: str) -> str:
# Read and clean data from GCS
return input_uri
@dsl.component
def train(data_uri: str) -> str:
# Train model and write artifacts to GCS
return f"{data_uri}#trained-model"
@dsl.pipeline(name="train-pipeline", pipeline_root=PIPELINE_ROOT)
def pipeline(input_uri: str = "gs://my-bucket/data/train.csv"):
data = preprocess(input_uri=input_uri)
train(data_uri=data.output)
# Compile pipeline to JSON template
compiler.Compiler().compile(
pipeline_func=pipeline, package_path="pipeline.json"
)
# Submit to Vertex AI (GCP-only)
aiplatform.init(project=PROJECT_ID, location=REGION)
job = aiplatform.PipelineJob(
display_name="train-pipeline",
template_path="pipeline.json",
pipeline_root=PIPELINE_ROOT,
)
job.submit()
# Pipeline runs only on GCP — no built-in
# portability to AWS, Azure, or on-prem.
# Metadata tied to Vertex ML Metadata service.01.
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.
Outperform E2E Platforms: Book Your Free ZenML Strategy Talk
E2E Platform Showdown
Explore the Advantages of ZenML Over Other E2E Platform Tools
ZenML vs Alteryx
Alteryx alternative for ML pipelines: open-source, code-first MLOps with vendor-neutral portability. Build production ML workflows that run anywhere
ZenML vs AWS Sagemaker
Vendor-neutral ML orchestration. Build, train, and deploy models across environments with flexible workflows and no cloud lock-in.
ZenML vs Azure ML
Looking for an open-source alternative to Azure ML Pipelines? ZenML builds portable ML pipelines across clouds without vendor lock-in.
ZenML vs ClearML
ClearML alternative: Flexible MLOps framework with extensive integrations. Accelerate ML development using adaptable architecture and reduced lock-in, while simplifying infrastructure setup.
ZenML vs Dataiku
Dataiku alternative: Open-source MLOps framework with portable Python pipelines and composable stack. Build production ML workflows with vendor-neutral flexibility and best-of-breed tool integration
ZenML vs Domino Data Lab
Domino Data Lab alternative: Open-source MLOps framework with portable pipelines and composable stack. Build production ML workflows with vendor-neutral flexibility and code-first portability
ZenML vs Metaflow
Metaflow alternative: Flexible ML orchestration with comprehensive MLOps. Accelerate ML using adaptable architecture and seamless integrations.
ZenML vs Valohai
Valohai alternative: Open-source MLOps framework with extensive integrations. Accelerate ML development using Python-based SDK, adaptable architecture, and existing infrastructure, while avoiding vendor lock-in.
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Ready to Build Portable ML
Pipelines Beyond Google Cloud?
See how ZenML can run on Vertex AI today and still stay portable across AWS, Azure, or on-prem when your strategy changes Explore ZenML's stack-based approach to integrating your existing trackers, registries, and artifact stores instead of rebuilding in GCP Learn practical migration patterns: keep Vertex training and serving where it helps, while moving pipeline orchestration and metadata to ZenML