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.
Start locally without complicated setup hassle
- ZenML is available as a simple pip package that lets you run and track pipelines locally.
- ZenML integrates with your orchestration layer of choice, avoiding having to learn different paradigms for dev, staging, and prod.
- ZenML integrates with your orchestration layer of choice or can be extended with your own orchestration service.
Abstract away infrastructure complexity
- Most orchestrators assume some form of infrastructure knowledge to use them maximally — ZenML abstracts that complexity away.
- ZenML separates infrastructure setup like Docker building from the application logic, and automates the tedious parts.
- ZenML focuses on the handovers between MLOps Engineers, ML Engineers, and Data Scientists.
Switch between orchestrators depending on your context
- You can switch between different orchestration services with a single click — from dev to staging to production.
- The more engineering-minded in the team still retain control over their productionalization because the framework is extensible.
- ZenML handles the pain of packaging your code into Docker to be deployed to your orchestration service of choice.
Explore in Detail What Makes ZenML Unique
| Feature | ZenML | Databricks |
|---|---|---|
| Workflow Orchestration | Yes Provides a flexible and portable orchestration layer for ML workflows across various environments | Yes Offers robust orchestration within the Databricks ecosystem, optimized for Spark-based workflows |
| Integration Flexibility | Yes Seamlessly integrates with a wide range of MLOps tools and cloud services | Not supported Primarily focuses on integration within the Databricks ecosystem and select partner tools |
| Vendor Lock-In | Yes Enables easy migration between different tools and cloud providers | Not supported Tightly coupled with Databricks' ecosystem, which may lead to vendor lock-in |
| Setup Complexity | Yes Lightweight setup with minimal infrastructure requirements | Not supported More complex setup, often requiring dedicated Databricks clusters and workspace configuration |
| Learning Curve | Yes Gentle learning curve with familiar Python-based pipeline definitions | Not supported Steeper learning curve, especially for teams new to Spark and the Databricks ecosystem |
| Scalability | Yes Scalable architecture that can grow with your needs, leveraging various compute backends | Yes Highly scalable, particularly for big data processing with built-in Spark capabilities |
| Cost Model | Yes Open-source core with optional paid features, allowing for cost-effective scaling | Not supported Subscription-based pricing model, which can be costly for smaller teams or projects |
| Data Processing | Yes Flexible data processing capabilities, integrating with various data tools and frameworks | Yes Optimized for big data processing with native Apache Spark integration |
| Collaborative Development | Yes Supports collaboration through version control and pipeline sharing | Yes Offers collaborative notebooks and workspace management for team development |
| ML Framework Support | Yes Supports a wide range of ML frameworks and libraries | Yes Supports popular ML frameworks, with optimizations for distributed training on Spark |
| Feature Store Integration | Yes Integrates with feature stores like Feast, and orchestrates feature engineering pipelines as part of your ML workflow | Yes Provides a built-in Feature Store within Unity Catalog for feature discovery, lineage, and online/offline serving |
| Model Monitoring & Drift Detection | Yes Integrates with monitoring tools like Evidently and Great Expectations, orchestrated as pipeline steps for drift detection and data quality | Yes Offers inference-table-driven monitoring with built-in data profiling and drift metrics via Lakehouse Monitoring |
| Governance & Access Control | Yes Provides RBAC, artifact lineage tracking, and a model control plane for approval workflows and audit trails | Yes Delivers fine-grained access control, auditing, and lineage through Unity Catalog across data and ML assets |
| Auto Retraining Triggers | Yes Supports scheduled pipelines and event-driven triggers that can initiate retraining based on drift detection or performance thresholds | Yes Enables auto-retraining via Databricks Jobs with scheduling, triggers, and integration with monitoring alerts |
Code comparison
ZenML and Databricks 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 Databricks compute, logs to MLflow,
# tracks artifacts, and triggers retraining — all
# in one portable, version-controlled pipeline
ml_pipeline()# Databricks Notebook / Job workflow
import mlflow
import pandas as pd
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_squared_error
import numpy as np
mlflow.set_tracking_uri("databricks")
mlflow.set_registry_uri("databricks-uc")
df = pd.read_csv("/dbfs/mnt/data/dataset.csv")
X, y = df.drop("target", axis=1), df["target"]
with mlflow.start_run():
model = RandomForestRegressor(n_estimators=100)
model.fit(X, y)
predictions = model.predict(X)
rmse = np.sqrt(mean_squared_error(y, predictions))
mlflow.log_metric("rmse", rmse)
mlflow.sklearn.log_model(
model, "model",
registered_model_name="catalog.schema.my_model"
)
print(f"RMSE: {rmse}")
# Retraining requires separate Jobs, schedules,
# and monitoring configured in Databricks UI01.
Flexibility and Vendor Independence
ZenML offers a vendor-neutral approach, allowing you to integrate with various tools and cloud providers, while Databricks is primarily focused on its own ecosystem.
02.
Lightweight and Easy Setup
ZenML provides a more lightweight solution with minimal infrastructure requirements, making it easier to set up and start using compared to Databricks' more complex environment.
03.
Cost-Effective for Small to Medium Projects
With its open-source core and optional paid features, ZenML offers a more cost-effective solution for smaller teams and projects, unlike Databricks' subscription-based model which can be costly for limited use cases.
04.
Gentle Learning Curve
ZenML's familiar Python-based pipeline definitions and consistent interface across platforms make it easier to learn and use, especially for teams without Spark expertise, compared to Databricks' steeper learning curve.
05.
Portability and Multi-Cloud Support
ZenML ensures workflow portability across different environments and supports easy migration between cloud providers. Databricks is available on AWS, Azure, and GCP, but workflows remain deeply tied to Databricks-specific constructs (workspaces, clusters, jobs, Unity Catalog), reducing portability compared to a tool-agnostic pipeline layer like ZenML.
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