Kestra has streamlined our data processes, reduced costs, and significantly enhanced our scalability and efficiency. It has truly been a critical asset in our digital transformation journey.
Kestra vs. Azure Data Factory: Open Orchestration vs. Azure-Native ETL
Azure Data Factory is Microsoft's managed ETL service. Kestra is open-source workflow orchestration for any cloud, any language, and use cases beyond data movement. One is built to integrate data inside Azure. The other orchestrates what your entire engineering team ships.
Managed Cloud ETL vs. Universal Orchestration
Open-Source Orchestration for Any Stack
Declarative YAML workflows versioned in Git, executed in isolated containers, deployed through CI/CD. Orchestrate data pipelines, infrastructure operations, AI workloads, and business processes across AWS, Azure, GCP, or on-premises without vendor lock-in.
"How do I orchestrate workflows across clouds and teams without committing to one vendor?"
Azure-Native Managed ETL
Fully managed data integration service built into the Azure ecosystem. Author pipelines visually in the Azure portal, connect natively to Azure data services, and scale ETL without managing infrastructure. Pricing is consumption-based per activity run.
"How do I move and transform data across my Azure services without managing servers?"
Azure ETL Covers One Cloud.
Orchestration Runs the Business Beyond It.
Universal Workflow Orchestration
- Data pipelines, infrastructure automation, AI workloads, and business processes
- Multi-cloud and on-premises: AWS, Azure, GCP, or hybrid
- YAML-first: Git-native, CI/CD-ready, reviewable by any engineer
- Open source with 26k+ GitHub stars and 1200+ plugins
- Self-service for non-engineers via Kestra Apps
Azure-Native Data Integration
- Data movement and transformation across Azure data services
- Azure-first: native connectors for Synapse, Data Lake, and SQL Database
- Visual pipeline authoring in the Azure portal (JSON ARM templates for code)
- Consumption-based pricing per activity run and data integration unit
- No open-source version
Time to First Workflow
ADF requires an Azure subscription, a resource group, a Data Factory instance, linked service credentials for each data source, and dataset definitions before your first pipeline runs. Kestra runs with two commands and no cloud account required.
~5
Minutes
curl -o docker-compose.yml \https://raw.githubusercontent.com/kestra-io/kestra/develop/docker-compose.ymldocker compose up
# Open localhost:8080# Pick a Blueprint, run it. Done.Download the Docker Compose file, spin it up, and you're ready. Database and config included. Open the UI, pick a Blueprint, run it. No Azure account, no resource groups, no linked service setup.
Hours to
Days
# 1. Create Azure subscription (or use existing)# 2. Create resource group in Azure portal# 3. Provision Data Factory instance# 4. Configure Linked Services (credentials per data source)# 5. Define Datasets (schema and format per source/sink)# 6. Author pipeline in visual designer or JSON editor# 7. Create triggers (schedule or storage event)
# Or deploy via ARM/Bicep:az deployment group create \ --resource-group my-rg \ --template-file adf-template.jsonRequires an Azure subscription, creating a resource group, provisioning a Data Factory instance, configuring linked services for each data source, and defining datasets before the first pipeline runs. The portal is well-designed, but standing up production infrastructure takes time.
Workflows Engineers Can Own End to End
Kestra: YAML that lives in Git
YAML is readable on day 1. Our docs are embedded in the UI for easy reference, the AI Copilot writes workflows for you, or start with our library of Blueprints. Every workflow is a file in your repository, reviewed in pull requests, deployed the same way as application code.
ADF: JSON pipeline definitions in the Azure portal
ADF pipelines are authored in the Azure portal visual designer or as JSON. Version control requires connecting to Azure DevOps or GitHub, exporting pipeline definitions, and committing JSON files manually. The JSON schema is verbose and Azure-specific.
One Platform for Your Entire Technology Stack
Orchestrate data pipelines, infrastructure operations, AI workloads, and business processes across any cloud or on-premises environment. Event-driven at its core, with native triggers for S3, Azure Blob, webhooks, Kafka, database changes, and API events.
ADF is purpose-built for moving and transforming data within the Azure ecosystem. Connect a source, apply a transformation, publish to an Azure sink, all configured in the portal and executed on Azure-managed infrastructure. Cross-system orchestration beyond the Azure data estate requires custom code or an external tool.
Kestra vs. Azure Data Factory at a Glance
| | | |
|---|---|---|
| Deployment model | Self-hosted (Docker, Kubernetes) or Kestra Cloud | Azure-managed SaaS (Azure subscription required) |
| Workflow definition | Declarative YAML | Visual designer or JSON (ARM templates) |
| Version control | Native Git and CI/CD | Requires Azure DevOps or GitHub integration |
| Cloud support | Multi-cloud and on-premises | Azure-primary (connects to other clouds as sources/sinks) |
| Languages supported | Any (Python, SQL, Bash, Go, R, Node.js) | Mapping Data Flows (Spark-based), Databricks notebooks, SSIS packages |
| Open source | Apache 2.0 | No open-source version |
| Infrastructure automation | Native support | Not designed for this |
| Self-service for non-engineers | Kestra Apps | Monitoring UI only |
| Pricing model | Free open-source core (Enterprise tier available) | Consumption-based per activity run and DIU hour |
| Air-gapped deployment | Supported | Not available (Azure-managed only) |
| Multi-tenancy | Namespace isolation + RBAC out-of-box | Resource group isolation with Azure RBAC |
+900%Data production
+250Active users
+5,000Workflows created
Kestra Is Built for How Engineering Teams Work
No Azure subscription required
Kestra runs anywhere Docker runs: AWS, GCP, Azure, on-premises, or a laptop. Multi-cloud teams get one orchestration layer without being tied to a single vendor's pricing, regional footprint, or subscription model. Your workflows deploy identically across environments.
YAML that engineers can own
Kestra workflows are YAML files from day one: they live in your repository, go through code review, and deploy through CI/CD the same way as application code. Every change is a readable diff and every deployment is traceable — no portal required.
Orchestrate beyond data movement
Kestra handles the full lifecycle: ingesting data, running transformations, triggering infrastructure provisioning, coordinating model training, waiting for approvals, and notifying downstream teams — all from a single YAML definition with shared observability and retry logic across every step.
The Right Tool for the Right Job
Choose Kestra When
- Your team works across multiple clouds or on-premises and needs one orchestration layer without Azure lock-in.
- Workflows go beyond data movement: infrastructure automation, AI workloads, and business processes need to run from the same platform.
- Engineers need to own their workflows through Git, pull requests, and CI/CD, not the Azure portal.
- Cost predictability matters. ADF's per-activity pricing compounds quickly at high execution volumes.
- Open source and air-gapped deployment are requirements.
Choose Azure Data Factory When
- Your data estate is Azure-native and you want managed ETL with no infrastructure to operate.
- Deep out-of-the-box integration with Synapse, Azure Data Lake, or SQL Data Warehouse is a priority.
- Your data team is already invested in the Azure portal and prefers visual pipeline authoring.
- SSIS lift-and-shift is on your roadmap. ADF has a built-in SSIS Integration Runtime.
Frequently asked questions
Find answers to your questions right here, and don't hesitate to Contact Us if you couldn't find what you're looking for.
Getting Started with Declarative Orchestration
See how Kestra can simplify your workflows—and scale beyond legacy ETL pipelines.