The differences between Kestra and Google Workflows

How to Choose the Right Orchestration Platform

A screenshot of the user interface of Kestra's application

About Kestra & Google Workflows

Kestra is an open-source orchestration platform that enables the automation of complex workflows across cloud, on-prem, and hybrid environments. It follows an everything-as-code approach, with YAML-based workflow definitions, an API-first architecture, and an extensible plugin ecosystem. Combining a no-code/low-code UI with direct YAML editing, Kestra provides flexibility for both technical and non-technical users.

Google Workflows, a cloud-based orchestration service provided by Google Cloud, focuses on sequencing steps for Google Cloud services. While it offers a simple YAML or JSON definition for workflows, it lacks critical enterprise-grade orchestration features. With no built-in UI for workflow design, debugging, or real-time observability, and no native plugin ecosystem, Google Workflows is best suited for Google Cloud-centric tasks but struggles in multi-cloud or hybrid environments.

Installation and Setup

Kestra provides a highly flexible deployment model, supporting Docker, Kubernetes, and Terraform. It can run on any cloud provider, on-premises, or in hybrid environments, ensuring full control over execution, security, and data residency.

Google Workflows, on the other hand, is fully managed by Google Cloud, with no option for on-prem installation. Setting it up requires enabling the Workflows API within a Google Cloud project. However, using it outside of Google Cloud demands extensive development effort, including building web services to create bridges between external systems.

Workflow Definition

Kestra workflows are defined in YAML, offering clarity and maintainability. With inline scripting in Python, JavaScript, SQL, and Bash, users can integrate custom logic directly. Its no-code editor allows workflows to be designed visually while automatically generating the corresponding YAML code for version control. Additionally, Kestra’s plugin ecosystem extends workflow capabilities beyond default functions.

Google Workflows also uses YAML or JSON but lacks a UI-based workflow designer. Its built-in DSL (domain-specific language) offers limited flexibility, requiring users to implement custom logic through REST calls or external services. Unlike Kestra, it does not have a plugin system, making extending its functionality more cumbersome. Pebble templating support is also limited, restricting customization options.

Scalability and Performance

Kestra dynamically scales across distributed environments, optimized for both event-driven and scheduled workflows. Its open architecture ensures seamless operation across multiple clouds or on-prem environments without vendor-imposed restrictions.

Google Workflows leverages Google Cloud’s infrastructure for scaling but remains tied to the GCP ecosystem. While it can handle large-scale tasks within Google Cloud, orchestrating workflows outside of GCP introduces additional complexity, including costs and development overhead.

Integration Capabilities

Kestra provides extensive integrations through its plugin ecosystem, supporting AWS, GCP, Azure, BigQuery, Snowflake, Kafka, dbt, Airbyte, and more. With an API-first approach, it facilitates seamless orchestration across different cloud providers, on-prem infrastructure, and SaaS applications.

Google Workflows is deeply integrated into Google Cloud services such as Cloud Functions, Cloud Run, and Google APIs. However, it does not support native plugins for third-party services. To connect with external platforms, users must implement custom REST calls, adding complexity and maintenance overhead.

Monitoring and Observability

Kestra offers built-in monitoring, real-time execution tracking, and logging. Users can visualize workflow performance through custom dashboards and access detailed task-level logs for easier debugging.

Google Workflows relies on Google Cloud Logging and Monitoring for observability, requiring users to configure multiple services to track workflow execution. Without a dedicated UI for debugging, troubleshooting issues is more complex compared to Kestra’s out-of-the-box solutions.

Open-Source vs. Proprietary

Kestra is a fully open-source platform with an active community and optional enterprise add-ons. Its open architecture allows organizations to deploy in any environment without vendor lock-in. The transparency of its codebase enables deeper customization and community-driven enhancements.

Google Workflows is a proprietary service tied to Google Cloud. Organizations using it are locked into GCP’s infrastructure, APIs, and billing model, with limited extensibility beyond Google’s ecosystem.

Feature Kestra Google Workflows
Scalability Runs on Kubernetes with dynamic scaling across cloud and on-prem environments Scales within GCP, but can become expensive and rigid for multi-cloud or hybrid workloads
Cloud Integrations Multi-cloud support (AWS, GCP, Azure, on-prem); integrates with event-driven and scheduled workflows Deeply integrated with GCP services but requires complex workarounds for multi-cloud or external system integrations
Vendor Lock-in Fully open-source, cloud-agnostic, and self-hostable Proprietary solution locked into GCP; integrating outside Google services requires additional development
Triggering System Built-in event triggers, webhooks, and scheduling No native triggering system; users must develop web services to enable triggers
Managing Dependencies Between Workflows Supports dependency management natively; allows workflows to trigger other workflows No native dependency management between DAGs; users must build custom logic to coordinate workflows
Custom Development & Extensibility Fully customizable with open-source plugins, integrations, and custom tasks No support for custom development beyond standard Airflow DAGs
Query Execution Complexity Simple queries are easily handled via inline SQL scripting or built-in integrations Even basic queries require defining operators, writing custom Python scripts, and managing task dependencies manually
Templating (Pebble) Customizable templating system for workflow parameters and task execution Limited templating flexibility, requiring workarounds to adapt logic