The differences between Kestra and Google Workflows

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.

Key Takeaways

• UI & Workflow Management: Kestra offers a no-code UI and YAML-based workflow definitions, while Google Cloud Composer requires Airflow DAGs written in Python without any visual interface.
• Extensibility & Custom Development: Kestra allows for custom development via plugins, scripting, and modular workflows, whereas Composer is rigid, requiring extensive development to extend functionality.
• Multi-Cloud & Vendor Lock-In: Kestra runs anywhere (AWS, GCP, Azure, on-prem), while Composer is deeply tied to Google Cloud, making hybrid or multi-cloud orchestration difficult.
• Debugging & Monitoring: Kestra provides real-time logs and execution tracking, whereas Composer users must navigate multiple GCP services to gather logs and troubleshoot workflows.
• Triggering & Dependencies: Kestra natively supports triggers and dependency management, while Composer lacks built-in triggering, requiring additional services to bridge workflows.

If you are fully committed to Google Cloud and need basic workflow sequencing for GCP services with minimal external dependencies, Google Workflows may be sufficient, but with significant limitations in flexibility, monitoring, and customization.