Temporal is a durable execution platform built for engineers who want to express long-running, stateful workflows in code. Kestra is a declarative orchestration platform built for teams who want to schedule, trigger, and monitor workflows in YAML, without writing SDK code or managing workflow state by hand.
YAML describes what should run, when, and in what order. Business logic lives in your existing Python, SQL, Bash, or dbt scripts. Kestra handles scheduling, retries, dependencies, and observability as first-class YAML primitives. Non-engineers can read, trigger, and monitor any workflow without touching code.
Temporal workflows are code: Go, Java, Python, TypeScript, or Ruby functions decorated with durability guarantees. The Temporal server persists state at every step, so workflows survive crashes and resume exactly where they left off. This makes Temporal exceptionally strong for long-running stateful processes, compensation patterns, and distributed application logic that must be expressed in a programming language.
Temporal also offers Temporal Cloud, a fully managed SaaS option. This comparison uses their self-hosted local development path—the same deployment model as Kestra's local setup—which requires installing the Temporal CLI, starting a development server, and writing Workflow and Activity code before anything runs.
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 SDK to install, no workflow code to write before you can see anything execute.
# Install Temporal CLIbrew install temporal
# Start local dev servertemporal server start-dev
# In another terminal: set up your SDK projectpip install temporalio
# Write Workflow + Activity code, register worker,# then trigger execution via CLI or SDK client...Install the Temporal CLI, start a local development server, choose a language SDK, set up your project structure, write Workflow and Activity definitions in code, then register and run the worker. Each step requires language-specific tooling.
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. Any team member can read the YAML and understand what a workflow does, no SDK knowledge required.
Temporal workflows are Python (or Go, Java, TypeScript) classes decorated with durability primitives. Activities handle individual tasks; Workflows coordinate them. The code is clean and testable, but authoring and modifying workflows requires SDK knowledge. Non-engineers can view execution history in the Temporal Web UI but cannot modify workflow logic.
Orchestrate data pipelines, infrastructure operations, business processes, and AI workflows from one platform. Event-driven at its core, with native triggers for file arrivals, Kafka, webhooks, and schedules, all defined in YAML alongside the workflow logic.
A durable execution engine for long-running stateful workflows expressed in code. Strong for saga patterns, distributed application logic, and workflows that need to survive infrastructure failures across days or weeks. Not designed for data pipeline orchestration or participation from non-engineers.
| | | |
|---|---|---|
| Workflow definition | Declarative YAML | Code (Go, Java, Python, TypeScript, Ruby, C#, PHP) |
| Primary use case | Data pipelines, infrastructure automation, business workflows | Long-running stateful workflows and distributed application logic |
| Data pipeline tooling | Native support (dbt, Airbyte, Spark, Snowflake, 1200+ plugins) | No native data pipeline plugins |
| Event-driven triggers | Native (schedules, file arrivals, Kafka, webhooks) | Signals and timers within workflow code; no YAML trigger primitives |
| Durable execution | Retries, timeouts, and failure handling in YAML | ✓ Automatic state persistence and deterministic replay |
| Self-service for non-engineers | Kestra Apps | No self-service interface |
| Infrastructure automation | Native support | Possible through workflow code, not a primary use case |
| Multi-tenancy | Namespace isolation + RBAC out-of-box | Namespace isolation available; RBAC via Temporal Cloud |
| Managed cloud option | Kestra Cloud | Temporal Cloud |
Kestra delivers end-to-end automation with the robustness we need at our scale. Few companies operate at this level, especially in AI/ML.
Kestra ships with native plugins for dbt, Airbyte, Spark, Snowflake, BigQuery, Kafka, Databricks, and 1200+ more. Run a dbt model, trigger an Airbyte sync, or load to Snowflake in a YAML task with no extra SDK to install. Every integration is a pre-built task type, not custom code your team writes and maintains.
A Kestra workflow is a YAML file that any team member can read and understand. Kestra Apps let business users trigger workflows, fill in parameters, and monitor results without engineering involvement. RBAC and namespace isolation keep business-user workflows separate from engineering infrastructure without restricting access to either.
Kestra's trigger system covers cron schedules, file arrivals in S3 or GCS, Kafka message consumption, webhook callbacks, and database row changes, all defined in YAML alongside the workflow. Triggers, tasks, and retry logic live in one file with no separate scheduling API or external coordinator required.
Find answers to your questions right here, and don't hesitate to Contact Us if you couldn't find what you're looking for.
See how Kestra can simplify your workflows—and scale beyond durable execution.