Amazon Managed Workflows for Apache Airflow vs Zluri
Overview
🔥 Trending in Task Scheduling on StackShare
A platform to programmaticaly author, schedule and monitor data pipelines, by Airbnb
Detailed Comparison
It is a managed orchestration service for Apache Airflow1 that makes it easier to set up and operate end-to-end data pipelines in the cloud at scale. With Managed Workflows, you can use Airflow and Python to create workflows without having to manage the underlying infrastructure for scalability, availability, and security. Managed Workflows automatically scales its workflow execution capacity to meet your needs, and is integrated with AWS security services to help provide you with fast and secure access to data. | It is a SaaS Management Platform (SMP) that helps you discover, manage, optimize, and secure your SaaS stack from a single dashboard. It helps you automate your IT tasks, expel SaaS wastage, and secure your SaaS ecosystem. |
Deploy Airflow rapidly at scale; Run Airflow with built-in security; Reduce operational costs; Use a pre-existing plugin or use your own | SaaS visibility; SaaS automation; Alerting; Usage control; Access monitoring |
Statistics | |
Stacks 21 | Stacks 1 |
Followers 13 | Followers 2 |
Votes 0 | Votes 0 |
Integrations | |
What are some alternatives to Amazon Managed Workflows for Apache Airflow, Zluri?
Airflow
Use Airflow to author workflows as directed acyclic graphs (DAGs) of tasks. The Airflow scheduler executes your tasks on an array of workers while following the specified dependencies. Rich command lines utilities makes performing complex surgeries on DAGs a snap. The rich user interface makes it easy to visualize pipelines running in production, monitor progress and troubleshoot issues when needed.
GitHub Actions
It makes it easy to automate all your software workflows, now with world-class CI/CD. Build, test, and deploy your code right from GitHub. Make code reviews, branch management, and issue triaging work the way you want.
Apache Beam
It implements batch and streaming data processing jobs that run on any execution engine. It executes pipelines on multiple execution environments.
Zenaton
Developer framework to orchestrate multiple services and APIs into your software application using logic triggered by events and time. Build ETL processes, A/B testing, real-time alerts and personalized user experiences with custom logic.
Luigi
It is a Python module that helps you build complex pipelines of batch jobs. It handles dependency resolution, workflow management, visualization etc. It also comes with Hadoop support built in.
Unito
Build and map powerful workflows across tools to save your team time. No coding required. Create rules to define what information flows between each of your tools, in minutes.
Shipyard
na
AI Autopilot
Agentic AI Platform for Intelligent IT Automation built by MSPs for MSPs. Revolutionize your operations with advanced AI agents.
PromptX
PromptX is an AI-powered enterprise knowledge and workflow platform that helps organizations search, discover and act on information with speed and accuracy. It unifies data from SharePoint, Google Drive, email, cloud systems and legacy databases into one secure Enterprise Knowledge System. Using generative and agentic AI, users can ask natural language questions and receive context-rich, verifiable answers in seconds. PromptX ingests and enriches content with semantic tagging, entity recognition and knowledge cards, turning unstructured data into actionable insights. With adaptive prompts, collaborative workspaces and AI-driven workflows, teams make faster, data-backed decisions. The platform includes RBAC, SSO, audit trails and compliance-ready AI governance, and integrates with any LLM or external search engine. It supports cloud, hybrid and on-premise deployments for healthcare, public sector, finance and enterprise service providers. PromptX converts disconnected data into trusted and actionable intelligence, bringing search, collaboration and automation into a single unified experience.
Camunda
Camunda enables organizations to operationalize and automate AI, integrating human tasks, existing and future systems without compromising security, governance, or innovation.
