StackShareStackShare
Follow on
StackShare

Discover and share technology stacks from companies around the world.

Follow on

© 2025 StackShare. All rights reserved.

Product

  • Stacks
  • Tools
  • Feed

Company

  • About
  • Contact

Legal

  • Privacy Policy
  • Terms of Service
  1. Stackups
  2. Application & Data
  3. Databases
  4. Big Data Tools
  5. Mule vs Zato

Mule vs Zato

OverviewComparisonAlternatives

Overview

Mule runtime engine
Mule runtime engine
Stacks127
Followers129
Votes8
Zato
Zato
Stacks12
Followers24
Votes0
GitHub Stars988
Forks246

Mule vs Zato: What are the differences?

Introduction

Mule and Zato are both integration platforms used for building and managing API-centric applications. While they have similar functionalities and goals, there are key differences that set them apart. In this article, we will explore the main differences between Mule and Zato.

  1. Deployment Model: Mule is designed as a standalone runtime engine that can be deployed on-premises, in the cloud, or as a hybrid solution. On the other hand, Zato is a full-featured service integration platform that includes a web server, message queue, and more, providing a holistic solution for integration needs.

  2. Integration Styles: Mule focuses on supporting various integration patterns, such as mediation, orchestration, and event-driven architecture. It provides a visual interface for designing integration flows. Zato, on the other hand, emphasizes a service-oriented architecture (SOA) approach, offering a unified and cohesive framework for building and managing APIs and microservices.

  3. Technology Stack: Mule is built on Java, making it a popular choice for enterprises already using Java-based applications. It also supports other technologies like XML, JSON, and RESTful APIs. Zato, on the other hand, is built on Python and leverages its rich ecosystem. It supports various protocols, such as HTTP, AMQP, and SOAP.

  4. Ease of Use: Mule's visual interface and drag-and-drop capabilities make it user-friendly and accessible to users with limited coding experience. It also offers a wide range of pre-built connectors and templates for simplifying integration tasks. Zato, while also providing a graphical interface, requires more manual configuration and coding. It offers a powerful API for customization and advanced scenarios.

  5. Community and Support: Mule has a large and active community, with plenty of resources, forums, and documentation available. It also has a commercial arm, providing professional support and additional features. Zato, although less popular, has a dedicated community and offers commercial support as well, focusing on maintaining a lightweight and efficient solution.

  6. Pricing Model: Mule follows a traditional pricing model, where users pay for licenses based on their deployment needs and the number of cores utilized. Zato, on the other hand, adopts an open-source model with optional commercial extensions and support packages available for purchase.

In summary, Mule and Zato differ in their deployment model, integration styles, technology stack, ease of use, community and support, as well as pricing model. Each platform has its own strengths and caters to different integration requirements and preferences.

Share your Stack

Help developers discover the tools you use. Get visibility for your team's tech choices and contribute to the community's knowledge.

View Docs
CLI (Node.js)
or
Manual

Detailed Comparison

Mule runtime engine
Mule runtime engine
Zato
Zato

Its mission is to connect the world’s applications, data and devices. It makes connecting anything easy with Anypoint Platform™, the only complete integration platform for SaaS, SOA and APIs. Thousands of organizations in 60 countries, from emerging brands to Global 500 enterprises, use it to innovate faster and gain competitive advantage.

Connect, integrate and automate all of your systems, APIs and apps, including cloud and legacy ones, using an open-source integration platform in Python. ESB, SOA, REST, API and Cloud Integrations in Python.

Connects data;Connects applications;Integration platform;Fast
Integrate everything. In Python.; Connect, integrate and automate all of your systems, APIs and apps, including cloud and legacy ones, using an open-source integration platform in Python.;Say goodbye to integration challenges and hello to peace of mind.
Statistics
GitHub Stars
-
GitHub Stars
988
GitHub Forks
-
GitHub Forks
246
Stacks
127
Stacks
12
Followers
129
Followers
24
Votes
8
Votes
0
Pros & Cons
Pros
  • 4
    Open Source
  • 2
    Microservices
  • 2
    Integration
No community feedback yet
Integrations
CloudApp
CloudApp
API Umbrella
API Umbrella
Zapier
Zapier
Docker
Docker
MySQL
MySQL
Linux
Linux
MSSQL
MSSQL
Microsoft Azure
Microsoft Azure
Amazon S3
Amazon S3
PostgreSQL
PostgreSQL
Odoo
Odoo
Ubuntu
Ubuntu
SQL
SQL

What are some alternatives to Mule runtime engine, Zato?

Apache Spark

Apache Spark

Spark is a fast and general processing engine compatible with Hadoop data. It can run in Hadoop clusters through YARN or Spark's standalone mode, and it can process data in HDFS, HBase, Cassandra, Hive, and any Hadoop InputFormat. It is designed to perform both batch processing (similar to MapReduce) and new workloads like streaming, interactive queries, and machine learning.

Presto

Presto

Distributed SQL Query Engine for Big Data

Amazon Athena

Amazon Athena

Amazon Athena is an interactive query service that makes it easy to analyze data in Amazon S3 using standard SQL. Athena is serverless, so there is no infrastructure to manage, and you pay only for the queries that you run.

Apache Flink

Apache Flink

Apache Flink is an open source system for fast and versatile data analytics in clusters. Flink supports batch and streaming analytics, in one system. Analytical programs can be written in concise and elegant APIs in Java and Scala.

lakeFS

lakeFS

It is an open-source data version control system for data lakes. It provides a “Git for data” platform enabling you to implement best practices from software engineering on your data lake, including branching and merging, CI/CD, and production-like dev/test environments.

Druid

Druid

Druid is a distributed, column-oriented, real-time analytics data store that is commonly used to power exploratory dashboards in multi-tenant environments. Druid excels as a data warehousing solution for fast aggregate queries on petabyte sized data sets. Druid supports a variety of flexible filters, exact calculations, approximate algorithms, and other useful calculations.

Apache Kylin

Apache Kylin

Apache Kylin™ is an open source Distributed Analytics Engine designed to provide SQL interface and multi-dimensional analysis (OLAP) on Hadoop/Spark supporting extremely large datasets, originally contributed from eBay Inc.

Splunk

Splunk

It provides the leading platform for Operational Intelligence. Customers use it to search, monitor, analyze and visualize machine data.

Apache Impala

Apache Impala

Impala is a modern, open source, MPP SQL query engine for Apache Hadoop. Impala is shipped by Cloudera, MapR, and Amazon. With Impala, you can query data, whether stored in HDFS or Apache HBase – including SELECT, JOIN, and aggregate functions – in real time.

Vertica

Vertica

It provides a best-in-class, unified analytics platform that will forever be independent from underlying infrastructure.

Related Comparisons

Bootstrap
Materialize

Bootstrap vs Materialize

Laravel
Django

Django vs Laravel vs Node.js

Bootstrap
Foundation

Bootstrap vs Foundation vs Material UI

Node.js
Spring Boot

Node.js vs Spring-Boot

Liquibase
Flyway

Flyway vs Liquibase