Selecting a deployment pattern for data editing and management systems

Data editing and management systems are typically deployed using one of four deployment patterns:

Selecting a deployment pattern is one of the most important decisions to make in designing a GIS system for your organization.

Perhaps the most critical factor in this decision will be aligning with your organization’s IT principles, guidelines, and comfort-level in supporting different deployment approaches. For example, some organizations may prefer to standardize on SaaS-based systems and solutions. Other organizations that are investing heavily into Kubernetes-based deployments, including hiring and training staff with operational Kubernetes experience and skills, may prefer Kubernetes-based deployment patterns. Whereas organizations that are building GIS capabilities into their existing applications may prefer a PaaS-based approach.


The capabilities as well as the considerations differ significantly between deployment patterns. Review the comparisons below, along with the deployment pattern pages for additional information.

For general information and considerations around these deployment approaches see the ArcGIS products and deployment options page of the ArcGIS overview.

Capability comparison

In addition to aligning with your IT principles, guidelines, and comfort-level, it’s also important to consider the capabilities of each deployment pattern in your decision-making process. The capabilities of a data editing and management system differ between deployment patterns. The following matrix compares the specific capabilities supported by each of the deployment pattern.

Capabilities used in a data editing and management system, but typically provided by other systems, such as basemaps, geocoding, and other location services provided by a location services system are not listed below. Learn more about related system patterns.

Capability SaaS PaaS Windows/Linux Kubernetes
Mapping and visualization
Data editing
Data import and export
Data interoperability and transformation1    
Edit tracking and auditing
Short transaction management
Long transaction management    
Spatial and attribute rules    
Data distribution and replication  
Data archiving and history    
Advanced data validation      
Workflow management and automation   2 2,3
Hosted Python notebooks   4  
Utility networks     3
Parcel management     3
Roads and highways     3
Pipeline referencing     3
Defense mapping     3
Production mapping5     3
Indoor GIS5  
Other industry solutions6  

Full support

Partial support

  1. ArcGIS Data Interoperability for ArcGIS Pro supported in all deployment patterns 

  2. Requires ArcGIS Workflow Manager Server  2

  3. Requires some Windows/Linux infrastructure  2 3 4 5 6 7

  4. Requires ArcGIS Notebook Server 

  5. Full capability spans multiple system patterns  2

  6. Some industry solutions only available on SaaS 

See the data editing and management system capabilities for more information on each row listed above. Additionally, each of the cells above is described in more detail in the data editing and management system deployment pattern pages.

The capabilities represented above reflect those available as of December, 2023.

General considerations

The considerations below aim to help align your organization’s business and IT needs with the appropriate data editing and management system deployment pattern. The information presented here is not meant to be exhaustive, but rather highlights key considerations for designing and implementing data editing and management systems.

  • Scalability, reliability, service level agreements (SLA), security, and the balance of responsibility between your organization and Esri tend to be major factors in selecting a deployment pattern. Please see the reliability, performance and scalability, and security pillars for more information.
  • Organizations with a moderate-to-large editor workforce or who have needs for long transactions, conflict detection and reconciliation, spatial and attribute rules, as well as archiving and advanced auditing or edit lineage tend to favor an ArcGIS Enterprise-based deployment on Windows, Linux, or Kubernetes. These two deployment patterns offer the most robust data editing and management capabilities and allow for flexible alignment with workflows and governance needs.
  • Crowdsourcing-style data collection scenarios can be supported by all deployment patterns; however, organizations working with a broad spectrum of users inside and outside of their organizational boundary tend to favor a SaaS or PaaS based deployment pattern. Both patterns are secure and internet accessible, allowing user access without direct access to an organization’s network. They also both scale seamlessly, and automatically, allowing for very large outreach and user engagement. The SaaS pattern in particular supports social logins and identity for external users, providing additional benefits for both auditing as well as community engagement.
  • The SaaS deployment pattern using ArcGIS Online features the quickest time to market. The capabilities it provides can be stood up very quickly, and the variety of applications it includes supports a wide range of workflows and user needs. While the data editing and management capabilities it provides are subset of those provided by the ArcGIS Enterprise-based deployments on Windows, Linux, and Kubernetes, they tend to be suitable for many data editing and management scenarios.
  • The PaaS deployment pattern using ArcGIS Platform is geared to organizations looking to build their own data editing and management applications, or to integrate data editing and management capabilities into existing systems and/or applications. There are some notable capability and consideration differences between the SaaS and PaaS deployment patterns, so please review those details carefully in the deployment pattern pages.
  • There are numerous industry-specific capabilities to consider when implementing a data editing and management system. As these capabilities differ between deployment patterns, review those details carefully in the deployment pattern pages. Here are some key considerations at a glance:
    • Esri provides numerous industry-specific configurations of ArcGIS through the ArcGIS Solutions program. These are generally available for SaaS, Windows, Linux, and Kubernetes deployment patterns.
    • Esri provides several advanced industry-specific data models and software components for utility network, parcel management, roads and highways, and pipeline management. These are only available for Windows and Linux, though may also be used in a Kubernetes and Windows/Linux hybrid deployment. These advanced industry specific data models and software are not available for SaaS or PaaS-based deployment patterns.
    • Esri provides a data editing and management system for indoor space planning and management through both SaaS and software-based deployment patterns called ArcGIS Indoor Spaces. This system is typically leveraged in combination with other systems and components to implement a full indoor GIS solution through ArcGIS Indoors.
    • For more information on Esri’s support for various industries in general, see the Industries page.

In addition to some of the differences described above, some other extended capabilities differ notably between deployment patterns. Some differences to consider include:

  • The workflow management and automation is typically provided by ArcGIS Workflow Manager, which has both ArcGIS Online (SaaS) and ArcGIS Enterprise (Windows/Linux) deployment options. The capabilities of these two options differ, so please review product documentation carefully in evaluating options. Additionally, while ArcGIS Workflow Manager for ArcGIS Enterprise is only available on Windows and Linux, it may be integrated with a Kubernetes-based deployment pattern for a Kubernetes and Windows/Linux hybrid deployment.
  • Python-based analysis and automation is supported by all deployment patterns using the ArcGIS API for Python. The SaaS and Windows/Linux deployment patterns also support hosted Python notebooks managed within the GIS system, which provides additional capabilities and integration opportunities, such as the scheduling of notebooks. Please see ArcGIS Notebooks for more information. Hosted Python notebooks are not currently supported for PaaS and Kubernetes deployment patterns.

Selecting a deployment pattern is one of the most important decisions to make in designing a GIS system for your organization. However, it is not the only one. There are many additional factors to take into consideration when designing your system, including areas like security, reliability, and integration. As such, please don’t consider the information provided here to be exhaustive. Review the architecture practices and pillars of the ArcGIS Well-Architected Framework, as well as product documentation, in detail as part of your design process.