DMARC-SRG, written in PHP, provides a clear web-based interface for viewing aggregated DMARC reports. It excels at breaking down authentication results (SPF, DKIM, DMARC) and identifying sending sources, offering a foundational overview of DMARC compliance. It is designed to be straightforward to set up and provides the essential visualisations needed to understand DMARC data.

Its core strength lies in its simplicity and ability to render complex DMARC XML reports into human-readable summaries, making it accessible for those comfortable with PHP environments. While it covers the basics well, we find it focuses primarily on report aggregation and visualization, rather than advanced features like threat intelligence or highly customized alerting mechanisms.

Parseddmarc, a Python package, focuses on the parsing and processing of DMARC reports via a command-line interface. It is highly configurable, allowing users to define how reports are ingested (IMAP, Gmail API, Microsoft Graph) and where the parsed data is outputted (JSON, CSV, Elasticsearch, Splunk, Kafka). This flexibility means it can be integrated into existing data pipelines and monitoring systems.

We appreciate its modular design, which allows for powerful custom scripting and integration with other tools for advanced analytics or alerting. While it requires more initial setup and technical expertise due to its CLI nature, its extensibility makes it a robust backend for DMARC data processing, especially for organizations with specific data handling requirements or a preference for Python-based solutions.

The user experience with DMARC-SRG is relatively direct, especially for those familiar with self-hosting PHP applications. The web interface is functional and provides a clear, albeit somewhat basic, visualization of DMARC data. Navigating through reports, viewing authentication failures, and identifying sending sources is intuitive. The interface prioritizes clarity over advanced interactivity, which can be a double-edged sword: simple to use for basic tasks, but lacking in deeper analytical tools.

For initial setup, it requires a web server with PHP, a database, and cron jobs for report fetching and processing. Once configured, daily interaction typically involves logging into the web interface to review the aggregated data. While it might not win any design awards, its utilitarian approach means that the core DMARC information is readily accessible without unnecessary clutter.

Parseddmarc's user experience is primarily for developers and system administrators comfortable with command-line tools and scripting. There is no graphical user interface out of the box, meaning all interaction is through the terminal and configuration files. This provides immense power and flexibility, but inherently steepens the learning curve for those unaccustomed to such environments. Its strength is in automation and integration.

To effectively use Parseddmarc, one must configure Python scripts, manage cron jobs for execution, and potentially set up external databases or visualization tools like Elasticsearch and Kibana for a complete reporting solution. While this demands more technical involvement initially, the reward is a highly customized and automated DMARC processing pipeline that fits seamlessly into an existing infrastructure. We found that once the initial setup is complete, it runs reliably in the background, requiring less direct 'user interaction' than a web-based dashboard.

As an open-source project, support for DMARC-SRG primarily comes from the community and the project's maintainers via GitHub issues. While the project is well-documented for installation and basic usage, immediate, dedicated support channels are not available. Users are expected to troubleshoot issues independently or rely on contributions and discussions within the open-source community.

We've found that for common issues, solutions can often be found in existing issue threads or the documentation. However, for unique configurations or complex debugging, it requires a strong understanding of PHP, server environments, and DMARC principles to resolve. This model is typical for self-hosted open-source tools and necessitates internal expertise.

Similar to DMARC-SRG, Parseddmarc is an open-source project, and its support model follows suit. Assistance is primarily through its GitHub repository, where issues can be raised and discussions among users and maintainers take place. The project's documentation is thorough, particularly regarding installation, configuration, and integration options, which is crucial given its command-line nature.

For specific implementation challenges or integrating with various data sinks (like Elasticsearch), users will leverage the provided examples and community knowledge. While direct, paid support is not offered, the Python community is generally very active and resourceful. We recommend having Python development or DevOps expertise available to leverage Parseddmarc to its fullest and address any operational challenges efficiently.

DMARC-SRG is best suited for individuals or small to medium-sized businesses (SMBs) who need a cost-effective, self-hosted DMARC reporting solution and possess a moderate level of technical proficiency, particularly with PHP and web server management. It's a great choice for those who want to get a visual overview of their DMARC compliance without committing to a commercial service.

MSPs: Less suitable for MSPs due to the lack of built-in multi-tenancy and advanced client management features. Would require significant custom development per client.

Enterprise: Not ideal for enterprise environments that often require advanced features, dedicated support, and robust scalability beyond what a basic PHP application offers without extensive customization.

SMB: Highly suitable for SMBs looking for a basic, self-managed DMARC overview.

Parseddmarc is an excellent fit for technically proficient users, developers, and organizations that prefer a Python-based ecosystem and require a highly customizable DMARC data processing backend. It's ideal for integrating DMARC data into existing security information and event management (SIEM) systems or custom reporting dashboards.

MSPs: Potentially suitable for MSPs if they have strong Python/DevOps capabilities and can build a multi-tenant layer on top of Parseddmarc for their clients, integrating it into their existing monitoring stacks.

Enterprise: Well-suited for enterprise organizations with a strong DevOps culture and the internal resources to integrate and manage a custom DMARC data pipeline, especially when combined with other data analytics platforms.

SMB: Less suitable for SMBs without dedicated IT or development staff, as its command-line interface and integration focus require a higher technical skill set for setup and ongoing management.