DMARC-SRG provides a straightforward PHP parser designed to process incoming DMARC aggregate reports. Its core functionality revolves around extracting data from these XML reports and presenting it in a more human-readable format, primarily as summary reports.

We found that its feature set is quite basic, focusing on fundamental DMARC analysis like showing pass/fail rates for SPF and DKIM, and identifying sending sources. It's a tool built for simplicity, offering a functional but bare-bones approach to DMARC data visualization.

Open-DMARC-Analyzer aims to be a full-featured DMARC report analyzer, offering more detailed insights compared to its counterpart. It processes both aggregate and forensic DMARC reports, providing a broader view of email traffic and potential threats.

Our experience shows it goes deeper into data visualization, offering filtering capabilities and more granular reporting. It requires a web server with PHP and MariaDB, indicating a more robust, database-driven approach to storing and analyzing DMARC data, which enables richer features.

Using DMARC-SRG primarily involves command-line operations or scripting to ingest XML reports and generate summaries. While it does have a web viewer, the initial setup and ongoing report processing require a degree of technical comfort with server environments.

The interface is minimalistic, serving its purpose without much in the way of advanced graphical elements or interactive features. It's functional, but certainly not designed for users who prefer a point-and-click experience. We found it best suited for those who appreciate direct access and control over their data.

Open-DMARC-Analyzer offers a web-based graphical user interface (GUI), making it more accessible than command-line-centric tools. Once installed, we found navigating its dashboards and reports relatively intuitive, allowing for easier filtering and data exploration.

However, the 'user experience' extends to the initial setup, which still requires significant technical expertise in setting up a web server, PHP, and a MariaDB database. While the daily usage is better, the deployment barrier is substantial for non-technical users.

As an open-source project, DMARC-SRG relies entirely on community support. Users facing issues typically turn to the GitHub repository's issue tracker for assistance, or consult existing documentation and examples.

There is no dedicated support team or official channels for DMARC-SRG. Our experience confirms that problem-solving often involves self-research and relying on contributions from other users. This model works for those comfortable with DIY, but can be a challenge when facing complex or urgent issues.

Open-DMARC-Analyzer also operates within an open-source framework, meaning support is community-driven. Users can seek help through its GitHub page, accessing issue logs, wikis, or potentially community forums if they exist.

While there's a community aspect, we found that the level of active support can vary, similar to many open-source projects. For intricate problems or specific configurations, users should expect to do their own troubleshooting, relying heavily on available documentation and their own technical skills.

DMARC-SRG is best suited for individuals or small teams with strong technical skills who need a simple, self-hosted solution to parse DMARC reports. It's ideal for learning the basics of DMARC reporting or for very low-volume domains where detailed analysis isn't critical.

It is generally not suitable for MSPs (managed service providers) or enterprise environments due to its limited features, lack of multi-tenancy, and absence of professional support. Even for SMBs, it requires dedicated IT resources to set up and maintain, making it less practical for many.

Open-DMARC-Analyzer is a good fit for organizations, typically SMBs, that prioritize data privacy and full control by self-hosting their DMARC reports. It's for those with the technical expertise and infrastructure to manage their own web server and database.

While technically usable by MSPs, it lacks native multi-tenancy and robust client management features, requiring significant custom development to scale. For enterprise, it may fall short on advanced features like integrated blocklist (blacklist) monitoring, AI-driven insights, or vendor-backed support typically expected at that level.