Atlan Reviews

Atlan's pricing model is per-user, contrasting with others charging based on resources. Users find it competitively priced against platforms like Collibra and Informatica, though it can be higher than newer options like Castor. Mid-sized and large enterprises appreciate its cost-effectiveness, with licensing flexibility and potential discounts enhancing appeal. However, smaller organizations may find it less affordable. While lacking an integrated data quality module, pricing is still considered reasonable for its features.

• "We pay per-user license. It's a different classification model than with other solutions, where they usually charge you for resources. So, that was a better model for us. And because of this difference in models or classification, it was cheaper for us to go with Atlan."

Organizations primarily use Atlan for its integration capabilities and user-friendly interface, enhancing productivity. Teams utilize it for data cataloging, metadata management, and business glossary creation, facilitating data quality analysis and lineage tracking. With Atlan, manual tasks are automated and error-free. Additionally, it serves as a central hub for data discovery, governance, and impact analysis. Companies also rely on Atlan to unify data from various sources and support asset management and knowledge sharing.

Atlan customer support is generally well-regarded, providing timely responses and solutions. Users note quick and effective assistance with questions and technical issues. While some find the support team knowledgeable, the ticketing process needs improvement. A few users have experienced delays, potentially due to being in a trial phase. Others mention a lack of proactiveness, suggesting enhancement in responsiveness. Overall, support appears effective, addressing customer needs efficiently and accommodating customized requirements.

Atlan's initial setup is simple due to its cloud-based nature on AWS, eliminating the need for infrastructure. Users appreciate the ease of use and fast deployment, especially when utilizing connectors for asset cataloging. Setup is often initiated as a minimum viable product with basic capabilities, later expanded through training super users. Some find the cloud instance installation straightforward, though on-premises setups require additional support from technical teams.

Atlan is noted for its scalability, supporting a wide range of professionals seamlessly. User addition is manageable, aligning with organizational needs. Recent updates enhance its scalability through data contracts. While its cloud-native infrastructure scales effectively, larger datasets pose challenges without architectural support. Atlan suits agile companies and supports growth, requiring occasional human intervention. There are no significant negative experiences mentioned, offering a high rating for scalability.

Atlan demonstrates high stability with minimal connectivity issues and consistent availability, allowing timely access to datasets. While the connectors perform well, playbooks sometimes encounter scaling challenges with automated tests. New feature rollouts occasionally impact performance, but timely optimizations ensure stable operations. The infrastructure might need adjustments when managing extensive metadata. Issues with the ML classification system during POC were addressed effectively, resulting in improved functionality. Many users report no downtime experiences.