Compare ClearML and Valohai 
- Users report that Valohai excels in Quality of Support with a score of 9.9, while ClearML, although still strong, has a lower score of 9.0. Reviewers mention that Valohai's support team is highly responsive and knowledgeable, making it easier for users to resolve issues quickly.
- Reviewers mention that Valohai offers superior Ease of Use with a score of 9.3 compared to ClearML's 8.9. Users on G2 highlight that Valohai's user interface is intuitive, which significantly reduces the learning curve for new users.
- Users say that ClearML shines in Language Flexibility with a perfect score of 10.0, while Valohai scores 9.6. Reviewers appreciate ClearML's support for multiple programming languages, which allows for greater versatility in project development.
- G2 users report that Valohai has a slight edge in Versioning with a score of 9.6, while ClearML scores 10.0. However, users mention that Valohai's version control features are robust and user-friendly, making it easier to manage model iterations.
- Users on G2 highlight that ClearML is more scalable, scoring 10.0 compared to Valohai's 9.4. Reviewers mention that ClearML's architecture allows for seamless scaling of projects, which is crucial for growing businesses.
- Reviewers mention that Valohai's Monitoring capabilities are superior, scoring 9.6 against ClearML's 8.3. Users report that Valohai provides comprehensive monitoring tools that help track model performance effectively, which is essential for maintaining high-quality outputs.
- Unlimited Usage
- Knowledge Repository
- Smart Orchestration (Your cloud or on-premise)
ClearML allows for more autonomy without dependence on the engineering team. Setup time is quick, and recording experiments and submitting jobs to the queues is easy. It helps my team work more efficiently and collaborate much better.
We have the on-prem version and it works well, but took a lot of work to get set up initially by our infrastructure team. This is understandable though, and we're just happy that ClearML supports on-prem deployments.
ML engineers @Floy use Valohai as backbone for developing and evaluating medical AI for radiology imaging data. Valohai allows us not only a seamless integration in our workflows but also the use of our own compute infrastructure. Additionally, clever...
ClearML allows for more autonomy without dependence on the engineering team. Setup time is quick, and recording experiments and submitting jobs to the queues is easy. It helps my team work more efficiently and collaborate much better.
ML engineers @Floy use Valohai as backbone for developing and evaluating medical AI for radiology imaging data. Valohai allows us not only a seamless integration in our workflows but also the use of our own compute infrastructure. Additionally, clever...
We have the on-prem version and it works well, but took a lot of work to get set up initially by our infrastructure team. This is understandable though, and we're just happy that ClearML supports on-prem deployments.
