G2

Nikhil P.

Scholar Trainee

Enterprise (> 1000 emp.)

8/13/2026

"Anyscale Makes Scaling Ray Workloads Smooth and Developer-Friendly"

4.5/5

What do you like best about Anyscale?

What I like most about Anyscale is how much it simplifies scaling AI workloads. The platform feels flexible and developer-friendly for running distributed workloads, and it reduces the operational burden that comes with managing infrastructure. I also appreciate its strong integration with the Ray ecosystem, along with the way it lets me move smoothly from experimentation to production without a lot of friction. Review collected by and hosted on G2.com.

What do you dislike about Anyscale?

One drawback of Anyscale is that it can take some time to get familiar with the platform and its configuration options. For teams that are new to distributed computing or Ray, the learning curve can feel a bit steep, and certain workflows could be made more intuitive and straightforward. Review collected by and hosted on G2.com.

What problems is Anyscale solving and how is that benefiting you?

Anyscale helps address the complexity of building and scaling distributed AI workloads. It makes it easier for us to move from experimentation to production without having to manage as much infrastructure ourselves. As a result, we save engineering time, deployment and scaling are simpler, and we can focus more on improving our models and applications instead of spending effort on infrastructure management. Review collected by and hosted on G2.com.