Anyscale Platform, Powered by Ray

What do you like best about the product?

I like Anyscale’s UI because it’s very clean and easy to understand. I also like being able to see workspaces, jobs, and services all in one place, which makes my work much easier.

What do you dislike about the product?

I dislike it when Anyscale feels slow while I’m checking larger workloads. I also find that the pricing gets high as my usage increases.

What problems is the product solving and how is that benefiting you?

Anyscale makes it easier for me to manage my workloads and the IT infrastructure they require. It saves me time and reduces manual effort, especially when I need to manage resources and scale them up or down.

What do you like best about the product?

I really like how Anyscale makes distributed ML workloads easier to run and scale. It allows our team to focus more on model development instead of spending time managing clusters and compute. It's especially useful when we need to quickly scale training or inference for larger workloads. It also saves us a lot of infrastructure work, making experiments faster to run and letting our ML engineers spend more time on models instead of cluster management.

What do you dislike about the product?

The learning curve can be a bit steep, especially for team members who are new to Ray and distributed computing. We have also had some situations where troubleshooting a failed job was not as straightforward as we'd like. Better error messages, debugging tools, and simpler documentation for common issues would make the experience smoother.

What problems is the product solving and how is that benefiting you?

I use Anyscale to develop, train, and deploy machine learning models at scale. It handles scalability and infrastructure, allowing us to run distributed workloads efficiently, saving time on setup and enabling faster experimentation without managing infrastructure manually.

What do you like best about the product?

I like anyscale UI, also it makes it easier to run and scale AI workloads without having to manage everything manually.

What do you dislike about the product?

I mostly dislike its pricing. Also, at first it took me some time to understand all of its features, which made me feel some discomfort.

What problems is the product solving and how is that benefiting you?

It helps me reduce the hassle of managing infrastructure and scaling AI workloads. This lets me focus more on building the application, instead of dealing with setup and maintenance.

What do you like best about the product?

I like that Anyscale automates our US staffing tasks using AI, which helps quickly review large amounts of resume data. It makes our hiring process faster and easier by automatically organizing candidate details and matching the right people to jobs, so we can focus on finding the best talent quickly. The tool manages big workloads easily, is simple to use, speeds up data processing, and scales up when needed. It integrates well with existing tools like Python, Slack, AWS, and Github, which is great because it doesn't require learning new systems. Its flexibility allows it to fit our busy times, and it's safe and saves a lot of time by doing boring work for us. The initial setup was quite easy and straightforward.

What do you dislike about the product?

The system is too hard to learn and set up. Advanced features take too much time to figure out for our specific workflows. We need better guides and real examples. We also need simpler tools to check and manage our workloads so we can fix problems faster.

What problems is the product solving and how is that benefiting you?

Anyscale automates our staffing tasks, speeding up data processing and matching candidates efficiently. It handles repetitive tasks, allowing our team to focus on selecting top talent, but setting it up and learning advanced features can be challenging. I wish for better guides and simpler management tools.

What do you like best about the product?

Powerful open-source software (Ray) that works well in production. The engineering team is smart and clearly knows what they’re doing.

What do you dislike about the product?

Startup life isn’t for everyone. The pace can be challenging for some.

What problems is the product solving and how is that benefiting you?

It eliminates the heavy DevOps burden of manually setting up, scaling, and managing multi-node CPU/GPU clusters.

What do you like best about the product?

You spend more time on the code/workflow and less on the plumbing of provisioning/maintaining distributed resources.

What do you dislike about the product?

ven with managed infrastructure, you still need to think about cluster sizing, concurrency, autoscaling behavior, failure handling, and workload/resource patterns.

What problems is the product solving and how is that benefiting you?

Anyscale is primarily solving the “how do we run Ray reliably at scale?” problem—especially when moving from experimentation to production workloads.

What do you like best about the product?

It helped me create a sandbox for my product, Clara AI, so I could offer it to prospects after the product call.

What do you dislike about the product?

As of now everything is working perfectly smooth and cloud storage for Procol's product is a big plus

What problems is the product solving and how is that benefiting you?

My business requires heavy cloud requirements which is solved by Anyscale

What do you like best about the product?

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.

What do you dislike about the product?

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.

What problems is the product 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.

What do you like best about the product?

I like how Anyscale makes it easy to scale distributed AI workloads and simplifies deployment and resource management. It reduces the operational complexity of running machine learning applications. I also found the initial setup to be quite easy, smooth, and pretty good.

What do you dislike about the product?

I find some features and configurations complex for new users, so I think clearer documentation, simpler setup guides, and more intuitive workflows would make Anyscale easier to adapt.

What problems is the product solving and how is that benefiting you?

I use Anyscale for building and deploying machine learning workloads, simplifying scaling, managing distributed computing, and reducing deployment complexity, making it more efficient.

What do you like best about the product?

Anyscale makes it easy to build, deploy, and scale AI/ML workloads with Ray while keeping the developer experience straightforward. I especially like its ability to seamlessly scale distributed workloads, manage GPU resources efficiently, and move from experimentation to production without major infrastructure overhead.

What do you dislike about the product?

The main drawback is the learning curve around Ray and distributed computing concepts, especially for teams new to the ecosystem. Some advanced configurations can also feel complex, and cloud costs can become difficult to predict when running large-scale GPU workloads.

What problems is the product solving and how is that benefiting you?

Anyscale helps simplify the development and deployment of distributed AI/ML workloads by handling infrastructure, scaling, and resource management through Ray. It reduces infrastructure complexity, speeds up experimentation, and makes it easier to move AI workloads from development to production efficiently.