Modal

What do you like best about the product?

What I like most about this is that it completely removes the headache of infrastructure management for my project. All of that is handled by it, so I don’t need to worry about scaling GPUs; it can handle whatever number of GPUs I need. It offers essentially infinite scaling with zero waste, and I don’t have to set up anything GPU-related myself. Another interesting part is that they built their own file system and container runtime, so I don’t need to worry about those either they manage it on their own. Because of that, I can just focus on my code and building, instead of worrying about deployment and infrastructure. Modal works well with the other tools I use as a developer, especially Python and GitHub. I can keep most of my application code in my existing workflow and. The integration is straightforward, so I do not have to completely change my development setup. This makes it convenient for testing AI workloads, running jobs, and scaling them when needed.

What do you dislike about the product?

What I dislike is that if I want to change the code, there are some commands I should use, which is great. However, later on, if I want to move the deployment to another server, it requires a lot of changes in my project, and that’s a problem for me. Basically, you become dependent on this model, so if they raise the price later, it becomes difficult to change the deployment server. Also, since it doesn’t always run when no one is using it and it goes into sleep mode, when a user suddenly tries to access it, it takes some time to load, which can make customers unhappy.

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

The two main problems this solves, and the features that impressed me most, are its massive scale and the way it handles infrastructure for you. With its scale, I don’t have to worry about how many GPUs I need to run my program, what kind of network setup is required, or other infrastructure-related details it takes care of all of that. It can even scale infinitely, so I can focus on building and not worry about deployment.It also provides AI-generated code, but what I really value is the isolated environment where I can test the AI-provided code before integrating it into my project. Basically, I don’t need to buy expensive GPUs or set up a secure testing environment myself, because it handles that for me.

What do you like best about the product?

I like that Modal makes it easy to run and deploy Python workloads without having to manage the underlying infrastructure. It is especially useful for running AI and compute-heavy tasks when I need extra resources.

What do you dislike about the product?

It can take some time to understand the platform and its deployment model. Debugging can also be a little difficult when something goes wrong in a remote environment.

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

It simplifies running compute-heavy and AI workloads without needing to manage servers manually. This helps us run jobs on demand, scale resources when needed, and spend less time managing infrastructure.

What do you like best about the product?

Modal provider is best-in-class for developer experience when working with Python. I used it to host my own STT and TTS models, and the overall workflow felt smooth and straightforward. On top of that, their free tier is very generous—like, very, very generous. look guys if you are looking for provider to host your agent or model like wisper and other then its really good option to use model i will higly suggest as its native support for paython deployment can give trouble on paid plan (might be withme bit still)

What do you dislike about the product?

Deployment can be a bit unpredictable sometimes. For example, it failed on me once when I tried to deploy it.

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

As I said previously, it failed once during deployment.

What do you like best about the product?

What I like best about this platform is how easily they let us scale AI workloads at random without even managing any servers or Kubernetes. Its own Python-first approach also makes deploying inference, training, and other batch processing jobs feel very much similar to writing local code. And also GPU selection, container dependencies and other auto-scaling can also be defined within the application. Faster container startup and access to different GPU types are particularly useful when workloads fluctuate. They pay for usage model also helps us to avoid spending on ideal infrastructure.

What do you dislike about the product?

This platform has a bit of a learning curve because its code-first deployment model is kind of different from the conventional cloud infrastructure setup. Also, debugging distributed jobs and optimising its cold start behaviour might require familiarity with model-specific concepts. And their cost may also be bit difficult to predict when highly parallel GPU workloads scale suddenly; this will create bit of confusion in the middle period, as some of their advanced capabilities and other enterprise controls are limited to higher price plans. And more visual configurations and other core cost forecasting tools would make the platform easier for non-technical teams.

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

This platform helps us to remove the infrastructure which is involved in provisioning GPUs and building other containers, along with scaling AI applications. We can also use it for model inference, fine-tuning, and data processing for secure code execution without even maintaining any dedicated computing clusters. And also applications which are automatically scaled according to demand and can scale down when unused. By reducing the initial GPU expenses, this platform is saving lots of cost involved at the initial setup. Overall, this platform allowed our team to test ideas and move models into production much faster. It also reduces the operational overhead while giving our developers more time to focus on the actual AI product.

What do you like best about the product?

it is fast and you dont have to spend too much time in configuring docker containers . it saves your too much time because it gives high performance job instantly

What do you dislike about the product?

there is not a major issue in this but one think that i dont like is that it is mostly focused on python

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

modal is solving the time we are spending on it by allowing us too running heavy python scripts and ai workloads

What do you like best about the product?

What I like most is how straightforward the platform feels. The interface is clean and intuitive, and getting started is easy. It offers a convenient way to experiment with AI applications without needing a complicated setup. Overall, it feels responsive and well organized, which makes it a solid place to learn and test ideas.

What do you dislike about the product?

What I dislike is that some parts of the platform come with a bit of a learning curve, especially if you’re new to AI infrastructure. I also feel that a few of the setup steps and the documentation could be more beginner-friendly and easier to follow. That said, overall it’s been a good experience.

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

Modal Labs helps cut down the time and effort it takes to test and deploy AI applications. Rather than spending my time setting up infrastructure, I can put more energy into building and experimenting. Overall, it’s made my workflow more efficient and much easier to manage, especially when I’m evaluating things and learning along the way.

What do you like best about the product?

Modal makes it easy to run Python workloads without managing servers or infrastructure. I used it for web scraping tasks, and the automatic scaling, fast deployment, and simple developer experience allowed me to focus on writing scraping logic instead of maintaining cloud resources.

What do you dislike about the product?

The platform is easy to use, but understanding the pricing model for different compute resources can take some time. More examples for advanced scraping workflows and browser automation would make it even easier for new users.

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

I used Modal to run large-scale web scraping jobs that needed parallel execution and dependable background processing. Rather than managing virtual machines or containers myself, I was able to deploy the workload quickly and rely on the platform to scale automatically. That cut down on operational overhead and made my data collection process more reliable and efficient overall.

What do you like best about the product?

I love using Modal Labs because it simplifies deploying GPU-powered AI workloads. The developer experience is excellent, allowing me to turn Python functions into scalable cloud services with very little code. Automatic scaling works seamlessly, and the fast startup times make it easier to build, test, and deploy AI applications without worrying about infrastructure. I really appreciate how well Modal Labs handles GPU provisioning and batch workloads behind the scenes. The integrations with popular Python AI frameworks are smooth, logs and monitoring are easy to follow, and the ability to scale from a small prototype to production without changing much code makes the development workflow much more efficient. Also, the initial setup was very easy.

What do you dislike about the product?

One area that could be improved is cost visibility and monitoring for GPU-intensive workloads, especially as projects scale. I'd also like to see more built-in debugging and profiling tools for long-running jobs, along with additional deployment templates and examples for common AI use cases to help new users get started even faster. One improvement would be a real-time cost dashboard that breaks down GPU usage and estimated spending by deployment, batch job, or individual function, making it easier to spot expensive workloads before costs grow unexpectedly. For debugging, I'd like more detailed execution traces, GPU utilization metrics, memory usage graphs, and easier access to logs from failed or long-running jobs, along with built-in recommendations for optimizing performance and reducing resource consumption.

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

I use Modal Labs to deploy and scale AI applications without managing cloud infrastructure. It eliminates the complexity of provisioning and managing GPU infrastructure, allowing me to deploy models, run batch jobs, and serve inference with minimal setup. This speeds up development and focuses on building AI applications.

What do you like best about the product?

I like how Modal Labs completely eliminates the infrastructure tax since we don't have to manage Kubernetes, configure CUDA drivers, or manually build Docker containers just to run a Python script on a GPU. Their custom container runtime boots up in seconds, allowing us to run local code on cloud GPUs almost instantly. I appreciate defining the GPU environment directly in Python with simple decorators, avoiding the headache of Docker, Kubernetes, and CUDA. I also enjoy Modal's shared storage volumes and network file systems for machine learning, which let us persist heavy model weights and training datasets across separate runs. The parallel mapping feature is particularly beneficial, enabling us to effortlessly scale a single Python function across hundreds of parallel CPU or GPU containers to process large datasets in minutes rather than hours. Additionally, the initial setup was straightforward with just pip commands.

What do you dislike about the product?

I find the lack of self-hosting or private VPC options to be a dealbreaker for enterprises with strict compliance requirements, and the proprietary Python SDK can lead to vendor lock-in. Additionally, running steady, 24/7 GPU workloads on their serverless model is much more expensive than renting dedicated instances from traditional cloud providers.

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

I use Modal Labs to eliminate infrastructure tax and idle GPU costs, run tasks using simple Python decorators, and scale functions efficiently. It simplifies our workflow by managing serverless GPU and CPU compute tasks without the need to handle complex cloud infrastructure.

What do you like best about the product?

Solid offering, a lot of unexpected payments that lead us to switch to another provider

What do you dislike about the product?

Got unexpected payments, even after closing the account

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

Modal has a GPU as a service that is generally more cost effective than aws/gcp