Modal is a platform for deploying generative AI models, large-scale batch jobs, and job queues, designed to help data science and machine learning teams accelerate development, manage costs, and scale compute workloads efficiently. Modal provides a cloud-based solution that allows users to run workloads across thousands of CPUs and GPUs, with a unique pay-per-use billing model that charges only for actual compute time down to the CPU cycle.
The platform is particularly suited for teams needing to run demanding AI, data processing, or automation tasks without committing to fixed cloud infrastructure or dealing with idle resource costs. Instead, Modal enables on-demand, scalable computing, making it easier for organizations to experiment, iterate, and manage production workloads at scale.
How was Modal started?
Modal was founded by a team with extensive experience in software engineering and data science. The founding members include creators of popular open-source projects such as Seaborn and Luigi, academic researchers, international olympiad medalists, and engineering leaders with decades of expertise. This background has shaped Modal’s technical focus and its appeal to developer-centric organizations. More about the team and their story can be found on their About page.
Who uses Modal?
Modal primarily serves data science, AI, and engineering teams that require powerful and flexible compute resources. Some of their customers include:
- OpenPipe
- Codegen
- Cognition
- Futurehouse
- Contextual AI
- Martian
- Fable
- Flora
- Succinct
- Musicfy
- Sync
- CanOfSoup
- ChaiDiscovery
- Taylor
- LemonSlice
These organizations rely on Modal to streamline the deployment of advanced AI models and data-intensive workflows.
What makes Modal different?
Modal differentiates itself through its granular, pay-per-use billing model and its ability to scale workloads across vast compute resources without the complexity of traditional cloud infrastructure management. This approach allows teams to avoid wasted resources and idle costs, focusing spend only on what’s actually used.
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