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Modal is a serverless platform for AI and data teams to run CPU, GPU, and data-intensive compute at scale with sub-second cold starts and programmable infra.
Modal is a high-performance serverless platform specifically engineered for AI and data teams. It addresses the common friction points in machine learning infrastructure by allowing developers to define their environment, hardware requirements, and code entirely in Python. By eliminating the need for complex YAML configurations or manual Kubernetes management, Modal enables teams to move from local development to cloud-scale execution almost instantly.
The platform is built on a custom, AI-native runtime that delivers sub-second cold starts, making it significantly faster than traditional container solutions like Docker. This performance is critical for modern AI applications such as real-time LLM inference, audio transcription, and image generation. Modal provides a unified experience where infrastructure is treated as code, ensuring that hardware requirements stay in sync with the application logic.
One of Modal's standout features is its elastic GPU scaling. Users can tap into a massive pool of GPU resources across multiple cloud providers without managing reservations or dealing with capacity quotas. This "scale-to-zero" capability ensures that teams only pay for the compute they actually use, making it highly cost-effective for both bursty batch processing and high-demand inference tasks. Additionally, Modal includes a built-in storage layer for high-throughput data access and integrated observability for seamless logging and debugging.
Whether you are fine-tuning open-source models, running massive batch transcription jobs, or deploying low-latency APIs, Modal provides the foundational tools to build robust, scalable data applications. It is trusted by leading AI companies like Scale, Suno, and Mistral to power their most demanding workloads.
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Pricing Model
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Define hardware and environments directly in Python code, eliminating the need for YAML or external config files.
Access thousands of GPUs across clouds with instant autoscaling and the ability to scale back to zero when idle.
An AI-native runtime engineered for heavy workloads, offering 100x faster performance than standard Docker containers.
Integrated logging and full visibility into every function and container for simplified debugging and monitoring.
A globally distributed storage system designed for high throughput and low-latency model loading.
