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 ![Runpod](https://images.g2crowd.com/uploads/product/image/large_detail/large_detail_ab691fa0f29c3814de0ecc1619e1455e/runpod.png "Runpod")
 
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# Runpod Discussions

Runpod is the AI Developer Cloud for developers building, training, fine-tuning, and deploying AI applications. The platform brings development environments, inference endpoints, and distributed training infrastructure into one account, so teams can choose the compute model that fits each stage of their workload. Runpod serves AI and machine learning developers, infrastructure engineers, researchers, and teams bringing AI applications into production. Its platform supports experimentation with models, custom application development, and the operation of deployed workloads. The core products address different stages of that work: - Pods provide configurable cloud compute environments for model development, training, fine-tuning, and other compute-intensive tasks. Developers can use templates or their own containers and connect through tools such as SSH, JupyterLab, and VS Code. - Serverless runs application code behind endpoints. Developers package their code and dependencies in worker containers, configure compute resources and scaling settings, and send requests through an API. Workers process requests and scale according to the endpoint configuration. - Clusters provide multi-node compute for distributed training and other workloads that require multiple connected GPUs. Teams can use Clusters when a job needs resources beyond a single machine. Runpod also provides storage options for workload data, templates for common development environments, and logs and monitoring tools for operating deployments. Developers can select resources and configure their applications around model requirements, request patterns, and deployment needs. The platform uses usage-based pricing, with costs determined by the resources and services selected. Pricing, capacity, and product-specific configuration details are available through the Runpod website, console, and documentation. Teams can evaluate Pods, Serverless, and Clusters according to their development workflow and production requirements.

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