Serverless platforms promise to run your code only when it is needed; Nuclio adds the part they usually skip, raw throughput for data-heavy work, with a single function instance able to process hundreds of thousands of HTTP requests or records per second. You write functions in Python, Go, Node.js, Java, .NET, or plain Shell, and deploy them from the dashboard on port 8070, from the nuctl command line, or from Jupyter through the Nuclio SDK. Events reach your code through HTTP calls, cron schedules, Kafka and Kinesis streams, MQTT and NATS messages, RabbitMQ queues, or Azure Event Hubs, so the same function model covers APIs, stream processors, and scheduled jobs. Each function ships as its own container, built securely with Kaniko, and scales on plain Docker for a single server or on Kubernetes when the workload outgrows one machine, with GPU support for inference and media pipelines. Projects organise functions with shared configuration, secrets, and API gateways, and the dashboard shows invocations, logs, and metrics while templates for common jobs get a first function running in minutes. Data science teams plug Nuclio into MLRun and Kubeflow Pipelines to serve models and automate ML workflows without a separate serving tier. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. Apache-2.0 licensed.