packet.ai - 2026 Pricing, Features, Reviews & Alternatives

Packet.ai is an on-demand GPU cloud infrastructure platform tailored for artificial intelligence and machine learning workloads. The service delivers high-performance NVIDIA graphics processing units with rapid deployment and full root SSH access. The platform is operated by hosted.ai and based in Europe. It addresses the computational requirements of data scientists, machine learning engineers, AI researchers and enterprise organizations for large language model training and AI inference tasks.

The platform offers multiple deployment methods including SSH access, a web dashboard, a command line interface and an application programming interface. GPU configurations range from a model featuring ninety-six gigabytes of GDDR memory with a PCIe Gen five interface optimized for professional visualization and inference to a configuration with one hundred eighty gigabytes of HBM memory connected via NVLink and based on Blackwell architecture for large scale training workloads. Another variant provides one hundred forty-one gigabytes of HBM memory with NVLink interconnect designed specifically for language model training.

A smart scheduling system allocates GPU resources only during active execution to avoid idle waste while preserving full hardware performance. On-demand provisioning eliminates interruptions and preemption. A service level agreement ensures high availability. Workloads execute within isolated containers that allocate dedicated GPU memory and maintain encrypted connections for data security. Technical support is available around the clock with an average response time under fifteen minutes and a four-minute average from signup to SSH access without requiring credit card information.

The platform includes a managed inference API to simplify inference workflows with OpenAI-compatible endpoints. Organizations requiring dedicated hardware can select bare metal options. The infrastructure supports cloud, hybrid and on-premise environments and integrates with existing monitoring and development tools. A web dashboard provides real-time visibility into GPU usage and performance metrics while the command line interface facilitates automation and instance management.