Best AI Infrastructure & Training Tools

An enterprise actions runtime for secure, governed AI-agent access to business systems.

Key Features:

• •MCP runtime for secure, reliable production AI agent deployments
• •Connects identity providers, enforces agent authorization, and enables actions in Google, Slack, and Salesforce
• •Hobby plan includes 100 user challenges, 1,000 standard tool executions, 50 pro executions, and one hosted MCP server

Enterprise AI platform providing ultra-fast model inference, training, and deployment with support for custom models, computer vision, and agentic AI workflows.

Key Features:

• •Ultra-fast AI inference (410 tokens/sec)
• •OpenAI-compatible API
• •Custom model training and deployment

Liquid AI: Efficient foundation models designed for real-world deployment on any device, from wearables to enterprise systems with specialized AI capabilities.

Key Features:

• •Liquid Foundation Models library with 20 listed models
• •Text, vision-language, audio, and nano model categories
• •Models optimized for CPUs, GPUs, and NPUs

Open-source sandbox infrastructure for running AI-generated code safely. Sub-90ms startup, per-second billing, and stateful environments for AI agents and code interpreters.

Key Features:

• •Sub-90ms Sandbox Creation: Sandboxes boot in under 90 milliseconds, enabling AI agents to request execution environments on demand without noticeable latency. Each sandbox is a fully isolated Linux environment.
• •Stateful Environments: Unlike ephemeral sandbox providers, Daytona environments persist between sessions. Installed packages, written files, and configured state survive across multiple agent interactions.
• •MCP Server Integration: Native Model Context Protocol server support allows MCP-compatible AI agents and frameworks to provision, manage, and tear down sandboxes through standardized protocol calls.