Google Colab vs Hexagon-ML

Google Colab lets users write and run Python in a browser without configuring a local environment, with access to GPUs and notebook sharing. Its hosted Jupyter notebooks combine executable code with rich text, images, HTML, and LaTeX. Data can come from Google Drive, spreadsheets, GitHub, and other sources, with Google Cloud Storage, Sheets, and BigQuery resources listed for integration. Colab AI can generate or transform code, explain Python libraries, suggest fixes, and perform multi-step data-analysis workflows. By default, it does not access Drive files or user secrets, though it can generate code to access them when explicitly requested. Google says generative-AI features collect prompts, related code, outputs, usage information, and feedback; human reviewers may process that data for up to 18 months. Free notebooks can run for at most 12 hours, depending on availability and usage. Pro+ supports continuous execution up to 24 hours when enough compute units are available. Colab also has a Visual Studio Code extension and a beta, allowlist-gated API. Paid plans start at 8.33 USD/user/mo (annual).

Hexagon-ML has no write-up on Specifiction yet; its figures are above.