Appfigures vs Deepnote
Side-by-side trajectory, velocity, and editorial themes.
Appfigures turns its estimate engine into market-ranking and competitor-intel products.
Appfigures has evolved from app analytics into market intelligence. Its download and revenue estimates now span iPhone and iPad and feed two larger products: a 15-report App Intelligence suite for sizing up any competitor, and new Leaderboards that rank apps and games across both stores by 14 metrics like revenue, downloads, and discovery.
The direction is clear — Appfigures is monetizing its estimate dataset by building higher-order products on top of it, with the richest features (historical Leaderboard data, per-app values) gated to Enterprise. Data completeness (iPad, by-state financials, faster Google Play) and a cleaner reporting UI round out the work.
Expect Leaderboards to deepen toward Enterprise upsell — more historical depth, per-app drill-downs, and category slices — following the same gate-the-good-stuff playbook used for App Intelligence.
Deepnote reshapes the data notebook into agent-operable infrastructure.
Deepnote, a collaborative data-science notebook, is steadily making itself agent-native: MCP tools now let AI agents create and wire integrations end-to-end, and OpenAI's Codex connects natively to a Deepnote workspace's notebooks, schedules, and data. Underneath, it keeps shipping solid workflow features — run snapshots, Git and GitLab sync, Polars, PDF export.
Two tracks are converging: reproducibility and engineering rigor (immutable run snapshots, Git sync, notebook interoperability) and agent-operability (MCP tools, Codex context). Deepnote is positioning the workspace as the trusted context layer that AI agents act through, not just a place humans write notebooks.
Expect more MCP tooling that lets agents operate Deepnote projects autonomously, plus deeper native hooks for external coding agents — the workspace-as-agent-context bet will likely expand beyond Codex.
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