Count

Collaborative AI-powered data canvas that blends SQL, Python, and natural-language agents for team analytics.

Freemium· Free; Pro $49/editor/mo; Scale $69/editor/mo (15-seat min); Enterprise customAgentsMulti-model (Anthropic, OpenAI, Google)

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Best for

Pick Count if your data team wants AI agents embedded in a transparent SQL/Python canvas that business stakeholders can actually open and read.

Skip if

Skip it if you need an on-prem or open-source BI stack, or if a handful of analysts already run fine in dbt plus a notebook.

Count is a collaborative analytics platform built around a shared canvas where SQL queries, Python notebooks, charts, and AI agents live side by side. The agents (backed by frontier models from Anthropic, OpenAI, and Google) let analysts and stakeholders ask questions in natural language, @-mention specific datasets or metrics, and get back auditable work that anyone on the team can inspect, fork, or tweak. It connects to warehouses, semantic layers, and BI tools via API and MCP, and surfaces results through interactive reports, metric trees, and process-flow maps.

The pitch is a self-serve BI alternative for data teams who want AI exploration without the black-box feeling of a pure chat interface. Pricing starts with a Free tier (3 editor seats, CSV only), Pro at $49 per editor/month with 75 collaborators included, Scale at $69 per editor/month with a 15-seat minimum, and an Enterprise tier with HIPAA, SSO, and row-level security. Viewer seats are unlimited across plans, which makes it cheaper to roll out across a non-technical org than seat-priced incumbents like Looker or Hex.

Integrations include Slack, an MCP client, and the standard warehouse connectors; agent context controls let you constrain what the AI is allowed to query and how it reasons. The product is proprietary and cloud-hosted, so teams that need on-prem or fully open-source tooling will need to look elsewhere.

Editor's take

Count is one of the more thoughtful answers to 'what does BI look like after LLMs,' favoring an inspectable canvas over a magic chat box. The unlimited-viewer pricing is the right instinct, though per-editor costs add up fast at scale. Best suited to teams that already think in metrics and want agents as collaborators, not oracles.

— The AI Tool Bible editorial team