Hex
Hex is rebuilding analytics around an agent — now an MCP client that pulls context from anywhere.
Comparison · Analytics
Maze vs Count
A side-by-side editorial comparison of Maze and Count — release velocity, themes, recent moves, and the top alternatives to consider.
Maze vs Count: at a glance
| Feature | Maze | Count |
|---|---|---|
| Sector | Analytics | Analytics |
| Velocity score | 3.8 | 6.3 |
| Sparks · 30d | 0 | 1 |
| Top themes | ux research, ai moderator, thematic analysis, panel quality | agentic-analytics, mcp, public-api, warehouse-connectors |
| Last editorial update | 1mo ago | 11d ago |
| Website | — | Visit → |
What is Maze?
UX research platform is reshaping itself around AI moderation and AI-driven analysis.
Maze is shipping aggressively across two adjacent fronts: AI-driven research execution (AI Moderator with adaptive conversation styles, visual stimulus support) and AI-driven analysis (thematic analysis now generated automatically across every study type). Around the AI core, recent releases also tighten panel recruitment with Fresh Eyes participant-freshness controls, expand Global Search to blocks and interview sessions, and improve Variant Comparison reliability for A/B prototype tests.
What is Count?
Count is turning its BI canvas into a governed, agent-operated analytics platform.
Count is a data-canvas analytics tool reorganizing itself around an AI agent. In two months it shipped a full public REST API and hosted MCP server (governed agent access via OAuth and service accounts), a major agent upgrade that lets the agent read and edit the entire canvas and answer from Slack, and the ability to plug external MCP servers (Linear, HubSpot, Stripe, Slack, Drive) into the agent. Around the agent it keeps broadening warehouse support—ClickHouse, Snowflake semantic models, OSI—alongside chart and UX polish.
Maze vs Count: editorial side-by-side
Maze
3.8ANALYTICS
UX research platform is reshaping itself around AI moderation and AI-driven analysis.
◆ Current state
Maze is shipping aggressively across two adjacent fronts: AI-driven research execution (AI Moderator with adaptive conversation styles, visual stimulus support) and AI-driven analysis (thematic analysis now generated automatically across every study type). Around the AI core, recent releases also tighten panel recruitment with Fresh Eyes participant-freshness controls, expand Global Search to blocks and interview sessions, and improve Variant Comparison reliability for A/B prototype tests.
◆ Where it's heading
The product is moving from 'research tool researchers operate' to 'research platform that runs and interprets studies on the researcher's behalf'. AI Moderator handles unmoderated conversation; AI thematic analysis turns transcripts into highlights without a researcher manually coding. The core wager is that the analysis bottleneck — not study design — is what limits the volume of research a team can do, and Maze is going after that bottleneck directly.
◆ Prediction
Expect AI Moderator to keep absorbing more interview style options and stimulus types, and the analysis side to push from theme-extraction toward auto-generated synthesis or report drafts. Panel-quality controls like Fresh Eyes are likely to expand into broader participant-cohort management.
Count
6.3ANALYTICS
Count is turning its BI canvas into a governed, agent-operated analytics platform.
◆ Current state
Count is a data-canvas analytics tool reorganizing itself around an AI agent. In two months it shipped a full public REST API and hosted MCP server (governed agent access via OAuth and service accounts), a major agent upgrade that lets the agent read and edit the entire canvas and answer from Slack, and the ability to plug external MCP servers (Linear, HubSpot, Stripe, Slack, Drive) into the agent. Around the agent it keeps broadening warehouse support—ClickHouse, Snowflake semantic models, OSI—alongside chart and UX polish.
◆ Where it's heading
Count is building toward analytics where agents are first-class operators: a governed API/MCP layer for access, an agent that drives the canvas end to end, external tool reach via MCP, and connection-level context so guidance is captured once and inherited. Governance—permissions, scopes, service accounts—is the enabling layer that makes agent access acceptable in real data stacks rather than a bolt-on.
◆ Prediction
Expect more connection- and warehouse-level context controls, a widening catalog of supported external MCP integrations, and deeper Slack-native agent workflows.
Alternatives to Maze and Count
Other Analytics products tracked by Sparkpulse, ranked by recent ship velocity. Each card links to a full editorial trajectory and lets you pivot into a head-to-head comparison with either Maze or Count.
Fulcrum
Fulcrum is in steady maintenance mode, polishing its field-mapping and mobile data-capture core.
Lightdash
Lightdash keeps sanding down the edges of self-serve BI, chart by chart.
Apify
Apify is rebuilding the Actor platform as MCP-first agent infrastructure.
Apache Superset
Duplicate Apache Superset row — same Helm-chart packaging feed, no distinct product signal
Apache Superset
Superset's public feed is all Helm-chart packaging — the 6.x product work sits behind release votes
Recent activity from Maze and Count
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
- 15d agoCountConnect external MCP servers to the Count agent ⚡
- 29d agoCountDashed lines
- 1mo agoCountNew workspace home
- 1mo agoCountClickHouse support
- 2mo agoMazeMultiple VC blocks, conditional logic, and balanced distribution
- 2mo agoMazeNew: AI-powered thematic analysis, now for every study type ⚡
- 2mo agoCountMajor Count agent upgrade: edits any cell, runs in Slack ⚡
- 2mo agoCountPublic API and MCP server ⚡
- 3mo agoMazeRelease Roundup – March 27th, 2026
- 3mo agoMazeFresh Eyes: Automatically exclude repeat participants
- 3mo agoMazeRelease Roundup – March 6th, 2026
- 3mo agoMazeSearch for blocks and interview sessions in global search
Frequently asked questions
What is the difference between Maze and Count?
They serve adjacent needs but don't currently overlap on shipped themes. Count is currently shipping more aggressively (velocity 6.3 vs 3.8), with 1 editorial sparks in the last 30 days against 0. See the at-a-glance table above for a side-by-side breakdown of velocity, recent sparks, and editorial themes.
Is Maze better than Count?
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. Count is currently shipping more aggressively (velocity 6.3 vs 3.8), with 1 editorial sparks in the last 30 days against 0. For your specific use case, the alternatives sections above list other Analytics products to evaluate alongside.
What are the best alternatives to Maze?
Top Maze alternatives in Analytics are ranked by recent ship velocity. Browse the "Maze alternatives" section above for the current picks, or visit /alternatives/maze for the full list with editorial commentary on each.
What are the best alternatives to Count?
Top Count alternatives in Analytics are ranked by recent ship velocity. Browse the "Count alternatives" section above for the current picks, or visit /alternatives/count for the full list with editorial commentary on each.