dbt Core
dbt 2.0 ships stable with an official OSS/proprietary split and agentic skill loading
A side-by-side editorial comparison of Count and Omni — release velocity, themes, recent moves, and the top alternatives to consider.
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.
Omni's Apps reach general availability, completing its embedded analytics platform pitch.
Omni is operating on three fronts in parallel: a governed BI workspace for analysts, an AI agent layer including Omni Agent and AI Routines connected via MCP, and an embedded analytics platform for product teams. The AI semantic model generation hitting GA in July and embedded Apps hitting GA in August represent the two most significant capability milestones in recent months — both cross the line from preview to production-ready commitment.
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.
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.
Expect more connection- and warehouse-level context controls, a widening catalog of supported external MCP integrations, and deeper Slack-native agent workflows.
Omni is operating on three fronts in parallel: a governed BI workspace for analysts, an AI agent layer including Omni Agent and AI Routines connected via MCP, and an embedded analytics platform for product teams. The AI semantic model generation hitting GA in July and embedded Apps hitting GA in August represent the two most significant capability milestones in recent months — both cross the line from preview to production-ready commitment.
The product is converging on a stack that spans the full data workflow: governed modeling, AI-assisted analysis, agentic automation (Routines, MCP tools), and embedded delivery (Apps). Each surface is advancing simultaneously, which points to a deliberate platform strategy rather than feature prioritization. Omni is positioning the integrated stack — governed data with AI on top, deployable anywhere — as its competitive wedge against both traditional BI tools and AI-native data startups.
AI Routines and MCP tools will likely expand toward write operations or workflow triggers, moving from query-and-report toward agentic data operations. The embedded Apps surface will add white-labeling and more deep customization options as enterprise product teams push on it post-GA.
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 Count or Omni.
dbt 2.0 ships stable with an official OSS/proprietary split and agentic skill loading
Fulcrum ships MCP server and AI Toolkit to let AI assistants build and query field data forms
Holistics builds AI governance and docs-as-analytics in parallel, shipping both weekly
OpenHouse breaks ground on Iceberg views while tightening storage lifecycle and authorization
Lightdash is cutting its dbt dependency and building AI-powered authoring into every layer of its BI stack.
Keboola's Kai AI assistant hits GA, completing the pivot from data platform to AI-native pipeline orchestration layer.
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
Both compete on the same themes — mcp — within Analytics. Count and Omni are shipping at a similar cadence (velocity 6.3 vs 6.3, both within Sparkpulse's "active" band). See the at-a-glance table above for a side-by-side breakdown of velocity, recent sparks, and editorial themes.
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. Count and Omni are shipping at a similar cadence (velocity 6.3 vs 6.3, both within Sparkpulse's "active" band). For your specific use case, the alternatives sections above list other Analytics products to evaluate alongside.
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.
Top Omni alternatives in Analytics are ranked by recent ship velocity. Browse the "Omni alternatives" section above for the current picks, or visit /alternatives/omni for the full list with editorial commentary on each.