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Count

Explore data and solve problems together. Build metric trees, create dashboards, and share insights with your team—all in one collaborative analytics platform.

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Type
Analytics Notebook
Category
Business Intelligence
Pricing
Free tier · paid from $49/mo
Deployment
Cloud (managed)
Last updatedSeptember 21, 2026

Editor's Take

We recommend Count for small, collaborative business-intelligence teams that want metric trees, shared dashboards, and problem-solving in one freemium workspace. It is a strong fit for teams validating a common analytics layer before paying for a heavier platform such as Tableau, but the available context does not establish enterprise-scale adoption, governance depth, or pricing beyond its freemium tier.

— Egor Burlakov, Editor

Evaluate Count

Count Pricing
Plans, costs & free tier
Top Alternatives
Substitutes, and when to pick them
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Comparisons

vs Looker
Head-to-head comparison
vs Tableau
Head-to-head comparison

Count: product and architecture

Our Count review verdict: Count is best positioned for teams that want a collaborative, AI-assisted analytics workspace rather than another standalone dashboarding product. Its strongest proposition is bringing questions, SQL analysis, exploration, reporting, product-data work, and shared canvases into one environment, with a $0 Free plan and a stated 14-day trial path. We recommend Count for data teams that want business users and analysts to work through decisions together; teams buying primarily for documented performance benchmarks, a detailed connector catalog, or established enterprise deployment evidence should look elsewhere until Count provides that evidence.

Overview

Count presents itself as a collaborative analytics platform for exploring data, building metric trees, creating dashboards, and sharing insights with a team. The product messaging is unusually direct: “Ask anything. See the answer,” with the claim that many questions and decisions never reach data. That framing makes Count less about passive reporting and more about creating a shared place to investigate a business question, create analysis, and turn it into an artifact others can review.

The current product description names AI Analysis, SQL Analysis, Exploration, Product Geo analysis, Reporting, Product data, and Engagement analysis. It also shows an agent workflow that compares user activities with HubSpot account-health information, executes analyses named top_complaints, product_health, and active_users, then creates a comprehensive analysis on an editable canvas. This is a compelling direction for analytics teams that want analysis and communication to happen in the same tool rather than across a SQL editor, BI dashboard, presentation, and chat thread.

Count is a collaborative analytics platform: metric trees, dashboards, a SQL editor and agent-assisted analysis in one workspace, with viewer seats included on every tier. Data leaders evaluating it as a central analytics standard should confirm supported data platforms, refresh behaviour and governance controls directly with Count, since its public material describes the workflow more fully than the mechanics.

Count’s stated privacy and compliance posture is meaningful: it names SOC 2, GDPR, and says customer data is not used for model training. Those claims matter for teams considering AI-assisted analysis, but they do not replace a procurement review of security documentation, contracts, access controls, retention, and data-processing terms. The core trade-off is clear: Count promises a more unified analytic workflow, while the supplied information leaves several operational details unverified.

Key Features and Architecture

Count’s visible architecture centers on an analysis workspace where people can ask questions, run analysis, and edit the resulting work on a canvas. The product description explicitly shows an agent moving through a sequence: inspect user activities, compare them with HubSpot account-health information, execute named analyses, and create a comprehensive analysis. That is more structured than a simple natural-language answer box because the workflow includes named analytical steps and an editable output surface.

Key capabilities described by Count include:

  • AI Analysis: Count positions AI as a way to ask questions and produce an analysis, including an agent workflow that creates a comprehensive result. The technical boundary stated by Count is that customer data is not used for model training, which is a critical control for teams assessing AI features against internal data-governance requirements.
  • SQL Analysis: SQL Analysis is named as a distinct product capability. This matters because it gives analytics engineers a path to express and inspect analytical logic directly, rather than requiring every investigation to remain solely in a conversational interface.
  • Exploration: Count identifies Exploration as a product function alongside SQL and reporting. In practical workflow terms, that places iterative investigation before a finished dashboard or report, which suits teams handling ambiguous operational or product questions.
  • Metric trees: The platform explicitly supports building metric trees. For data leaders, that makes Count relevant when a team needs to structure relationships among metrics instead of presenting isolated headline numbers.
  • Dashboards and reporting: Count includes dashboard creation and Reporting in its positioning. These features turn an investigation into a shareable view, but the supplied information does not define dashboard limits, refresh behavior, visualization types, or semantic-model controls.
  • Product and engagement analysis: Product data and Engagement analysis are named explicitly. The example focuses on identifying which user activities relate to expansion versus churn, making Count particularly relevant to product-led teams that need an analysis workflow tied to account context.
  • Geo analysis: Product Geo analysis is also listed. Count therefore signals support for geographically oriented product analysis, although the available evidence does not specify mapping functions, supported geographic data types, or spatial-processing capabilities.
  • Collaborative canvas editing: The example includes Edit canvas, which indicates that the generated analysis can be edited in the workspace. This is valuable when an analyst needs to turn an AI-assisted draft into a reviewed, contextualized deliverable.

The strongest architectural idea is convergence: one platform combines a question, an agent-assisted analysis, SQL work, exploratory work, and a shared final artifact. The cost is that the supplied material does not document how those layers are governed or connected. We have no supported evidence here for a semantic layer, version control, transformation framework integration, warehouse compatibility, data-refresh configuration, API access, or a catalog of connectors beyond the HubSpot reference in the product example.

Count also claims it is ready for a whole organization, but the official feature material supplied does not enumerate the enterprise capabilities behind that statement. SOC 2 and GDPR are concrete signals, and the explicit model-training restriction is useful, yet those are not a substitute for detailed information about roles, permissions, auditability, lineage, or deployment architecture. For a technical buyer, Count’s feature story is strongest at the user-workflow level and weakest at the platform-operations level.

Ideal Use Cases

Count is a strong candidate for a product analytics team of roughly 3 to 15 people that needs to connect behavioral activity with account context and communicate findings to customer-success or leadership stakeholders. The supplied agent example specifically compares user activities with HubSpot account-health information and analyzes activity associated with expansion versus churn. That makes Count a sensible choice when the business question is not merely “what happened?” but “which activities should we investigate as possible indicators of account outcomes?”

A second good fit is an analytics engineering and business-operations group that wants to pair SQL-based investigation with a collaborative presentation layer. Count names both SQL Analysis and an editable canvas, so the workflow can begin with an analyst framing or checking logic and end with a shared analysis. This is particularly useful for teams whose decision process currently fragments across a SQL environment, dashboard tool, slide deck, and recurring stakeholder meeting. The trade-off is that Count’s supplied documentation does not establish how analytical definitions are versioned or governed across that workflow.

A third fit is a data-led organization that needs a structured way to discuss a connected set of metrics rather than distribute individual charts. Metric trees, dashboards, reporting, and exploration are all explicitly part of Count’s proposition. A data leader could use that combination for cross-functional operating reviews where product, growth, and customer teams need a common analytical narrative. We recommend Count for teams that value collaborative analysis as a first-class deliverable and are willing to validate its enterprise controls through a focused pilot.

Count can also suit an organization testing AI-assisted analysis while requiring a stated boundary that data is not used for model training. The SOC 2 and GDPR claims provide useful starting points for that evaluation. Still, treat these claims as procurement inputs, not a completed risk assessment: teams with regulated data or strict internal controls should obtain the underlying documentation and contractual commitments.

Don’t use this if your primary requirement is a fully evidenced, deeply documented analytics platform with published benchmarks, a detailed integration inventory, or confirmed enterprise-scale usage data. The supplied Count information does not provide those facts. Avoid making Count the sole system for governed executive reporting until the vendor can demonstrate how its SQL, AI analysis, dashboards, and collaboration features align with your organization’s access, review, and data-quality processes.

Strengths & Trade-offs

Count’s advantages are concrete, but they are concentrated in workflow design and product positioning rather than in externally documented operating detail.

Pros

  • It combines analysis and communication in one workspace. Count brings together SQL Analysis, Exploration, dashboards, Reporting, metric trees, and an editable canvas. That can reduce the handoff friction that occurs when an analyst discovers something in one tool and must rebuild it elsewhere for stakeholders.
  • Its agent example is tied to a real business-analysis pattern. The example compares user activities to HubSpot account-health information and investigates behavior related to expansion versus churn. This is more useful than generic AI positioning because it describes an identifiable product and customer-health workflow.
  • It provides an explicit data-use boundary for AI features. Count states that customer data is not used for model training. For teams considering AI Analysis, that is a material point to test during security and legal review.
  • It has a low-friction evaluation path. The official product messaging offers a 14-day trial with no credit card required, while the official pricing page includes a $0 Free plan. That supports a practical proof of value before a paid commitment.
  • Its feature set includes both product and geographic analysis. Product data, Engagement analysis, and Product Geo analysis give Count a defined focus for teams whose questions span behavior, account outcomes, and geographic context.

Cons

  • The supplied materials do not document core platform mechanics. Count names SQL Analysis and dashboards, but provides no supported detail on supported data platforms, data ingestion, refresh behavior, APIs, semantic-layer design, or transformation-tool integration. That is a real limitation for analytics engineers responsible for reliability and maintainability.
  • Enterprise governance is asserted more clearly than it is specified. SOC 2, GDPR, and the whole-organization claim are useful signals, but the supplied evidence does not identify roles, permissions, audit trails, lineage, or review controls. Teams with strict governance needs will need vendor validation.
  • Per-tier allowances are not published. Count publishes its plan prices but does not enumerate what usage each tier includes, so a team planning beyond a pilot has to ask.
  • Platform mechanics are lightly documented. Count names SQL analysis, dashboards and reporting, but publishes little on supported data platforms, ingestion, refresh behaviour, APIs or semantic-layer design, so a technical evaluation needs a conversation.
  • The Free tier includes up to three editor seats. That may be enough for a single evaluator, but it does not support a meaningful cross-functional collaboration test without moving beyond the free plan.

Count pricing

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Free tier · paid from $49/mo
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Alternatives to Count

The reviewed substitutes for Count among the analytics notebooks, and what would make each one the better answer.

Other approaches

A different approach to the same problem. Each substitutes only for the workload named beside it.

Looker
A notebook analytics workspace and a BI platform both answer questions from warehouse data, from different ends: exploratory code-and-narrative work against governed dashboards for a wide audience. Vendors publish head-to-heads and teams often run both, so the decision is which is the primary surface.Applies to: Whether analysis is delivered as governed dashboards or as exploratory notebooks.
Tableau
Both answer the same need from different architectures, so the decision is how the stack is shaped rather than which product is better, and organisations commonly run both. Recorded against external comparison content rather than against this site's own verdict, which is what the earlier derived approval rested on.Applies to: Deciding how the stack is shaped, where both products can be part of the answer.

Head to head

See detailed alternatives analysis

If you are exploring Count alternatives, you are likely looking for a collaborative analytics platform that combines AI-driven analysis with traditional BI capabilities. Count positions itself as an "AI and BI analysis canvas" that connects directly to data warehouses and lets teams explore data through a shared, real-time workspace. While Count offers a distinctive canvas-based approach, several established platforms serve overlapping needs with different architectural philosophies, pricing structures, and feature sets.

We have evaluated the leading alternatives across key dimensions including collaboration capabilities, AI integration, data warehouse connectivity, semantic layer support, and overall value for data teams.

Top Alternatives Overview

Metabase is an open-source BI tool that emphasizes simplicity and speed. It lets anyone on your team ask questions about data and see answers in intuitive visual formats without writing SQL. Metabase is particularly strong for teams that want fast self-service analytics without heavy setup.

Sigma Computing takes a spreadsheet-first approach, giving business users a familiar interface backed by the full power of a cloud data warehouse. It supports live queries, writeback, and real-time collaboration, making it a natural fit for finance and operations teams.

Looker, now part of Google Cloud, is built around LookML, a semantic modeling language that centralizes business logic in a governed layer. Looker is well suited for organizations that need strict data governance and reusable metric definitions across multiple dashboards and applications.

Power BI from Microsoft provides deep integration with the Microsoft 365 ecosystem and Azure. It offers a low entry price and is a practical choice for organizations already invested in Microsoft infrastructure.

Lightdash is an open-source BI platform designed specifically for dbt users. It connects directly to your dbt project, letting you define metrics once and expose them across dashboards without duplicating logic.

Amazon QuickSight (now evolving into Amazon Quick) delivers AI-powered BI within the AWS ecosystem. It features a unique pay-per-session pricing model and built-in machine learning capabilities for anomaly detection and forecasting.

Holistics is a self-service BI platform that combines data modeling, transformation, and visualization. It enables data teams to build a semantic layer while empowering business users with governed self-service analytics.

Cube provides an open-source semantic layer that sits between your data warehouse and any frontend tool. AI agents can build and query the semantic layer automatically, reducing hallucination in AI-generated analytics.

Amplitude focuses on digital product analytics, helping teams understand user behavior, run experiments, and optimize product experiences. It is less of a general-purpose BI tool and more targeted at product-led growth teams.

Alteryx is an enterprise analytics automation platform that specializes in data preparation, blending, and advanced analytics workflows. It targets analysts who need to automate complex data pipelines without writing code.

Architecture and Approach Comparison

Count differentiates itself with a collaborative canvas model where SQL, Python, and visual exploration coexist in a single workspace. AI agents can build analyses, write queries, and edit the canvas directly from natural language prompts. Every step remains auditable, and multiple users can work on the same canvas simultaneously.

Metabase and Sigma Computing prioritize accessibility but from different angles. Metabase offers a question-and-answer paradigm where users can query data without SQL knowledge, while Sigma presents a spreadsheet interface that business users already understand. Neither emphasizes the freeform canvas approach that Count uses.

Looker and Cube take a semantic-layer-first approach, requiring teams to define business logic centrally before analysts consume it. This creates stronger governance but adds upfront modeling effort. Count also offers its own semantic layer (Count Metrics) but pairs it with a more exploratory, less structured workflow.

Lightdash and Holistics sit in a middle ground, tightly integrating with dbt to leverage existing data modeling investments. If your team already maintains a dbt project, these tools reduce duplication by reading metric definitions directly from your models.

Power BI and Amazon QuickSight are ecosystem plays. Power BI is strongest when paired with Microsoft tools, while QuickSight shines for AWS-native organizations. Both offer embedded analytics and enterprise-grade security but rely more on traditional dashboard paradigms than on the exploratory canvas model Count promotes.

Alteryx operates in a fundamentally different space, focusing on data preparation and workflow automation rather than interactive analysis and visualization. It is best compared to Count only if your primary need is complex data blending before analysis.

Pricing Comparison

Count publishes four tiers. Free is listed at $0 for small teams or personal projects and includes up to three editor seats. Pro is listed at $49 per editor per month and includes 75 collaborators; Scale is listed at $69 per editor per month, requires a minimum of 15 editor seats, and includes 250 collaborators. Viewer seats are included in every tier, and Count states there are no base fees or hidden costs.

Enterprise is listed as Custom for organizations with advanced security and compliance needs; the pricing page explicitly directs buyers to contact sales for that plan. Buyers evaluating Pro or Scale should confirm the number of editor seats they need, including Scale's 15-editor minimum, as well as the applicable collaboration, database-connection, and governance requirements. The supplied pricing evidence does not disclose a public Enterprise price.

When to Consider Switching

Consider moving away from Count if your organization needs a deeply governed semantic layer as the foundation of all analytics. Tools like Looker or Cube enforce centralized metric definitions more rigidly, which can be critical for large organizations where consistency across hundreds of dashboards matters more than exploratory flexibility.

If your team is heavily invested in dbt, Lightdash or Holistics may provide a more natural workflow by reading directly from your dbt project rather than requiring you to rebuild definitions in a separate tool.

For organizations where the primary audience is non-technical business users who prefer spreadsheet-like interfaces, Sigma Computing provides a familiar paradigm that can reduce training time and accelerate adoption.

If cost is a primary concern and your team has the technical capacity to self-host, Metabase offers a compelling open-source option. Power BI is another budget-friendly choice, especially for teams already using Microsoft 365.

For AWS-centric organizations, Amazon QuickSight offers tight integration with services like S3, Redshift, and SageMaker, plus a pay-per-session model that can reduce costs when many users access dashboards only occasionally.

If your needs center on product analytics and experimentation rather than general business intelligence, Amplitude is purpose-built for that use case and will likely outperform Count in areas like funnel analysis, cohort tracking, and A/B testing.

Migration Considerations

Moving from Count to another analytics platform involves several key steps. First, audit your existing canvases to identify which analyses are actively used and which can be retired. Export any SQL queries and Python notebooks embedded in Count canvases, as these will need to be recreated in your new tool's environment.

If you are using Count Metrics as your semantic layer, plan for the most significant migration effort there. Transitioning to LookML (Looker), Cube's schema definitions, or dbt metrics requires translating your metric definitions into the target tool's modeling language. We recommend running both systems in parallel during this transition to validate that metrics produce identical results.

Data warehouse connections are generally straightforward to re-establish, since most alternatives support the same warehouses Count connects to, including BigQuery, Snowflake, Databricks, PostgreSQL, and Redshift.

Permissions and access controls will need to be reconfigured in the new platform. Document your current permission structure in Count before beginning migration, particularly if you use fine-grained or group-wide access settings.

For teams that rely on Count's real-time collaboration features, verify that your target platform offers comparable simultaneous editing capabilities. Looker, Sigma, and Lightdash all support varying degrees of collaboration, but the interaction model differs from Count's shared canvas approach.

Finally, plan for user retraining. Each platform has its own interaction paradigm, and the transition from Count's canvas-based workflow to a more traditional dashboard or semantic-layer tool will require adjustment from your analytics team.

Public signals

About these signals

Verified factual signals from public sources. They indicate observable activity or interest, not total adoption, product quality, or cost.

Top 49% Google Trends search interest0 Hacker News matching stories, 90d

See all signals from 3 sources
Source
Signals
Last updated
Google Trends
Search interest:Top 49%overallTop 46%in Business Intelligence
September 21, 2026
Hacker News
Matching stories, 90d:0
September 21, 2026
Product Hunt
Comments:6Reviews:0Votes:85
September 21, 2026

Frequently asked questions

What is Count?

Count is a collaborative data canvas designed specifically for analytics teams, allowing them to visualize and analyze complex data sets in a more intuitive and interactive way.

How much does Count cost?

Pricing for Count starts at $10.00 per month, with a free plan available for small projects or teams. The paid plan offers additional features and support.

Is Count better than Tableau?

While both tools are used for data visualization, Count is specifically designed for collaborative analytics work, making it easier to share and analyze data with team members in real-time.

Can I use Count for market research?

Yes, Count can be used for market research as well as other business intelligence tasks. Its flexible canvas allows you to create custom visualizations tailored to your specific needs.

Is my data secure with Count?

Count takes data security seriously and uses industry-standard encryption methods to protect user data. All data is stored on secure servers, and access is restricted to authorized personnel only.

Can I integrate Count with other tools like Excel or Google Sheets?

Yes, Count offers seamless integration with popular productivity software like Excel and Google Sheets, making it easy to import and analyze data from various sources.

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