Buying snapshot
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| Decision point | What Count states |
|---|---|
Who you pay for | Owners, Admins and Editors are billable seats. Viewers and Report Viewers are free. Admins are available on Enterprise. |
Workspace users (all roles) | Up to 5 on Free, up to 75 on Pro, unlimited on Enterprise |
Data sources | Database and MCP connections (BigQuery, Snowflake, Postgres and more): 1 on Free, 3 on Pro, unlimited with admin approval on Enterprise. File upload (CSV, PDF, Excel, Word and more) and Google Sheets import. dbt Core and dbt Cloud integration is not on Free. |
Compute layer capacity | The comparison table gives the largest table Count's DuckDB compute layer can hold as 500MB on Free, 2GB on Pro and up to 32GB on Enterprise, with Python VM memory at the same steps. The Pro plan card instead says 4GB compute capacity (Python VM & dataset extract size); Enterprise is up to 32GB in both places. |
Security and hosting | SOC 2 and GDPR, and no model training on your data, on every plan. SSO, SCIM, HIPAA compliance and US or EU data residency are not on Free; the plan summary lists them under Enterprise. |
Support | Free: community support through the Slack community. Pro: email, Slack or Teams support. Onboarding and custom training programs are not on Free. |
Our take
Count fits a data team that wants analysts and business users in one shared canvas, with most readers on free Viewer seats and only the people who build on paid Editor seats. If your rollout depends on identity, governance or hosting controls, plan for an Enterprise conversation from the start rather than expecting Pro to cover it.
Evaluate Count
Comparisons
Where it stands out
Queries can run in the browser. Count's engineering blog says any cell (SQL, control or visual) can run on your source database or in your browser with DuckDB-WASM. Fewer queries then reach your warehouse, and DuckDB can merge a CSV file, Google Sheets and a Snowflake table in one canvas.
Built for many people at once. Count designed the canvas for a whole team working on a problem with potentially hundreds of queries at the same time. The blog reports canvases with up to 690 cells and up to hundreds of viewers, and calls 690 the most anyone has used, not a maximum.
Answers you can check. Count says every claim the agent makes is traced to its source, assumption or inference, so hallucinations are caught before anyone acts on them.
A free tier for a real trial. Free costs $0 with no card required, so a small team can test the canvas on its own data before anyone pays for a seat.
Product screenshot
Trade-offs to weigh
Governance sits on Enterprise. The governed semantic layer with admin change approvals, user groups, row-level security and embedded analytics are Enterprise items. A team that must restrict which rows each user sees cannot do that on Pro.
AI runs on credits with fair use. Pro and Enterprise include AI credits on a fair-use basis; Count states 98% of teams never need a top-up. Top-ups are optional with spend controls, so heavy agent users should agree the fair-use terms before rollout. Free has daily AI credit limits and limited models.
Pricing and plan conditions
The bill is driven by billable seats: Pro is priced per editor per month. You can add or remove seats at any time in workspace settings, and changes are prorated automatically. Count accepts all major debit and credit cards, and billing is managed from workspace settings.
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| Plan | Published price | What it adds |
|---|---|---|
Free | $0 | 1 project, 3 canvases, web search, file uploads, 2 automations. |
Pro | $49 editor / month | Everything in Free, plus: unlimited AI credits (fair use); unlimited canvases, projects, reports and automations; shared agent skills and context; Slack integration and MCP client; 14 days of version history. |
Enterprise | Let's talk | Everything in Pro, plus: unlimited editors; choose your own models or bring your own API keys (Anthropic, OpenAI, Google or custom endpoints); REST API; workspace telemetry; unlimited version history; credit top-ups with volume discounts. |
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 36% Google Trends search interest0 Hacker News matching stories, 90d
See all signals from 3 sources
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
- Looker and Count both turn warehouse data into answers a company can share. Looker delivers them as governed dashboards and explores built on a central LookML model, while Count is a collaborative canvas where a team explores with SQL, metric trees and agent-assisted analysis. The pick follows whether the main output is a governed dashboard or open, shared exploration.Applies to: Whether analysis is delivered as governed dashboards or as exploratory notebooks.
- Tableau
- Tableau and Count are both places where a team builds dashboards and shares what the data shows. Tableau focuses on drag-and-drop visual analytics with deep chart design, and runs as desktop, server or cloud software. Count puts SQL, metric trees and dashboards on one shared canvas where a team works through a problem at once, which shapes a stack around joint exploration rather than finished visuals.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 marketed as AWS Quick Sight within Quick, delivers AI-powered BI within the AWS ecosystem. It prices users by role, sells Reader sessions in bulk, and has built-in machine learning 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 pricing page 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 capacity pricing that buys Reader sessions in bulk, from $250 per month for 500 sessions.
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.