Best hex alternative

Hex is a popular tool for combining SQL and Python in a collaborative notebook format. However, its high pricing ($27,000/year for 10 editors and 10 viewers) and complexity for non-technical users push many teams to seek alternatives. This article reviews five AI business intelligence platforms, with Querio as the top choice: an AI data analyst that answers in plain English with real SQL and Python, built on git-native analytics, with no per-seat pricing. Here's a quick breakdown:

• Querio: Plain English questions answered with SQL and Python, definitions and dashboards in your git repo, free plan and Core at $2,499/mo, unlimited users.
• Tableau: Strong visualizations, $15/user/month, limited AI features.
• Deepnote: Collaborative notebooks, free tier available, coding required.
• Mode (Analyst Studio): SQL-first approach, $137,000/year average, robust AI integration.
• Looker: Enterprise-focused, $35,000–$60,000/year, steep learning curve.

Querio stands out because anyone can ask in plain English, the data team reviews changes to definitions as pull requests, and there's no per-seat pricing, so analytics can grow across the company.

Quick Comparison:

Platform	Price (Starting)	Key Features	Best For
Querio	Free plan; Core $2,499/mo	AI agent, git repo, unlimited users, live data	Data teams and their AI agents
Tableau	$15/user/month	Visualizations, collaboration tools	Data visualization enthusiasts
Deepnote	Free tier	Collaborative notebooks, coding	Technical teams
Mode	$137,000/year	SQL-first, AI assist, dbt integration	Advanced analytics professionals
Looker	$35,000/year	LookML semantic layer, enterprise	Large organizations

Querio leads because people and AI agents work from the same definitions.

1. Querio

AI and Natural Language Capabilities

Querio is an AI data analyst you can check. You ask questions like, "What's our monthly recurring revenue by region?" in the app or Slack, and the agent answers with charts and tables backed by real SQL and Python in a notebook your analysts can open, check and rerun. You always see how a result was produced.

Context, metric definitions, saved queries and dashboards are plain files in a git repo your team owns. Sales and finance get the same MRR and churn numbers because every answer comes from the same definitions, and if you use dbt, Querio reads your MetricFlow metrics instead of keeping a second copy. Agents propose changes, and saving context or promoting a dashboard opens a pull request your reviewers approve. Claude, ChatGPT, Claude Code and Cursor can do the same work through Querio's MCP server and CLI, seeing only what their user may see. For more advanced users, the integrated workspace handles data science work in the same notebooks.

Warehouse and Integration Support

Querio queries your data warehouse directly, so there's no data duplication. It supports live connections to platforms like Snowflake, BigQuery, Amazon Redshift, ClickHouse, and PostgreSQL, so you're always working with current data. On Snowflake and BigQuery, queries run as the person through per-user OAuth, so the warehouse's own access rules apply. On the compliance side, Querio is SOC 2 Type II, GDPR and HIPAA compliant.

Pricing and Scalability

Querio keeps things simple: there is a free plan, and Core is $2,499 a month (or $25,000 a year billed annually). There's no per-seat pricing, with unlimited users and connections on every plan. Only AI model calls are metered, as credits, and admins can set a spending cap. The free plan includes 500 AI credits a month, the MCP server and the CLI.

2. Tableau

AI and Natural Language Capabilities

Tableau's AI functionality revolves around the Tableau Agent, which allows users to perform natural language queries for tasks like filtering, aggregations, and time series analysis. However, this feature is currently limited to worksheets[1]. Tableau uses Data Indexing and the Einstein Trust Layer to interpret metadata while ensuring row- and column-level security. Additionally, the Explain Data tool helps users understand metric calculations, such as "Days to Ship", making complex data more accessible.

Collaboration Features

Tableau makes team collaboration simple with its web authoring tools on Tableau Server or Tableau Cloud, enabling real-time edits without the need to switch between desktop and browser environments. Dashboards can be published in seconds, and automated subscriptions deliver updates daily or weekly. Tableau also integrates with Slack, allowing users to query data and view visualizations directly within their communication platform[4].

"Tableau gives us the ability to work together on the solution and then collaborate more effectively as we're building it." – Andrew Aucott, General Manager, Business Intelligence, Jet2[4]

These features, combined with strong data connectivity and integration options, make collaboration efficient and seamless.

Warehouse and Integration Support

Tableau connects to more than 200 data sources, ranging from cloud platforms and on-premises databases to flat files. It also supports native connectors, as well as JDBC and ODBC for custom connections. Deployment options are flexible, whether you choose the fully hosted Tableau Cloud, public cloud providers like AWS, Azure, or GCP, or an on-premises setup with Tableau Server. Security is a priority, with SSL/TLS encryption and compliance with HIPAA and GDPR standards[5][7].

Pricing and Scalability

Starting at just $15 per user per month, Tableau offers an affordable solution for organizations of various sizes. For example, a 500-person company could expect annual costs of approximately $174,600, significantly less than some enterprise-only platforms, which can exceed $1.2 million per year[3]. With a 4.4/5 rating on major review sites and an 8.4/10 rating on PeerSpot, users frequently highlight its drag-and-drop interface and powerful visualization tools. However, some have reported performance issues when dealing with datasets in the terabyte range[7].

3. Deepnote

AI and Natural Language Capabilities

Deepnote is designed to meet the increasing demand for smarter analytics workflows by blending automation with familiar tools. One standout feature is its autonomous code generation, which uses Codeium-powered, context-aware suggestions to auto-generate and refine SQL, Python, and text blocks. These suggestions are tailored to your data schemas, making it easier to write and correct code efficiently[8].

For users already working with Jupyter, Deepnote offers seamless compatibility. You can upload your existing .ipynb files and immediately enhance them with AI-driven features. This system not only reduces errors but also improves the collaborative experience, as outlined in the next section[8].

Collaboration Features

Deepnote is built for teamwork. It supports real-time multi-user editing, allowing teams to work on notebooks simultaneously. You can add comments directly to specific code or text blocks, making it easy to share feedback without relying on separate communication tools[9].

"Collaborative data analysis where team members can freely share their work and get feedback… This made the analysis workflow much faster." – Deepnote Community[9]

Sharing your work is simple. You can send a link or an email invite, with detailed permission settings to control access. Projects are organized in shared folders, making it easy to revisit past analyses. With over 500,000 data professionals already using Deepnote, its focus on collaboration has proven effective at scale[9]. These features integrate smoothly with Deepnote's extensive data connectivity options, which are detailed below.

Warehouse and Integration Support

Deepnote connects to 100+ data sources, including popular cloud warehouses like BigQuery, Snowflake, Redshift, Athena, and ClickHouse[9]. It also supports direct integrations with tools like Git, GitLab, and dbt, including Jinja support for SQL blocks[8][9]. For file-based connections, Deepnote works with Google Drive, Amazon S3, Dropbox, and OneDrive, offering twice as many integrations as competing platforms like Hex[8].

Instead of using Jupyter's JSON-based file format, Deepnote employs a YAML-based format, which simplifies version control by providing cleaner git diffs[10].

Pricing and Scalability

Deepnote offers flexible pricing options to cater to both individuals and businesses. For smaller teams or solo users, there's a free tier and a two-week free trial that doesn't require a credit card[8]. Enterprise plans are available for larger organizations, with custom pricing and advanced features like role-based security, SSO (SAML/OIDIC), and directory sync[9].

The platform is HIPAA, SOC 2, and GDPR compliant, making it a solid choice for industries with strict regulatory requirements[9]. Recent updates have enhanced data app performance by 40%[8], and users can now choose from a range of hardware options, from standard CPUs to high-performance GPUs, to handle more demanding workloads[9].

4. Mode

AI and Natural Language Capabilities

Mode's AI Assist simplifies SQL creation by generating code directly from natural language prompts embedded in a --! comment. For instance, typing SELECT --! total sales by rep will instantly produce the corresponding SQL query[11].

This AI functionality uses advanced GPT models, considering your warehouse's name, table column names, and data types to deliver precise results. Importantly, Mode does not share column values or historical data with OpenAI. The company also adheres to a strict Data Privacy Agreement, ensuring your data remains secure and isn't used to train external models[11].

In 2023, Mode was acquired for $200M and later rebranded as Analyst Studio in early 2025[13]. This revamped platform combines Mode's SQL notebooks and Python/R workbench with ThoughtSpot's AI-driven natural language search. Analyst Studio introduces AI agents to handle tasks like data categorization, anomaly detection, and reporting, enabling teams to shift their focus to strategic analysis instead of manual data preparation. These AI tools enhance productivity while fostering collaboration - a core principle of Mode's approach.

Collaboration Features

Mode offers a real-time multiplayer workspace, allowing team members to see each other's cursor movements and updates live while building dashboards together[16]. This environment bridges the gap between technical analysts and business users by combining code-based workflows with drag-and-drop visualizations[12][16].

"Mode is valuable as a full-service platform from a data analyst all the way to an end user. Analysts can go deep, end users get the data they need, and our team gets to make everyone a part of the data strategy." – Adam Smith, Analytics Manager[12]

Reports can be scheduled for automatic delivery via Slack or email, and analysts have the option to add explanatory text alongside charts to provide context, turning raw data into actionable insights[12]. Additionally, Mode's integration with the dbt Semantic Layer ensures consistent metrics across the organization, creating a unified source of truth for data[14][15].

Warehouse and Integration Support

Mode connects seamlessly with major cloud data warehouses like Snowflake, BigQuery, Redshift, and ClickHouse. Its proprietary Helix engine caches large datasets directly in the browser, allowing users to filter and aggregate data quickly without repeatedly querying the warehouse. Teams can typically get started in 30 minutes or less[14][15][16].

The platform's integration with the dbt Semantic Layer brings pre-defined metrics into Mode, and any updates to foundational data automatically refresh related reports[12][15]. This streamlined process significantly reduces setup times compared to traditional BI tools, which often require weeks or months to implement.

Pricing and Scalability

Mode operates on custom enterprise contracts, with average agreements reported at approximately $137,000 per year[13]. Pricing generally ranges from $6,000 to over $50,000 annually, depending on the size of the deployment[13][16]. For new users, a 30-day free trial is available[14].

Since the ThoughtSpot acquisition, Analyst Studio has been positioned as an enterprise-level platform, with costs for mid-to-large deployments reaching six figures annually[13]. While official pricing requires contacting sales, smaller team plans have been noted starting at around $12 per user per month in certain cases[2]. This pricing structure highlights Mode's focus on delivering advanced analytics and collaboration tools, even as competitors like Querio emphasize simplicity and transparent costs.

5. Looker

AI and Natural Language Capabilities

Looker’s Gemini AI suite brings conversational analytics right into your workflow. With its Conversational Analytics feature, you can simply type questions in plain English and get instant visualizations like charts or tables. It also supports follow-up questions, making it feel seamless. For more advanced needs, the Code Interpreter steps in, handling tasks like forecasting, anomaly detection, and multi-step calculations.

What sets Looker apart is its reliance on the LookML semantic layer for AI-generated queries. This ensures that the insights you get align with your organization’s governed business definitions, avoiding the pitfalls of pulling raw, unstructured data. The platform also provides reasoning steps for transparency and generates narrative summaries of charts. These summaries can be directly added to Google Slides, simplifying presentations.

Collaboration Features

Central to Looker’s collaboration tools is the LookML semantic layer, which acts as a unified source of truth. By centralizing business logic and definitions, it eliminates inconsistencies in metrics across departments. The platform supports Git-based version control, allowing data teams to track changes, review code with pull requests, and roll back models when needed. Role-based permissions in Team Workspaces help organize shared assets efficiently, with pricing tiers like $400/year for Viewers and $1,665/year for Developers.

Teams can also create and share custom AI agents to streamline decision-making on data-driven questions. Looker integrates with tools like Slack and Microsoft Teams, delivering dynamic charts directly into conversations. Additionally, its Connected Sheets feature enables teams to analyze live Looker data within Google Sheets for better collaboration.

"At YouTube, we're focused on helping creators succeed... We've been testing Conversational Analytics in Looker to give our partner managers instant, actionable data that lets them quickly guide creators and optimize creator support." – Thomas Seyller, Senior Director, Technology & Insights, YouTube Business [17]

Beyond these features, Looker ensures smooth integration with various data sources, enhancing team collaboration.

Warehouse and Integration Support

Looker connects to major cloud data warehouses like BigQuery, Snowflake, Redshift, PostgreSQL, MySQL, and SQL Server. Following Google’s $2.6 billion acquisition in 2019 [15], it also offers deep integration with the Google Cloud Platform. However, setting up Looker can be a bit of a hurdle. Every new data source or metric requires logic to be defined in LookML, which then needs to be reviewed and redeployed. This modeling process, combined with additional ETL steps, can delay ad hoc queries and create bottlenecks. While Looker earns a 4.6/5 rating on Modern DataTools [18], the learning curve for LookML remains a common pain point for users.

Pricing and Scalability

Looker’s pricing is tailored for larger organizations with advanced data needs. The Standard plan ranges from $35,000 to $60,000 annually, while enterprise contracts average around $150,000 per year. On top of this, per-user fees apply - $400/year for Viewers and $1,665/year for Developers. Total costs often exceed $5,000 per month, even before factoring in warehouse expenses.

Looker is best suited for organizations with strong SQL expertise and a commitment to managing LookML. While it excels in enterprise governance and structured analytics, its high costs and complex setup process may not appeal to teams looking for quick, flexible solutions. These elements solidify Looker’s role in the evolving world of AI-driven analytics.

Strengths and Weaknesses

Querio pairs a plain English interface with definitions that live in your git repo and changes that go through review. That mix gives it a clear edge over traditional platforms.

Querio shines with its natural language interface, enabling users to interact with live warehouse data using plain English. Its context and metric definitions are files in your git repo, so every person and agent answers from the same definitions.

Tableau is well-known for its visualization capabilities, powered by its VizQL engine. However, its advanced features come with a steep learning curve, and its premium pricing can be a drawback. Compared to Querio, Tableau's complexity and cost make it less appealing for users seeking a streamlined and budget-friendly solution.

Looker focuses heavily on enterprise-level governance, which can feel rigid compared to Querio's flexible and approachable model. Its LookML semantic layer ensures consistent metrics across departments, but the centralized modeling approach often requires technical expertise, making it less ideal for smaller teams. As Trakkr AI Analysis points out, "Looker is the industry benchmark for metric consistency; Mode's flexibility comes at the cost of potential logic silos" [6]. Querio, by contrast, offers a more accessible and user-friendly alternative.

Mode (now ThoughtSpot Analyst Studio) prioritizes code-based workflows, which can limit its usability for non-technical users. This technical emphasis creates a barrier for business stakeholders, a problem Querio solves with its plain English interface and governed design.

Deepnote focuses on collaborative notebook environments but requires coding skills, making it less suitable for natural language analytics and self-service dashboards for non-technical users. Querio's ability to democratize analytics across skill levels highlights a significant advantage over Deepnote.

Conclusion

Querio stands out as the top choice for data teams and their AI agents. It serves both technical and non-technical users: its agent turns plain English into SQL and Python you can inspect, without moving data or needing advanced skills.

The platform gives teams natural language query capabilities, and its definitions, dashboards and notebooks live as files in a git repo you own. With a free plan, no per-seat pricing and spending caps on AI credits, budgeting stays predictable. Querio queries live data warehouses and is SOC 2 Type II compliant.

Unlike competitors that require extensive training, Querio provides AI-powered business intelligence where agents do the work and a person saves any change to shared context. Every query, agent action and promotion is logged. This makes it an ideal choice for business intelligence teams aiming to scale analytics across their organization.

FAQs

How does Querio turn plain English into SQL you can trust?

Querio's agent turns plain English questions into SQL and Python in a notebook your analysts can open, check and rerun. It answers from the metric definitions in your git repo, and changes to those definitions go through pull requests, so users without SQL skills get answers the data team has reviewed.

Will Querio work with my existing data warehouse without moving data?

Yes. Querio connects to your existing data warehouse, including Snowflake, BigQuery, Redshift, Databricks, ClickHouse and PostgreSQL, and queries your data right where it is. Nothing gets copied out.

What does Querio’s pricing include?

Querio has a free plan, and Core is $2,499 a month (or $25,000 a year billed annually). There's no per-seat pricing: every plan has unlimited users and data connections, and only AI model calls are metered, as credits.

Key features include:

• Natural Language Querying: Allows users to interact with data using plain language, making it easier to extract insights without needing advanced SQL skills.
• Integration with Major Data Warehouses: Connects to live data in Snowflake, BigQuery, Redshift, Databricks, PostgreSQL and more.
• Git and Agents: Definitions and dashboards live in your git repo, and Claude, ChatGPT, Claude Code and Cursor work from them through the MCP server and CLI on every plan.

Querio's Core plan adds SSO, groups, audit history and unlimited automations.