Deepnote vs Querio: a side-by-side comparison

Deepnote and Querio both support AI-assisted SQL, Python, notebooks, and published analytics. Their approaches differ once an analysis needs its own environment, several notebooks, custom packages, scheduled execution, or machine learning.

Deepnote gives teams a collaborative data workspace combining SQL, Python, and R with 100+ integrations, configurable compute, and reusable analytical workflows. Deepnote Agent works inside the project, while MCP lets external agents access the same notebooks, integrations, and execution tools.

In Querio, analysis starts in an Explore. Each Explore combines an agent conversation with the notebook created during the analysis. Users can inspect the generated SQL and Python, ask follow-up questions, and arrange selected results into shareable reports.

We’ll compare how both platforms handle agents, analytical workflows, app publishing, and pricing. To see how those differences play out, we also gave both agents a practical task using a public PostgreSQL database: take an analysis from the first question to an interactive application.

Deepnote vs Querio at a glance

What is Deepnote?

Deepnote is a collaborative data workspace where data teams and AI agents build, run, and review analytical work together. Its notebooks support everything from exploratory analysis and machine learning to agent-driven workflows, with a shared project environment that lets teams turn their work into scheduled jobs and interactive data apps.

Each Deepnote project can contain multiple notebooks with shared data integrations, files, installed packages, and environment settings. Rather than maintaining separate tools for querying data, writing Python, reviewing results, and publishing reports, teams can perform those tasks within the same project.

Deepnote also connects to more than 100 integrations, including Snowflake, BigQuery, PostgreSQL, Amazon S3, GitHub, and other services. SQL queries return DataFrames that can be used directly in Python, while configurable CPU and GPU machines allow teams to run more demanding analytical workloads.

Deepnote Agent works with the project's existing code and context. It can write SQL, modify Python blocks, execute calculations, create charts, and help maintain notebooks. Teams can also connect external agents through Deepnote MCP or work locally using Deepnote's open-source notebook format and development tools.

Teams can turn their analytical work into apps and automated workflows:

• Custom data apps and Streamlit apps: Build an interface around the analysis, with the notebook handling data and calculations behind it. For a new data app, select New data app from the workspace, describe what you need to the agent, and refine the result alongside the preview before publishing. Teams can also build apps with external coding agents (Codex, …) and host them on Deepnote.
• Scheduled and API-triggered runs: Run the notebook automatically to refresh an analysis, generate predictions, or carry out a step in a larger pipeline.

This gives teams several ways to put the same analytical work to use, whether colleagues need an interactive tool or another system needs the results on a schedule.

Deepnote also provides granular permissions so colleagues can inspect, comment on, execute, or edit a project without everyone receiving the same level of access.

Projects can also be opened in VS Code-compatible editors or converted to Jupyter, Quarto, percent-style Python, and marimo.

What is Querio?

Querio is an AI analytics platform organized around agents and connected business data. Users start an Explore by describing an analysis in natural language. Querio uses the available data context, generates SQL or Python, executes calculations, and presents the results in a notebook.

The notebook remains available for inspection and editing. Users can revise generated queries, reference earlier cells in follow-up requests, create visualizations, and arrange outputs into reports.

Querio provides three ways to work with an Explore:

• Chat mode focuses on the agent conversation.
• Hybrid mode shows the conversation beside the generated notebook.
• Layout mode presents selected notebook outputs on a cleaner canvas.

The interface is designed to make analytical work accessible without hiding the underlying calculations. In the example Dunder Mifflin sales report, Querio separates revenue by product category and account manager into tables, with the agent's analysis steps available alongside the results.

Querio's Context area lets teams inspect connected database tables, review their columns and descriptions, and control which information is visible to the agent. It also contains reusable SQL, Python, and Markdown Knowledge entries, along with joins, agent Skills, and other supporting instructions.

In the preconfigured Launchpad workspace we explored, these included revenue definitions, reporting conventions, and an existing customer-health workflow. That context helps explain how Querio's agent can work with business-specific data rather than relying on table names alone.

Deepnote vs Querio architecture

Deepnote: a programmable workspace for analysis, models, and apps

Deepnote organizes analytical work into projects rather than treating each notebook as a standalone workspace. A project can contain multiple notebooks that share files, data integrations, dependencies, permissions, and environment settings. This makes it easier to divide a larger analysis into smaller pieces without rebuilding the setup for every notebook.

For example, a team might use one notebook to prepare customer data, another to train and evaluate a model, and a third to publish the results as an interactive app. All three remain within the same project, reusing shared resources while allowing different contributors to work on separate parts of the analysis.

Deepnote also separates workspace and project permissions. Teams can control who can view, comment on, execute, edit, or manage a project. Enterprise controls include permission groups, SSO and directory sync, audit logs, federated authentication, and single-tenant deployment options.

Querio: conversational analytics over connected business data

Querio organizes each analysis in an Explore, which combines an agent conversation with a notebook. SQL cells retrieve data from connected warehouses, while Python cells handle calculations and transformations. Users can also create charts and add interactive controls such as dropdowns and date pickers. Uploaded CSV, Parquet, and JSON files are handled through DuckDB, giving users a queryable analytical layer without first loading the files into a warehouse.

Querio controls workspace membership, datasource access, Explore sharing, revocable public links, and review statuses such as Trusted or Needs review. Self-hosting is arranged with Querio directly.

Deepnote Agent vs Querio agents

How Deepnote Agent works

Deepnote Agent operates across the notebook and has two explicit modes.

• In Ask mode, Agent leaves the notebook unchanged. A user can ask about the data, discuss a possible approach, understand existing code, or get help with a Deepnote feature.
• In Edit mode, Agent can add, change, move, or delete blocks. It executes code, inspects the resulting output, and adjusts its work based on what happened. Deepnote shows the plan first, then each action as it happens, and closes with a summary of the changes made. Every action item links back to the block it touched, and the whole run can be discarded in one click if the result is not useful.

We find that review process particularly useful when Agent modifies an existing analysis. Rather than accepting a finished result without knowing what changed, we can inspect the affected blocks, review the diff, and undo the run if necessary.

For example, we might ask Agent to correct a revenue calculation, update the downstream charts, and rewrite the accompanying explanation. We can then inspect the changes instead of trying to reconstruct what happened from a final summary.

Agent also works with the wider Deepnote environment, including connected data sources and reusable modules. Teams can choose supported models, while Enterprise customers can bring their own LLM.

Deepnote MCP adds another route into the same workspace. An authenticated MCP client can search projects, read or create notebooks, update blocks, run notebooks, inspect run history, and review integration usage. Write operations follow the user or API key’s existing access. Our write-up on running Codex against Deepnote through MCP shows the pattern end to end.

How Querio agents work

Querio's agent works inside an Explore, drawing on the workspace's Catalog and Knowledge Base, where teams maintain metric definitions, table joins, and business rules. It uses that context to write SQL, run Python, create tables or charts, and explain results in the notebook behind the conversation. Reusing these definitions can help keep answers consistent across similar questions, although we'd still inspect the generated SQL before relying on business-critical calculations.

The agent retains the earlier messages and notebook cells within the Explore. Users can ask follow-up questions, request changes to an existing result, or mention a particular cell with @ when they want the agent to focus on it. Generated cells can be edited, rerun, bookmarked, or added to a Layout.

Querio's reactive cells update dependent calculations when an upstream query or input changes. Saved Explores can also be rerun through Automations, with results delivered by email or Slack. Its Embedded API lets developers bring conversational analysis into other applications.

Compute, custom environments, and machine learning

This is where Deepnote offers one of its most useful advantages for data science and engineering teams.

Deepnote compute and custom environments

Deepnote projects run on configurable machines, giving teams control over both the compute resources and the environment their analysis requires:

• Compute: Choose standard CPU machines for everyday analysis, scale up for more demanding workloads, or use GPU hardware for compatible machine learning tasks.
• Environment: Install packages, configure custom Docker images, manage secrets, and use a terminal without moving the analysis to another platform.

Imagine we're building a customer churn model. After querying historical customer activity, we might need to clean the data, engineer features, train multiple models, evaluate performance, and save the selected model for recurring predictions.

We can keep these steps inside a Deepnote project, sharing files and dependencies across notebooks. A scheduled run can later fetch updated data and generate predictions without someone repeating the analysis manually.

Querio compute and analytical execution

Querio combines warehouse-based SQL execution with managed Python processing.

Its runtime supports transformations, calculations, visualizations, and interactive notebook controls. Uploaded data can also be analyzed without first loading every file into a warehouse.

Teams with specific runtime requirements should confirm the available compute, package support, and environment controls with Querio.

That model keeps much of the runtime administration outside the analyst's day-to-day workflow.

Querio also positions itself around analytics-as-code workflows, including ETL and automation. However, teams that require a particular Python version, custom system dependencies, Docker images, or GPU hardware should evaluate the available runtime controls before choosing a plan.

Deepnote explicitly supports those configurations as part of its notebook environment, making it a stronger fit for teams whose analysis extends into model development and custom computational work.

MCP, Git, and local development

Both Deepnote and Querio let developers and external AI agents work with their analytical environments. The difference is how those tools interact with the underlying notebooks, project resources, and business context.

Deepnote CLI, MCP, and Sync

Deepnote extends its notebook workflow beyond the browser through MCP, Git synchronization, and local development tools. It gives developers and coding agents several ways to work with projects outside the browser.

• Deepnote CLI: Run notebooks locally or in Deepnote Cloud, inspect and validate notebook files, synchronize a workspace, and publish custom apps or Streamlit apps. These commands let teams include analytical work in scripts, CI workflows, and coding-agent tasks.
• Deepnote MCP: Connect compatible agents to project discovery, notebook editing, execution, and results inspection, subject to the authenticated user’s permissions.
• Deepnote Sync: Bring notebook changes into Git workflows so teams can review analytical code through pull requests.

The open .deepnote format keeps notebooks readable outside Deepnote. A developer can edit a notebook locally, review the changes in Git, run it in Deepnote Cloud, and publish an app from the terminal. Format conversion is also available when moving work between notebook tools.

With @deepnote/convert, teams can move between Deepnote, Jupyter, Quarto, percent-style Python, and marimo formats.

We find this particularly useful when analytical work needs to follow the same development practices as the rest of a team's code. A notebook can be edited in an IDE, reviewed through Git, and executed in Deepnote without maintaining separate versions of the analysis.

Querio MCP, Git, and APIs

Querio also supports MCP, CLI tooling, and APIs for developers working with its analytics environment. External tools can interact with Querio's analytical resources, while its Embedded API brings conversational analysis and associated results into other applications.

Querio's Context layer can be synchronized with Git, allowing teams to version business definitions, instructions, and other resources used by its agents. This provides a development workflow for maintaining the knowledge behind recurring business analyses.

For teams maintaining analytical notebooks as code, we favor Deepnote's combination of portable notebook files, local execution, Git synchronization, and shared project environments. Developers and external agents can work with the same notebooks, integrations, and compute resources that the rest of the team uses.

Deepnote vs Querio in practice: building an RNA analysis app

We wanted to test how far Deepnote Agent and Querio's agent could take an analysis, from connecting to an unfamiliar database to building something stakeholders could interact with.

For this, we used RNAcentral, a public database containing RNA sequences from different organisms. An RNA sequence is essentially a string of four letters: A, C, G, and U. Researchers can study these sequences to understand their lengths and composition, including Guanine and Cytosine (GC) content, which measures the proportion of G and C bases.

We connected both platforms independently to RNAcentral's public PostgreSQL database and gave their agents the same task: find the relevant data, analyze the sequences using SQL and Python, create useful visualizations, and build an interactive RNA Composition Explorer.

The intended result was straightforward. A user should be able to select an RNA type, filter reference sequences by length, and enter their own sequence to compare its composition with the selected group. This allowed us to evaluate both the underlying analysis and how each platform handled turning it into an interactive application.

Building the RNA explorer with Deepnote Agent

We started with a new Deepnote project and connected RNAcentral using its PostgreSQL integration.

Deepnote Agent inspected the database schema to locate the sequences, RNA type information, and relevant annotations. It then developed SQL queries to retrieve a bounded set of records, followed by Python calculations for sequence length, GC content, and the proportions of A, C, G, and U.

Working with RNAcentral's extensive sequence catalog, Deepnote Agent developed a targeted sampling approach to retrieve relevant records efficiently. It then combined SQL-based data extraction, Python calculations, and visualizations in the notebook, documenting the methodology and limitations of the resulting analysis.

Within that same initial request, Deepnote Agent also generated a standalone RNA Composition Explorer. It created the HTML interface, saved it in the project's files, and provided a hosted URL without requiring a separate request to build the application.

The interface goes beyond displaying the charts from the notebook. It includes an RNA type selector, sequence-length filters, and a field for entering a new RNA sequence. It presents summary metrics, composition comparisons, distributions, and a table of matching reference sequences.

The interface uses the project's underlying notebook calculations. Deepnote also kept the SQL, Python, and analytical assumptions available for inspection, so we could continue working on the analysis without recreating its logic in a separate application project.

Building the RNA explorer with Querio

We repeated the initial task in a new Querio Explore, connecting it independently to the same RNAcentral database.

Querio's agent inspected the schema and created a notebook with SQL queries, Python calculations, data-quality checks, and charts. It also added statistical comparisons across RNA types, including tests for differences in sequence length and GC content.

The agent documented its sampling approach and flagged limitations that could affect the interpretation of those tests. The generated notebook included interactive controls for selecting RNA types, filtering by domain of life and sequence length, checking Rfam annotations, and comparing an entered sequence with the reference data.

We could inspect the SQL and Python, review the charts and statistical comparisons, and interact with the generated controls. Querio had handled the analytical requirements well, although the notebook still presented the results in cell order rather than as a finished application interface.

We then sent a separate follow-up prompt asking Querio to organize the relevant cells into its Layout and clean up the explanatory Markdown. Querio supports arranging notebook outputs into a shareable interface, but in our test, that presentation step required an additional request. Deepnote Agent, by comparison, had already generated and hosted its standalone HTML interface as part of the original task.

What the exercise showed

Both agents handled the analytical work: they explored the database, generated SQL and Python, calculated sequence composition, and created charts and interactive controls. The difference emerged when we asked for an application people could use:

• Deepnote: Agent created the analysis and a custom HTML interface, saved the app’s files in the project, and provided a hosted link within the initial request. The interface and analytical code remained available for further development.
• Querio: Its agent produced a notebook with calculations, charts, and interactive controls. We then asked it to arrange selected outputs into a Layout and refine the presentation.

Deepnote’s newer Data Apps and Streamlit support extend this workflow further. Teams can build apps within Deepnote or use an external coding agent (Codex, …) and publish them to Deepnote, with notebooks handling the data and calculations behind a custom interface.

For teams building analytical tools, that flexibility is the stronger advantage. The initial result can become an application they continue to develop, with control over both its interface and the analysis behind it.

Deepnote vs Querio pricing and plans

Querio’s paid plans carry a much higher starting cost than Deepnote’s. For a five-editor team, Deepnote Team costs $2,340 a year before additional usage charges, with unlimited viewers. Querio Core requires a $25,000 annual workspace commitment, more than ten times that annual subscription cost.

Deepnote’s pricing includes:

• Free: Up to three editors and five projects, with Basic compute and limited Agent usage.
• Team: $39 per editor per month billed annually, including unlimited viewers and notebooks, premium integrations, scheduled execution, and AI and compute credits.
• Enterprise: Custom pricing for controls such as SSO, directory sync, audit logs, unlimited AI, permission groups, private Docker images, bring-your-own LLM, federated authentication, single-tenancy, and volume compute discounts.

The editor-based model matches how analytical teams usually work. A smaller group builds, and a larger audience reads notebooks, dashboards, and apps without a paid viewer seat, so cost tracks the number of people writing analysis rather than consuming it. A team can also start on Free and move to Team when the work needs it.

Querio’s current plans use workspace pricing:

• Free: Unlimited users and data connections, notebooks, dashboards, MCP and CLI access, and 500 monthly AI credits.
• Core: $25,000 billed annually, including 10,000 monthly AI credits, SSO and group sync, datasource permissions, API access, and private connectivity.
• Enterprise: Custom pricing, with single-tenant hosting and additional deployment and support options.

For a small team that needs SQL, Python, automation, and custom apps, Deepnote offers a broader development environment at a fraction of Querio Core’s starting cost. Querio’s free plan is worth distinguishing from that paid proposition; moving to Core needs a specific business justification.

Deepnote or Querio: which should you choose?

If you’re choosing based on how much your team can build, automate, and maintain in one platform, Deepnote offers more room to work. You can start with a question for Agent, develop the analysis across notebooks, add the packages or compute it needs, and turn the result into a scheduled workflow or custom app.

Our RNAcentral exercise brought that advantage into focus. Both agents produced useful analyses, but Deepnote Agent also delivered a hosted interface from the initial request. The app’s files and analytical code stayed together in the project, ready for further development. Teams can also bring their preferred coding agent into that workflow and publish apps to Deepnote.

Querio’s appeal is its focus on conversational business analytics, with shared metric definitions guiding the agent’s answers and Layouts presenting the results. If that workflow is your main requirement, its free plan is a reasonable place to start. The paid offering is harder to recommend on value: at $25,000 a year for Core, a small team needs a compelling reason to pay substantially more than Deepnote Team.

When the work needs to become a reusable computational workflow or custom application, Deepnote's combination of agents, programmable notebooks, and project-level environments gives the team more room to build on what it already has.