Deepnote vs Omni: a side-by-side comparison

Deepnote and Omni both let teams use AI to analyze data, but they serve different goals. Omni is a natural option for organizations moving away from Looker while keeping a familiar approach to business intelligence: shared metric definitions, warehouse queries, and dashboards. Deepnote gives people and AI agents a shared workspace for developing, executing, and maintaining analytical work.

Omni’s founding team includes former Looker leaders, and the platform offers tools for migrating LookML models and reporting content. Its appeal is straightforward: preserve the foundations of governed BI while giving users more flexible ways to explore data and ask questions in plain English.

For business users, that can feel like ChatGPT for company data, with answers grounded in the organization’s metrics and permissions. Analysts can continue working through SQL, spreadsheet calculations, and visual exploration.

Deepnote starts with an executable project that combines data connections, SQL, Python, notebooks, and Deepnote Agent. Teams can ask a business question, investigate the result, develop a statistical model, and turn the work into a recurring workflow or interactive app within the same environment.

This comparison looks at that practical choice: updating an established BI workflow with Omni or building analytical workflows with Deepnote.

Deepnote vs Omni 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 provide an executable environment for everything from exploratory analysis and machine learning to reusable agent workflows, while keeping the code, results, and context available to everyone working on the project.

It combines SQL, Python, R, charts, and interactive inputs in notebooks that share a project environment. A project can contain several notebooks alongside its files, data integrations, installed packages, and machine configuration.

The platform connects to more than 100 data sources and services, including Snowflake, BigQuery, PostgreSQL, Amazon S3, and GitHub. A SQL block can return a DataFrame that the next Python block uses directly, so analysts can move from warehouse queries into statistical analysis without exporting results into another tool.

Deepnote also supports configurable CPU and GPU machines, custom Docker environments, and a terminal for managing dependencies.

Deepnote Agent can write and modify notebook code, execute calculations, inspect outputs, and help maintain existing analysis. Through Deepnote MCP, external agents such as Codex, Claude, and Cursor can work with the same projects, subject to workspace permissions.

Once the analysis is ready, teams can publish interactive data apps, schedule notebooks, or trigger execution through the API. The notebook remains the source of the analytical logic, rather than becoming a separate artifact that needs to be maintained elsewhere.

What is Omni?

Omni is a business intelligence platform built around a shared semantic model, warehouse analytics, and AI. Its founding team includes Colin Zima and Jamie Davidson, who previously held leadership roles at Looker.

That background helps explain its appeal to organizations replacing Looker. Teams can retain the familiar practice of defining business metrics centrally while adopting conversational analytics and more flexible workbook exploration. Omni also provides Looker migration tools, including a LookML converter that translates model structures into Omni’s equivalents.

For business users, the experience resembles ChatGPT for company data: ask a question, inspect the answer, and follow up. The semantic model supplies business definitions and access rules, giving the agent context beyond database column names.

Analysts can move between natural-language questions, SQL, spreadsheet formulas, and visual query building. They can also develop calculations during exploration and promote useful definitions into the shared model.

Omni therefore offers a way to modernize an established BI workflow. Deepnote is the stronger fit when the team also needs to develop custom analytical code, manage dependencies, train models, or maintain executable workflows alongside its reporting.

Deepnote projects vs Omni workbooks

Deepnote: shared projects for ongoing analysis

Deepnote organizes analytical work into projects rather than treating every notebook as an isolated document.

A project can hold several notebooks that share integrations, files, dependencies, and environment settings. Different team members can work on separate notebooks without each one needing a new data connection or package setup.

Suppose we're investigating customer retention. We might use one notebook to prepare subscription data, another to analyze customer cohorts, and a third to evaluate whether an intervention improved retention.

The notebooks serve different purposes, but they share the same project resources. If the analysis grows into a forecasting or machine learning task, we can continue developing it in that environment.

Deepnote also supports real-time editing, comments, version history, and granular project permissions. Teams can distinguish between viewing, commenting, executing, editing, and managing the analysis.

For projects maintained alongside other code, Deepnote Sync keeps notebook files synchronized with Git. The open-source .deepnote format uses human-readable YAML, making changes reviewable through pull requests.

This combination works well when several people contribute to an analysis over time, especially when the project contains more than one related notebook.

Omni: a familiar BI workflow for teams leaving Looker

For analysts accustomed to Looker, Omni’s workbooks offer a familiar starting point: choose business fields, query warehouse data, and turn the results into a report. Omni expands that workflow with SQL editing, spreadsheet-style calculations, and visual exploration in the same workbook.

Omni also supports CSV and Excel uploads for data that isn't already in the warehouse, along with editable input columns for certain workflows.

For an order-volume breakdown, an analyst can select fields such as Order Items Count and Product Category, then display the results as a bar chart. The workbook keeps the field picker, results, and visualization together, with options to inspect the SQL or adjust the query. This makes it possible to move between exploring the data and refining the analysis without switching to another tool.

A workbook can also contain multiple tabs. Analysts can keep related queries in one document, duplicate existing tabs, and create linked tabs that reuse a query for different visualizations. Saved workbooks have version history and can be shared with other users.

This makes Omni a practical option when the goal is to improve how people explore and report on warehouse data. Deepnote becomes more useful when the next step is to develop the analysis further—for example, testing a retention hypothesis in Python or building a prediction workflow that shares the project’s data connections and dependencies.

Deepnote Agent vs Omni AI agents

Both platforms support agent-assisted analytics, but the work their agents are designed to perform reflects the underlying product architecture.

Deepnote Agent: working with executable notebooks

Deepnote Agent operates directly inside the notebook, using the surrounding project context to understand and modify an analysis.

It has two modes:

• Ask mode lets us discuss an approach, investigate code, or ask questions about the data without modifying the notebook.
• Edit mode lets Agent create, change, move, or remove blocks, execute code, inspect outputs, and adjust its work based on the results.

In Edit mode, Agent presents a plan before making changes. Individual actions link to the affected blocks, and completed edits can be inspected through a before-and-after diff. We can also undo the entire run.

This is particularly useful when Agent is working on an existing notebook. If we ask it to change a retention calculation and update the downstream charts, the important part isn't simply whether it produces a new result. We also need to understand what changed and whether the revised logic is appropriate.

Deepnote's newer Agent Workspace extends that approach beyond one-off editing requests. Skills capture reusable knowledge and procedures, while agents can run as notebook-backed workflows on a schedule or through the API.

External agents can also access Deepnote projects through MCP, allowing compatible tools to inspect notebooks, work with connected resources, execute code, and review results under the permissions already configured for the workspace.

The underlying notebook remains an inspectable record of the work, whether the changes come from a person using the editor or an agent operating through another interface.

Omni Agent: conversational analytics over business data

Omni’s Analytics Agent gives business users a conversational way to query company data. Someone investigating a revenue decline can ask which customer segments contributed most, request a chart, and ask follow-up questions without starting in a dashboard or SQL editor.

For a team moving from Looker, this changes how people access the BI system while retaining shared business definitions. The agent uses the semantic model’s metrics, relationships, and permissions to interpret the request, and users can continue exploring the results in a workbook.

Omni also supports working with uploaded files. When a dataset isn't part of the selected model, CSV files can be brought into a workbook as queryable data. Its chat-based import workflow can prompt users to confirm this step before proceeding with the analysis.

For maintaining the semantic model itself, Omni provides a separate Modeling Agent. It can propose measures, dimensions, relationships, and Topics, which organize relevant fields into curated datasets. Changes can be handled through Sandbox mode for reviewing a group of proposed edits, Review mode for approving changes individually, or Auto mode for applying them directly.

Omni also supports reusable Agent Skills, AI Evals, and developer integrations such as MCP. These help teams maintain business definitions, standardize recurring tasks, and evaluate how agents use the available context.

Omni’s conversational experience centers on exploring business data through its BI model. Deepnote Agent can continue into the notebook’s implementation: changing data preparation, running statistical code, inspecting outputs, and updating the downstream analysis within the project.

Semantic layer and reusable business logic

Deepnote modules: business logic that can be executed

Deepnote lets teams publish notebooks as reusable modules. These modules can contain SQL queries, Python functions, data transformations, and configurable inputs.

Suppose our revenue calculation needs to account for refunds, discounts, and a specific transaction status. We can define that logic in a notebook, document the assumptions, and publish the relevant blocks as a module.

Other notebooks can import the module instead of copying and maintaining separate versions of the calculation.

When the source module changes, notebooks importing it can use the updated logic on their next execution. Deepnote Agent can also consult relevant modules when working on an analysis, helping it build on definitions the team has already established.

The benefit isn't limited to business metrics. Modules can package data preparation routines, feature engineering steps, or reusable functions for evaluating machine learning models.

We prefer this approach when the definition itself requires executable logic or when an analytical workflow needs to combine business context with transformations that go beyond a warehouse query.

Omni's governed semantic layer

The semantic layer is the strongest connection between Omni and the Looker approach to BI. Teams define measures, dimensions, relationships, and access rules centrally so that reports and AI queries can use consistent business definitions. For organizations leaving Looker, this preserves an established way of governing analytics.

If a finance team defines net revenue in the model, dashboards and AI queries can reference that definition rather than recreating the calculation independently. When the model changes, downstream content that references it uses the updated definition.

Omni also lets analysts develop definitions within a workbook before promoting them into the shared model. This gives teams room to explore while deciding which calculations should become reusable business metrics.

Omni's semantic model also supports AI-specific context, including reporting conventions, business terminology, sample queries, and instructions about how particular fields should be used. Teams can define this guidance at the model, Topic, or field level, helping the agent interpret questions using the organization's established definitions rather than relying on the database schema alone.

For the underlying semantic model, Omni supports Git-based development, branching, and review workflows. Its integrations with dbt, Snowflake Semantic Views, and Databricks Unity Catalog Metric Views also help teams reuse definitions maintained elsewhere in their data stack.

Deepnote modules and Omni's semantic layer aren't identical systems. Omni has more specialized controls for governing metrics across its BI surfaces. Deepnote's advantage is that reusable context can include the executable analysis itself, including Python logic, notebooks, and workflows that agents and teammates can inspect and run.

Compute, custom environments, and machine learning

This is one of the areas where the practical differences between Deepnote and Omni become most pronounced.

Deepnote: configurable environments for data science

Deepnote projects have their own execution environments, with configurable machines, a filesystem, and installed dependencies.

Teams can choose the resources an analysis needs:

• Compute: Use standard CPU machines for everyday work, larger instances for demanding calculations, or GPU hardware for compatible machine learning workloads.
• Environment: Install Python packages, work with files and secrets, access a terminal, and configure custom Docker images.

Consider a churn prediction workflow. After preparing historical customer data, we might train several models, compare their performance, and store the selected model for later predictions.

In Deepnote, we can keep that process in notebooks within the same project, reusing integrations and supporting files. Deepnote Agent can assist with the analysis, while a scheduled notebook can execute the prediction workflow against updated data.

The environment is configurable enough to accommodate specialized Python libraries, model dependencies, and computational requirements. We don't have to assume the analysis will fit inside the operations provided by a BI tool.

Omni: warehouse queries and a requeryable cache

Omni combines warehouse querying with a DuckDB-powered cache. When a follow-up request can be answered from cached results, eligible operations such as filtering, sorting, or reaggregating data can run without sending another query to the warehouse.

For example, after retrieving revenue by product and region, an analyst may be able to narrow the result to one region or summarize it at a higher level using the cached data. This can make interactive exploration more responsive and reduce repeated warehouse work.

That is a concrete reason to evaluate Omni when replacing Looker. Actual performance still depends on the queries, warehouse configuration, available cached data, and freshness requirements, so teams should compare representative workloads.

This query architecture serves a different purpose from Deepnote’s configurable execution environment. Deepnote lets teams install analytical libraries, manage project dependencies, and select CPU or GPU resources for custom computation.

If the requirement is responsive exploration of business metrics, Omni’s approach is relevant. If the work includes training a churn model, running a simulation, or maintaining a Python pipeline, we’d choose Deepnote.

Data apps, automation, and publishing results

Deepnote and Omni both let teams deliver more than a static report. Each can publish interactive experiences and automate recurring work, although the capabilities behind those outputs differ.

Deepnote data apps and scheduled workflows

Deepnote lets teams turn notebooks into interactive data apps, with selected outputs visible and the underlying code hidden.

A sales operations team could build a lead-scoring model, then publish an app where account managers select a territory and review the highest-priority leads. The app uses the notebook's existing logic rather than requiring a separate implementation of the model.

Because the analytical code remains in Deepnote, we can update the scoring method, review the changes, and publish the improved result from the same project.

Deepnote also supports scheduled notebook execution, background runs, and API-triggered workflows. These can refresh data, perform transformations, execute models, or produce updated outputs for other systems.

Each run creates a snapshot that teams can inspect later, including the code and outputs from that execution. This is useful when a recurring analysis produces an unexpected result and we need to understand what happened.

Deepnote's scheduled notebooks are available on paid plans, with hourly and custom scheduling on Enterprise. Standard scheduling currently supports one scheduled notebook per project, so teams with several independent schedules may need to organize them across projects.

Omni dashboards, apps, and AI Routines

Omni supports dashboards with filters, drill-downs, and visualizations built on its shared semantic model. Teams can share these reports and schedule deliveries through channels such as email and Slack.

AI Routines add another form of automation. Instead of sending the same dashboard on a schedule, Omni can execute a recurring natural-language request, analyze the latest results using governed business definitions, and deliver an updated explanation. For example, a sales manager could receive a weekly summary of pipeline changes and deals that need attention.

Omni also supports AI-generated apps. Users can describe an internal tool, and Omni generates an application that references data and metrics in its semantic model. These apps can support interactive controls, scenario analysis, and certain writeback workflows. They can also be embedded into other products, with Omni's access controls applied to the underlying data.

For a team replacing Looker, these capabilities extend the BI workflow into conversational summaries and interactive applications. The shared semantic model remains the foundation for accessing business data.

Deepnote is especially useful when the application grows out of an analytical project. A team can develop and review SQL and Python in notebooks, configure the required dependencies, and publish selected outputs and controls as a data app. We’d favor that approach when maintaining the underlying computation is a substantial part of the work.

Deepnote vs Omni pricing

Deepnote publishes its Free and Team pricing, while Omni uses a sales-led pricing process. The difference affects how easily teams can estimate their costs before starting an evaluation.

Deepnote pricing

Deepnote offers Free, Team, and Enterprise plans.

Free supports up to three editors and five projects, with Basic compute and limited AI usage. It provides a starting point for individuals and small teams developing notebooks together.

Team costs $39 per editor per month, billed annually. It includes unlimited viewers and notebooks, premium integrations, background execution, scheduled notebooks, more powerful machines, and monthly AI and compute credits.

Enterprise uses custom pricing and adds features such as permission groups, SSO and directory synchronization, audit logs, private Docker images, bring-your-own LLM support, and single-tenant deployment options.

Deepnote's pricing is based primarily on paid editor and admin seats rather than charging every person who reads an analysis. Teams can have a relatively small group maintaining notebooks while a larger audience views results and uses published apps.

See Deepnote pricing for the current plan details.

Omni pricing

Omni offers a free trial, but doesn't publish standard subscription rates on its main website.

Organizations request access and work with Omni to establish pricing based on their requirements. The commercial agreement may depend on factors such as users, analytics usage, embedded deployment, and other services or capabilities.

Because Omni's analysis runs against connected warehouse infrastructure, teams should also account for the compute costs associated with those queries.

For a meaningful comparison, we'd ask Omni to clarify the following in its quote:

• User and application access: The cost of analysts, business users, embedded viewers, and AI-powered experiences.
• Usage and deployment: AI consumption, warehouse costs, embedding requirements, and any additional support or deployment charges.

Deepnote's published Team pricing makes it easier to estimate the starting cost for a known number of editors. Omni requires a commercial quote before teams can establish a comparable figure.

Deepnote or Omni: which should you choose?

Choose Deepnote when your team needs AI to help develop and maintain analytical work: preparing data, investigating questions, writing custom code, running models, and turning the results into recurring workflows or applications. Its shared projects keep the data connections, execution environment, code, and outputs together as the work develops.

Omni is an option when the immediate goal is to replace Looker. It retains a familiar foundation of governed metrics and warehouse analytics while adding conversational access, flexible workbooks, and a requeryable cache. Teams can modernize how people ask questions without abandoning their established approach to BI.

The decision comes down to the work you need to support. If most requests are about exploring and distributing business metrics, Omni addresses that need directly. If those questions regularly lead to new analytical methods, Python dependencies, model development, or automated computation, we’d favor Deepnote.

For teams building beyond reporting, Deepnote provides room to carry the investigation through to an executable workflow.