The Google Sheets versus Excel debate has been running for over a decade, and for most of that time the answer was simple. If you needed serious analytical power, you used Excel. If you needed easy collaboration, you used Google Sheets. The two tools occupied different ends of the spectrum and the choice between them was mostly determined by what your team already used.
That clarity is gone in 2026. Google Sheets has closed meaningful gaps in formula capability and data handling. Excel has built genuine real-time collaboration into its cloud version. Both platforms have added AI features that change the day-to-day experience of doing analytical work. And the proliferation of data tools that connect to both platforms means the choice of spreadsheet is now entangled with questions about the broader stack an analyst works in.
This guide makes the comparison honestly, covers what has actually changed recently, and gives you a clear framework for deciding which tool belongs in your workflow depending on what kind of analytical work you actually do.
Where They Stand in 2026
Excel remains the more powerful analytical tool by a meaningful margin when the comparison is about raw capability. The formula library is deeper, the data handling limits are higher, the statistical functions are more comprehensive, and the integration with Power Query, Power Pivot, and the broader Microsoft data stack gives Excel a level of analytical infrastructure that Google Sheets does not match.
Google Sheets has spent the last several years closing gaps that used to make it feel like a clearly inferior product for analytical work. Named functions, which let you define and reuse custom formula logic across a workbook the way you would define a function in a programming language, arrived in Google Sheets in 2022 and meaningfully changed what complex formula work looks like in the platform. The LAMBDA equivalent, array formula improvements, and expanded connector ecosystem have all moved Google Sheets closer to Excel on the capability axis.
The honest summary is that Google Sheets is now good enough for a wider range of analytical work than it used to be, but Excel is still better for the analytical tasks that push the limits of what spreadsheets can do. The question for most analysts is not which tool is more powerful in absolute terms but which tool is powerful enough for the work they actually do while being the better fit for how they work.
Performance and Data Size Limits
This is where the gap between the two tools is most concrete and most consequential for analysts working with real datasets.
Excel handles up to approximately one million rows per worksheet, specifically one million forty-eight thousand five hundred and seventy-six rows, with performance that stays workable on modern hardware up to several hundred thousand rows for most operations. With Power Query handling data transformation outside the grid and Power Pivot running in-memory column-store compression, Excel can work with datasets that run into tens of millions of rows without loading everything into the grid at once.
Google Sheets has a cell limit of ten million cells per spreadsheet regardless of how those cells are distributed across sheets. On a single sheet, that translates to roughly ten million cells, but performance degrades noticeably well before that limit is reached. Sheets with more than a few hundred thousand rows of data with active formulas across many columns become slow in ways that affect daily usability. Complex array formulas on large datasets can take seconds to calculate where the same operation in Excel is near-instantaneous.
For analysts whose datasets fit comfortably within a few hundred thousand rows and whose formula complexity is moderate, this performance gap is not a practical problem. For analysts who regularly work with datasets at the upper end of spreadsheet capacity or who build complex formula models with many interdependent calculations, the performance difference is real and affects how quickly work gets done.
The workaround for Google Sheets on large datasets is BigQuery integration through the Connected Sheets feature, which allows a Google Sheet to query a BigQuery dataset directly without importing the data into the grid. This is a genuinely useful feature that extends Google Sheets’ practical data capacity significantly for organizations already running BigQuery. It does not eliminate the performance gap but it provides an architectural path around it for teams in the Google ecosystem.
Formula Capability and Depth
Excel’s formula library has been building for four decades and it shows. The statistical function set in Excel covers distributions, hypothesis tests, regression analysis, and time series functions at a depth that Google Sheets does not fully match. The engineering functions, the financial functions, the database functions that operate on structured ranges, all of these are more comprehensive in Excel than in Google Sheets.
The dynamic array revolution that Excel introduced with functions like FILTER, SORT, UNIQUE, SEQUENCE, and XLOOKUP has now been replicated in Google Sheets, which added its own versions of these functions. Both platforms support the core dynamic array workflow where a single formula spills results across a range automatically. The implementation differences are subtle enough that analysts fluent in one platform’s array functions can adapt to the other without significant relearning.
Where Excel still leads on formulas is in the combination of LAMBDA and the helper functions that come with it. LET, LAMBDA, MAP, REDUCE, SCAN, MAKEARRAY, and BYROW collectively make it possible to write genuinely reusable, readable, and maintainable formula logic in Excel that approaches what you would write in a scripting language. Google Sheets has its own LAMBDA implementation but the full ecosystem of helper functions around it is thinner and some behaviors differ in ways that require adjustment.
Power Query, which is Excel-only, deserves separate mention because it is not just a formula feature. It is a full data transformation interface that sits outside the grid and handles data cleaning, reshaping, joining, and loading at a scale and with a user experience that has no equivalent in Google Sheets. For analysts who spend significant time preparing data before analysis, Power Query is one of the most productivity-significant features in the Microsoft ecosystem. Google Sheets has no direct equivalent. The closest options are either writing Apps Script to transform data programmatically or using a third-party connector to move data through a transformation step before it reaches the sheet.
Collaboration and Real-Time Sharing
This used to be Google Sheets’ clearest advantage and it is now the most complicated part of the comparison.
Google Sheets pioneered real-time collaborative editing and it remains the smoother experience. Multiple users editing the same sheet simultaneously, seeing each other’s cursors and changes in real time, with automatic version history that lets you restore any previous state, is the experience Google Sheets was built around from the beginning. The sharing model is simple. A link with permission settings covers most sharing scenarios without requiring the recipient to have a specific account type or software installation.
Excel has closed this gap significantly through Microsoft 365 with files stored in OneDrive or SharePoint. Real-time co-authoring in Excel now works and works reliably in a way that it did not several years ago. The experience is somewhat behind Google Sheets in fluidity, particularly when multiple users are editing the same cell range simultaneously, but for most collaborative use cases it is functional.
The practical difference in 2026 is organizational rather than technical. Teams that primarily work in a Microsoft 365 environment with files in SharePoint will find Excel collaboration natural because it integrates with the tools they already use for document sharing, team communication, and access management. Teams that work primarily in Google Workspace will find Google Sheets collaboration natural for the same reason. The collaboration experience of each tool is better when it is embedded in its own ecosystem than when it is used in isolation.
Where Google Sheets still holds a genuine edge is in sharing with external collaborators. Sharing a Google Sheet with someone outside your organization requires only that they have a Google account, which most people do, and a link with appropriate permissions. Sharing an Excel file stored in SharePoint with external collaborators involves permission management that is more complex to configure correctly, particularly for organizations with strict security policies about external access.
AI Features: Copilot vs Gemini in Sheets
Both platforms added AI assistants in the past two years and both have improved substantially since their initial launches. The comparison here matters for analysts because AI features have moved from novelty to genuine workflow tools for specific tasks.
Microsoft Copilot in Excel, available through Microsoft 365 subscriptions with the Copilot add-on, remains the more capable AI integration for analytical work specifically because it can see and reason about the structure of your spreadsheet directly. You can ask Copilot to generate a formula based on your actual column layout, create a pivot table from your data with a natural language description of what you want to summarize, identify anomalies in a dataset, or explain what a complex formula does. The in-context awareness means the output is specific to your file rather than a generic template you need to adapt.
Google’s Gemini integration in Google Sheets, available through Google Workspace plans with the Gemini add-on, has improved significantly and covers similar ground. Formula generation, data analysis, chart creation from natural language descriptions, and data cleaning suggestions are all within its scope. The quality of formula generation from Gemini in Sheets has caught up to Copilot in Excel for most common formula types.
The difference that remains is depth of integration. Copilot in Excel feels like a feature built into the application’s core workflow. Gemini in Sheets still feels somewhat more like an add-on that sits alongside the spreadsheet rather than inside it. That distinction matters for how naturally analysts incorporate AI assistance into their work rather than treating it as a separate step.
For analysts whose primary AI use case in spreadsheets is formula generation and explanation, both tools now handle this well. For analysts who want AI to actively participate in analysis, suggest insights from data, and generate structured outputs like pivot tables and charts through conversation, Copilot in Excel currently provides a more integrated experience.
Automation and Scripting
Excel’s automation layer is VBA, a scripting language that has been part of Excel since 1993 and that remains widely used despite its age. VBA can automate almost anything Excel can do manually, from formatting operations to complex data transformations to generating reports across multiple sheets. The limitation of VBA is that it runs locally, requires the desktop application, and does not work in Excel for the web. For analysts who work exclusively in the browser-based version of Excel, VBA automation is not available.
Excel also supports Office Scripts, a newer JavaScript-based scripting platform that works in Excel for the web and can be triggered through Power Automate. Office Scripts fills the automation gap for browser-based Excel workflows and is the direction Microsoft is moving for modern Excel automation.
Google Sheets uses Apps Script, a JavaScript-based scripting environment that runs in the cloud and is deeply integrated with the Google Workspace ecosystem. Apps Script can automate Google Sheets operations, connect to other Google Workspace tools like Gmail, Calendar, and Drive, call external APIs, and be triggered on schedules or by events like form submissions. For analysts who need automation that spans multiple Google Workspace tools, Apps Script is the more flexible environment because it operates across the full ecosystem rather than within a single application.
The practical comparison depends on what you need to automate. Pure spreadsheet automation for formatting, data manipulation, and report generation is comparably capable in both platforms. Automation that integrates with other tools in your ecosystem favors whichever platform’s ecosystem you primarily work in. Excel and Power Automate together cover complex workflow automation in the Microsoft stack. Google Sheets and Apps Script together cover the same in the Google stack.
Data Connectivity and Integration
Excel’s data connectivity story runs through Power Query, which can connect to hundreds of data sources including databases, APIs, cloud services, other Office files, web pages, and flat files of every common format. The transformation capability within Power Query means data can be cleaned and reshaped before it reaches the grid, which keeps the analytical layer of the spreadsheet separate from the data preparation layer.
Google Sheets connects to external data through a combination of built-in connectors, the IMPORTDATA, IMPORTHTML, IMPORTFEED, and IMPORTRANGE functions for web-based data, the BigQuery connected sheets integration for large-scale data, and third-party connectors through tools like Zapier, Make, and various native integrations. The built-in import functions are simpler to use than Power Query for straightforward cases but significantly less capable for complex data transformation requirements.
For analysts who work with databases, the comparison depends on the specific database and the depth of transformation needed. Both platforms can pull data from SQL databases. Excel through Power Query provides a more powerful transformation layer between the database and the analysis. Google Sheets through its native connectors or third-party tools provides a simpler path for straightforward data retrieval.
The BigQuery integration deserves emphasis again because it represents a genuinely different capability tier for Google Sheets users in organizations running BigQuery. Analysts who can query petabyte-scale data in BigQuery and surface the results in a Google Sheet for further analysis and sharing are operating at a scale that has no direct equivalent in base Excel without connecting to a separate data warehouse.
Pricing and Accessibility
Google Sheets is free for personal use through a Google account with fifteen gigabytes of storage shared across Google Drive. Google Workspace plans for business users start at six dollars per user per month for the Business Starter tier and increase through Business Standard, Business Plus, and Enterprise tiers with progressively more storage, administrative controls, and features. Gemini AI features require a Workspace plan with the Gemini add-on, which adds cost above the base plan pricing.
Excel is available through Microsoft 365 Personal at approximately seventy dollars per year, Microsoft 365 Family at approximately one hundred dollars per year covering up to six users, and Microsoft 365 Business plans starting at approximately six dollars per user per month for Business Basic through higher tiers for Business Standard and Business Premium. Copilot in Excel requires an additional Copilot for Microsoft 365 add-on at thirty dollars per user per month on top of qualifying business plans.
For individual analysts, Google Sheets being free for personal use is a meaningful practical advantage. For organizational deployments, the pricing comparison depends on what else the organization is paying for in each ecosystem. Most organizations are already paying for either Microsoft 365 or Google Workspace, which means the marginal cost of the spreadsheet tool itself is effectively zero because it is included in an existing subscription.
The AI add-on pricing is where the cost comparison gets significant. Copilot for Microsoft 365 at thirty dollars per user per month is a substantial addition to an existing Microsoft 365 subscription. Google’s Gemini add-on pricing is lower for comparable plans. For organizations evaluating whether to invest in AI-enhanced spreadsheet capability, the cost difference between the two platforms’ AI add-ons is worth factoring into the decision.
Which Tool Belongs in Your Workflow
The answer depends almost entirely on three things: what kind of analytical work you do, what ecosystem your team works in, and what data infrastructure you connect to.
If your analytical work involves large datasets, complex statistical models, multi-step data transformation pipelines, or Power Pivot data models that aggregate millions of rows, Excel is the better tool. The performance headroom, the depth of the formula library, Power Query, and the broader Microsoft analytics stack give it capabilities that Google Sheets cannot match for this kind of work.
If your analytical work involves moderate-sized datasets, frequent collaboration with colleagues or external stakeholders, regular sharing of live data with people who need to view or edit it in a browser, or integration with other Google Workspace tools, Google Sheets is the better fit. The collaboration experience is smoother, the sharing model is simpler, and the Google ecosystem integration is more cohesive.
If your organization is already standardized on one platform, the answer is almost certainly to use that platform unless you have a specific analytical requirement that the standard platform genuinely cannot meet. The productivity cost of context-switching between platforms and maintaining fluency in both outweighs the marginal capability advantage of using the less familiar tool for a specific task.
If you are an individual analyst building portfolio projects or developing skills without an organizational constraint, learn Excel first because its analytical depth is greater and Excel proficiency is a more consistently valued skill in data analyst job requirements. Then learn Google Sheets because its collaboration model and Google ecosystem integration appear frequently in smaller organizations and startups and knowing both is more valuable than knowing one deeply.
FAQs
Is Google Sheets good enough for data analysis in 2026?
Google Sheets is good enough for the majority of data analysis work that most analysts do day to day. Datasets up to a few hundred thousand rows, standard formula-based analysis, pivot tables, charts, and collaborative review of findings all work well in Google Sheets. The tool falls short of Excel for large dataset performance, complex statistical modeling, multi-step data transformation, and the depth of formula capability that advanced analytical work requires. For analysts whose work stays within those limits, Google Sheets is a fully capable tool. For analysts who regularly push those limits, Excel remains the stronger choice.
What can Excel do that Google Sheets cannot?
The most significant capabilities that Excel has and Google Sheets lacks are Power Query for data transformation, Power Pivot for in-memory data modeling across very large datasets, the full VBA automation environment for desktop-based scripting, a deeper statistical and engineering function library, and performance that stays workable at row counts approaching one million. For analysts working within moderate data volumes and formula complexity, most of these gaps do not affect day-to-day work. For analysts working at the upper end of spreadsheet capability, they are meaningful limitations.
Is Microsoft Copilot in Excel better than Gemini in Google Sheets?
For analytical work specifically, Copilot in Excel currently provides a more integrated experience because it can see and reason about your spreadsheet structure directly, generating formulas and pivot tables that are specific to your actual data layout. Gemini in Google Sheets has improved significantly and covers similar ground for most common AI-assisted tasks. The gap between them has narrowed over the past year. Copilot still leads for analysts who want AI to actively participate in data analysis and insight generation rather than primarily assisting with formula writing.
Which is better for collaboration, Google Sheets or Excel?
Google Sheets provides a smoother real-time collaboration experience and a simpler sharing model, particularly for sharing with external collaborators outside your organization. Excel through Microsoft 365 with OneDrive or SharePoint now supports real-time co-authoring that is functional for most collaborative use cases and integrates naturally with organizations already standardized on Microsoft 365. The practical advantage depends on your organizational context. Google Sheets is the better default for collaboration in mixed or external sharing scenarios. Excel collaboration is better when the entire team is inside a Microsoft 365 environment.
Should I learn Google Sheets or Excel first?
Learn Excel first if your goal is data analyst employability. Excel proficiency appears more frequently in data analyst job requirements and the analytical depth you develop learning Excel transfers to Google Sheets more easily than the reverse. Learn Google Sheets alongside Excel if you work in or plan to work in organizations that run Google Workspace, which is common in startups, technology companies, and education. Proficiency in both is more valuable than deep proficiency in one for most analyst roles in 2026.