AI for Google Sheets: what Gemini can’t do

Gemini comes free with your Workspace plan. That makes it the first thing you'll try — and the reason it's worth knowing exactly where it stops.

By the Quiriz Team · Published August 17, 2026 · Competitor features verified August 2026

Let's start where most comparison pages won't: if your data is one tab and a few hundred rows, use Gemini. It's already switched on, it costs nothing extra, and it sits in the same tab as your numbers. We'd be wasting your time arguing otherwise.

This page is about what happens after that. When the data is eleven exports from five systems, when the answer has to match last month's, and when six other people need it too — that's a different job, and it's the one Quiriz is built for.

The short version

	Quiriz	Gemini in Sheets
Rows it can handle reliably	Full dataset — queried in a database	Struggles past a few hundred
Reach your other files	100+ stored datasets, joined	Cannot access your Drive files
Same number every cycle	Governed metrics compiled to SQL	Re-derives from scratch each run
Getting external exports in	Folder upload, Drive/OneDrive sync, email-in	Manual import, every cycle
Ask from Slack	Yes — answers from your datasets	No Slack surface
Works in Excel too	Yes	Sheets only
Cost	Per workspace, unlimited asking	Included with Workspace — Gemini wins this
In-sheet formatting, pivots, dropdowns	Not our job	Excellent — Gemini wins this
Quick question on a small sheet	Works	Faster to reach — Gemini wins this

1. The row ceiling

This is the one to check first, because it decides whether the rest of the page matters to you. Gemini in Sheets degrades past a few hundred rows. Users report lag and failures in the thousands. Past what fits in a single session, summaries become partial and data gets truncated — per-row analysis stops being feasible.

So the useful question isn't "is Gemini good?" It's: how many rows is your sales export? If the answer is five figures, Gemini isn't the tool for the arithmetic — no matter how good it is at everything else.

Quiriz doesn't read rows into a model at all. Your question is compiled into a SQL query that runs against the whole dataset in a database. Four hundred thousand rows is a query, not a reading exercise.

2. A table that foots perfectly can still be wrong

Here's a real Gemini output — contribution margin by product category, asked of a 2025 sales dataset.

Gemini's answer

Category	Units	Net Sales	COGS	Contribution Margin	Margin %
Accessories	1,500	$38,213.59	$15,330.74	$22,882.85	59.88%
Bottoms	2,139	$116,576.80	$52,512.94	$64,063.86	54.95%
Dresses	468	$38,274.55	$17,941.14	$20,333.41	53.13%
Outerwear	570	$68,601.06	$27,960.54	$40,640.52	59.24%
Tops	2,314	$116,944.70	$50,502.47	$66,442.23	56.82%
Total	6,991	$378,610.71	$164,247.83	$214,362.88	56.62%

We checked every cell. The arithmetic is right: each margin is sales minus cost, each percentage is correct to two decimals, the columns foot. It looks authoritative because it is — arithmetically.

And it still can't be used for a decision, because the output doesn't tell you:

What "contribution margin" means here. Contribution margin is revenue less all variable costs — COGS plus payment processing, shipping, pick-and-pack, commissions. This is sales minus COGS, which is gross margin. Price a category off a "59.88% contribution margin" that's actually gross margin and the decision is wrong while every cell is right.
What "net sales" means. Gross less returns? Less discounts? Including shipping revenue? Tax-exclusive? Five reasonable definitions, five different tables, all footing perfectly.
Whether any rows were dropped. If some orders have a blank category, a join silently excludes them — and the totals still foot. Nothing here tells you whether 6,991 is all of them.
Which date field, which year basis, and what happened to refunds and cancellations.

Ask the same question next month and any of those can resolve differently, because the analysis is rebuilt from scratch every run. You'd get a second table that also foots perfectly, and nothing would flag the change.

3. What a governed answer looks like instead

Quiriz answers from a defined metric. "Net sales" means what your team agreed it means, once, and the definition travels with the answer — so the caveats arrive with the number instead of living in someone's head.

Quiriz answering in a Slack thread: asked for total Shopify sales in 2025, it replies with $349,874.55 net and lists product sales after discounts, refunds deducted, that shipping fees and sales tax are excluded, and that paid and refunded orders dated in 2025 are included — The same answer, defined. Quiriz states what's included and excluded — after discounts, refunds deducted, shipping and tax excluded — without being asked. That's the difference between a number and a number you can defend.

And because the definition is compiled rather than re-invented, the number is the same next month, and the same for whoever asks it.

4. Getting your data in — and keeping it current

Gemini has no dataset, no ingestion, and no refresh. It reads what's already in the sheet in front of it and cannot reach your Drive files. Every export has to be walked in by hand: download it, import it into Sheets, get it into one tab, consolidate anything that spans files — and repeat the whole thing next cycle.

Quiriz stores datasets. Point it at a folder and bulk upload. Connect Google Drive or OneDrive and let updates pull automatically. Email a file in. Push a sheet straight from the add-on. Mapping and type detection run on their own, including the parts that usually break AI tools — merged cells, multi-row headers, totals rows, leading zeros, dates stored as text.

Quiriz relationship view showing four HubSpot datasets connected by detected join keys: sales pipeline with 180 rows, call logs with 583 rows, CRM contacts with 180 rows and CRM revenue and lifecycle with 90 rows, joined on hubspot_owner_id and hs_analytics_source — Quiriz detects how your datasets relate and joins across them. Gemini answers about the tab you're looking at — these would be four separate imports it couldn't connect.

5. Everyone asks — from Slack, Sheets, or Excel

Gemini is Sheets-only and per-seat by tier. Quiriz answers wherever the question gets asked: in a Google Sheets cell, in an Excel cell, or in the Slack channel where someone actually wondered aloud. And the people asking don't need a seat — you pay for your data, not your headcount.

Where Gemini genuinely wins — including on price

Gemini is cheaper than us, and we're not going to dress that up. It's included with your Workspace plan, the monthly allowance is per user and generous enough that most people never reach it, and at the moment you decide it costs nothing extra. Quiriz costs money. If price is the deciding factor, this is a short conversation.

It's also excellent at the things it's designed for: pivots, dropdowns, conditional formatting, formula help, and building Slides decks from what's in front of it. For a quick question on a small sheet, it's already open and we're not.

One caveat worth knowing if you're on the entry tier: Business Starter only gets limited Gemini. Full Gemini starts at Business Standard, $18/user/month — so for Starter customers, "included" actually costs about $10 more per person per month.

The question this page is really asking isn't which is cheaper. It's whether a number you can define, audit, and reproduce — on a dataset bigger than a few hundred rows — is worth a workspace fee.

You probably shouldn't choose. Use both.

The split between them is clean, and it follows one rule: a language model shouldn't be the thing that computes the number.

Quiriz — the number

Ingest messy exports from many systems
Join across datasets
Governed metric definitions
Aggregate over full row counts
Per-step rows for audit

Gemini — the presentation

Pivots, dropdowns, conditional formatting
Charts and in-sheet layout
Slides decks and Docs write-ups
Narrative and interpretation
Formula help and sheet productivity

Concretely: don't hand Gemini four hundred thousand rows. Quiriz runs the aggregation and writes twelve rows — one per month — into your sheet. Gemini then charts it, formats it, and builds the deck, working comfortably inside the range where it's genuinely good. That's not a workaround; it's the right use of a tool with a documented row ceiling. Let it present, not compute.

It also quietly solves the "can't reach your Drive files" problem: Quiriz puts the answer in the sheet Gemini is already looking at.

And it works in reverse. When Gemini or a teammate reshapes or enriches that data, push it back to Quiriz from the add-on. It becomes a governed dataset again — refreshable, scoped by team and project, and askable from Excel and Slack by people who were never in that spreadsheet. Otherwise a good piece of analysis stays as one person's tab, at one moment in time, with no lineage.

Which one is right for you

Stick with Gemini if…

Your data is one sheet, a few hundred rows
You want formula help, pivots, formatting
Everything lives in Drive and nobody needs answers elsewhere
You ask occasionally, well inside the cap

Add Quiriz if…

Data arrives as exports from several systems
The same report goes out every cycle and must match
Several people need answers, most only occasionally
Answers belong in Excel or Slack, not just Sheets
Different people should see different data

Also compare: ChatGPT for data analysis · Claude for Excel · Microsoft 365 Copilot · AI in Excel: the full guide

Try it on your own data. No signup — upload a messy export and ask it something.

Try with your data → Get the Sheets add-on See the Slack demo

Gemini features verified August 2026. Google ships quickly — if something here is out of date, tell us and we'll correct it.

Related: AI Pricing Comparison 2026 — verified per-seat prices for Copilot, ChatGPT, Claude and Gemini and what a 10-person team really pays. AI Hallucination Statistics 2026 — why the same model scores 0.7% or 94% depending on the test.