Gemini
Multimodal intelligence natively embedded in the Google Cloud ecosystem.
Best forTeams on Google Workspace or Google Cloud, and any business whose real problem is sitting in a data warehouse rather than a document.
Gemini 4 Argon, generally available on Google Cloud since 13 October 2026, is Google's flagship reasoning model: it reads text, images, audio, video and PDFs in a single 1M-token context and is built for agents that plan and act. Its deep integration with Google Cloud — including BigQuery, Gemini Enterprise Agent Platform (formerly Vertex AI) and Workspace — makes Gemini the natural choice for organisations already invested in the Google stack.
What Gemini does in the tools you already own
- Google Sheets, and your Excel files
- In Sheets, Gemini writes the formula, builds the analysis and explains what it did. The question we get asked more often is whether it touches Excel, and the honest answer is that it works on Excel files rather than inside the Excel application. Upload an xlsx and Gemini will read it, analyse it and hand back a cleaned version. If you need AI in the Excel ribbon itself, that is Copilot. If you need the analysis done and do not care which application it happens in, this works.
- BigQuery
- This is the one that changes numbers rather than saving minutes. Gemini runs inference inside BigQuery, so you classify, score and enrich data without exporting it anywhere. For anyone with POPIA exposure, keeping the data in the warehouse instead of shipping it to an external endpoint is worth more than the convenience.
- Gmail and Docs
- Drafting, summarising and thread catch-up in the places people already work. The same category of value Copilot delivers on the Microsoft side, and the reason the choice usually follows whichever suite you already pay for.
- Gemini Enterprise Agent Platform (formerly Vertex AI)
- Where the actual automation gets built: agents that plan and act, grounded in your own data, with region and retention set explicitly. Everything we put into production for clients on Google runs here rather than on a consumer surface.
- Grounding with Search
- Answers can be grounded in live web results rather than bounded by a training cutoff. Genuinely useful for anything price-sensitive or regulatory, where a confidently stale answer is worse than no answer.
What makes Gemini stand out
Multimodal Understanding
Process and reason across text, images, audio, video, PDFs and code in a single model — eliminating the multi-pipeline setups competing models still require. Gemini 4 Argon takes all five input types into one 1M-token context, so a contract bundle, its scanned annexures and the recorded negotiation call can be analysed together.
Google Cloud Integration
Native integration with Gemini Enterprise Agent Platform (formerly Vertex AI), BigQuery ML, Gemini Enterprise and Google Workspace — reducing friction for teams already operating on Google infrastructure. Also available via AI Studio, Antigravity, Gemini CLI, and Android Studio.
Frontier Reasoning, Built for Agents
Gemini 4 Argon is Google's flagship reasoning model for complex software engineering, multi-step agents and hard analytical work, with thinking levels (low / medium / high) to balance cost, latency and depth per request. Google trained it to respect system boundaries and pause for confirmation before irreversible steps, and reports it cut indirect prompt-injection attack success to 0.7% on Gray Swan's adaptive benchmark — the property that matters most once an agent can act on your systems.
Grounding with Search
Gemini grounds responses in real-time web data via Google Search, and supports Grounding with Google Maps, File Search, Code Execution, and URL Context as built-in tools — keeping answers current and verifiable rather than bounded by training cutoffs.
What we build with Gemini
Document & Image Analysis
Analyse invoices, forms, medical images, or engineering drawings alongside their associated text with high-fidelity multimodal reasoning — enabling end-to-end document automation at scale.
BigQuery ML Integration
Run Gemini-powered inference directly within BigQuery — scoring, classifying, and enriching data without moving it out of your warehouse.
Workspace Automation
Automate report generation, email drafting, meeting summaries, and document creation within Google Workspace using Gemini-powered agents, now extended to NotebookLM and Gemini in Chrome.
Agent Platform Pipelines
Build end-to-end ML and agent pipelines on Gemini Enterprise Agent Platform (formerly Vertex AI) with Gemini 4 Argon as the reasoning layer — from data preparation to deployment to monitoring, with agentic tooling built in via Google Antigravity.
Gemini vs Microsoft Copilot
| Gemini | Microsoft Copilot | |
|---|---|---|
| Where it lives | Inside Docs, Sheets, Gmail, Meet and BigQuery | Inside Word, Excel, Outlook, Teams and SharePoint |
| Strongest use | Analysis and inference where the data already sits | Assistance where the documents already sit |
| Spreadsheets | Native in Sheets; reads and returns Excel files | Native inside the Excel application itself |
| SA data residency | Data can sit in Johannesburg (africa-south1); Gemini models are not offered there | South Africa datacentre regions available for the tenant |
| The deciding question | Is your problem in the warehouse or in the documents? | Is your problem in the warehouse or in the documents? |
What it costs, and where your data sits
How it licences
- Pricing model
- Two lines again. Gemini in Workspace is per user per month, either bundled into the Workspace tier or added on. Anything built on Agent Platform (formerly Vertex AI) is consumption-based, priced per million tokens. Gemini 4 Argon has introductory pricing of USD 2 per million input tokens and USD 10 per million output tokens until 30 November 2026, rising to USD 4 and USD 20 after that.
- Commitment
- Workspace follows your existing annual agreement. Agent Platform is pay-as-you-go.
- What this means in rand
- Because Google has moved Gemini into higher Workspace tiers over time, some businesses are already paying for it without having switched it on. Worth checking your current tier before buying anything: we have started more than one engagement by finding the licence was already there.
POPIA and data residency
Where it processes
Data in Johannesburg (africa-south1); Gemini 4 Argon runs on the global endpoint or US/EU multi-regions
Google Cloud runs a Johannesburg region, so your warehouse, storage and pipelines can stay in the country. The model is a different matter, and this is the qualifier most pages skip: Gemini 4 Argon is offered through the global endpoint and the US and EU multi-regions, and Google's location tables do not list Gemini models in africa-south1 at all. The global endpoint makes no promise about where a request is processed. So plan for the model call to cross the border, which POPIA section 72 permits with comparable protection, consent or contractual cover, and design so that as little personal information as possible makes the trip. The strongest argument on this platform is architectural: keep the records in BigQuery in africa-south1 and send the model only the redacted fields a step actually needs. Availability changes, so we confirm it at design time.
Why take our word for it
FICA compliance automation on Vertex AI
We automated regulatory onboarding checks for a South African neo bank on Google Vertex AI and BigQuery, with a daily aggregation pipeline and an audit trail of every automated decision kept for examination. Regulated work, in production.
Cloud data architecture for a neo bank
A full data estate on Google Cloud for a South African bank. When we recommend Gemini it is on the back of having built the warehouse underneath it, which is usually where the real problem turns out to be.
Gemini questions
Ready to put Gemini to work?
We'll help you identify where Gemini can create the most impact — and build it the right way, the first time.