senn-techAI Radar: Google Gemini
The current Gemini generation from Google is strong in two main areas: a very large context window and native multimodality (text, image, audio, and partially video in a single model). The combination — long context plus genuine multimodality in one model — is rare, and exactly why Gemini keeps its own position for certain use cases.
Where it fits
The large context window is genuinely useful: processing entire document collections or codebases in one go, without tedious chunking. For anyone already working in Google Workspace, the integration comes practically for free — and for agents that share state across many tool calls, the reach is a real edge over models with a conventional 128K window. In practice it means less engineering for retrieval pipelines and more confidence that the model actually holds the relevant passages.
Context Window and Architecture
The Gemini line now moves in the one-to-two-million-token range — enough to hold a complete project archive or an entire repository plus build logs in a single call. The price is memory: long contexts inflate the KV cache, and usable context correlates directly with VRAM demand and latency. For hosted calls you feel it as slower first-token time; for self-hosting you feel it as hardware cost. Multimodal here means a jointly trained model from the ground up, not image recognition bolted on afterward. That is useful for document workflows where text, scans, and tables belong together. Anyone processing handwritten notes, invoice scans, and emails in a single pipeline saves the separate OCR and layout step that native multimodality makes unnecessary. That saves concrete effort in the integration work.
License: Closed, With an Open Sibling
Gemini itself is closed and API-only — no weights, no self-hosting, no fine-tuning on your own data. Google serves the open side in parallel with the Gemma line, which is open-weight and therefore runnable on your own hardware, but noticeably smaller. If you need full control, you don't reach for Gemini but for Gemma — or one of the other open models.
The caveat
As with other US cloud models: the data leaves the premises and the EU. For the German-speaking SME with GDPR requirements, this is the decisive weighing point. Enterprise contracts and EU processing locations mitigate the risk but do not change the fundamental cloud character. Add to that platform lock-in: anyone who tightly couples business processes to Gemini and Workspace builds a dependency that is costly to undo later. A gateway with an open interface keeps the option open here, without having to give up Gemini's strengths.
Our view
Gemini is a powerful tool for multimodal and context-heavy tasks. We treat it like any cloud provider: useful for non-critical workloads, interchangeable via a gateway — and no substitute for proprietary models where data sovereignty matters. Where speed and multimodality are in demand, it has its place; where sensitive data is involved, the answer stays on-premise.
Further Reading
What is the real advantage of the large context window?+
Entire document collections or codebases can be processed in a single call without tedious chunking and retrieval pipelines. Anyone already working in Google Workspace gets the integration practically for free, and agents that share state across many tool calls benefit from the reach. The price is an inflated KV cache, meaning more VRAM and latency — usable, but not free.
Can you self-host or fine-tune Gemini?+
No. Gemini is closed and API-only — no weights, no self-hosting, no fine-tuning on your own data. Google serves the open side in parallel with the smaller Gemma line, which is open-weight and therefore runnable on your own hardware. If you need full model control, you do not reach for Gemini but for Gemma or another open model.
How do you handle platform lock-in to Google Workspace?+
We treat Gemini like any cloud provider: useful for non-critical workloads, interchangeable via a gateway with an open interface. Anyone who tightly couples business processes to Gemini and Workspace builds a dependency that is costly to undo later. Where sensitive data comes into play, the answer stays on-premise — speed and multimodality do not justify concessions on data sovereignty.