Gemini
Google's hosted multimodal models. Gemini is Google's family of multimodal language models, available through the Gemini API and Google Cloud Vertex AI. The models accept text, images, audio, video and documents, and support long context and tool calling. Teams use Gemini for document processing, multimodal search, assistants and data workflows inside Google Cloud.
When it is the right choice
- You already run on Google Cloud and want models inside the same account and controls
- You need to process images, video, audio or long documents in one model
- You want tight integration with BigQuery and other Google data services
- Not ideal when a self-hosted model is required for data control; consider Gemma or another open-weight model
How we use it
- Deploy Gemini-based services through Vertex AI with IAM, VPC controls and regional settings
- Build document and media pipelines that feed results into your existing data stores
- Monitor cost, latency and errors per feature in Cloud Monitoring or Grafana
- Evaluate model changes on your own data before switching versions
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Fine-Tuning or Prompting? A Decision Guide for Small TeamsWhen prompting, RAG or fine-tuning is the right way to adapt a language model, and what each one costs a small engineering team.Gemini: frequently asked
Should we use the Gemini API or Vertex AI?
The Gemini API is quick to start with. Vertex AI fits production on Google Cloud better because it uses your project's IAM, networking, regions and billing.
Can Gemini read PDFs and images directly?
Yes, Gemini accepts documents and images as input. For large volumes we still add a pipeline around it for retries, validation and storage of results.
Running Gemini, or deciding whether to? Tell us what you have; the first consultation is free.
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