Technology
Google Gemini Development — Multimodal AI on Vertex & Gemini API
Production Gemini integrations on Vertex AI and the Gemini API — multimodal, long-context, and grounded with Google Search.
What we build with Google Gemini
- Gemini Pro and Flash deployment on Vertex AI or Gemini API
- Multimodal pipelines: text, images, audio, video, and code
- Long-context reasoning over million-token inputs
- Grounding with Google Search and your private corpora
- Function calling, structured outputs, and tool use
- VPC-SC, CMEK, and region-pinned deployments for compliance
Why DiveScale
Built by engineers who ship Google Gemini in production
Gemini is the right pick when your data already lives on Google Cloud, when multimodal input dominates the workflow, or when grounded answers need to cite the open web. We ship Gemini integrations end-to-end on Vertex AI with the security posture enterprise procurement expects.
Our engineers handle the unglamorous parts: quota management, region pinning, VPC-SC, CMEK, IAM scoping, and rollback plans when a new Gemini version changes behavior on your golden dataset.
We benchmark Gemini against Claude and OpenAI on your data before recommending, and abstract the provider so the choice stays reversible.
Google Gemini use cases we deliver
How we deliver
Our Google Gemini delivery process
- 01
Discovery on GCP
We audit your Google Cloud setup, identify the workloads where Gemini wins, and pick between Vertex AI and the Gemini API.
- 02
Prototype + evals
Working prototype in 2 weeks with a golden dataset, latency budget, and cost model.
- 03
Enterprise hardening
VPC-SC, CMEK, region pinning, IAM, and quota management — production controls your security team needs.
- 04
Ship & operate
We deploy, monitor with Cloud Logging, and keep the system current as Google releases new Gemini versions.
Related technologies
OpenAI
Production-grade integrations with GPT-4o, GPT-4.1, o-series reasoning models, Realtime voice, embeddings, and the Assistants API.
Learn moreAnthropic (Claude)
Production builds on Claude Opus, Sonnet, and Haiku — long-context reasoning, tool use, prompt caching, and Computer Use agents.
Learn moreGoogle Cloud
GCP architecture, GKE, Cloud Run, BigQuery, and Vertex AI — production engineering for organizations leveraging Google’s data and AI strengths.
Learn moreLLMs
Production LLM engineering — model selection, RAG, fine-tuning, evals, guardrails, and the operational layer that keeps quality high.
Learn moreGoogle Gemini — Frequently Asked Questions
Vertex AI is the right choice when your data is already on GCP, when you need VPC-SC and CMEK, or when MLOps tools like Model Garden and pipelines matter. The Gemini API is faster to get started with for prototypes.
