Google offers a family of models with the Gemini API for text generation, reasoning, coding, agent workflows, tool use, multimodal prompts, and document-centric processing.
For current API LLMs, Gemini 3.1 Pro Preview, Gemini 3 Flash Preview, Gemini 3.1 Flash-Lite Preview, Gemini 2.5 Pro, Gemini 2.5 Flash, and Gemini 2.5 Flash-Lite are particularly relevant. Older Gemini 2.0 Flash variants are still available, but are already marked as deprecated.
Google Gemini API
LLM “AI for every developer”
Origin: USA ⓘ Global parent company: Google LLC, 1600 Amphitheatre Parkway, Mountain View, California 94043, United States. For EMEA Gemini API Paid Services: Google Cloud EMEA Limited, 70 Sir John Rogerson’s Quay, Dublin 2, Ireland.
Batch / Context Caching / Priority / Flex Additional billing and operational options for controlling cost, latency, and throughput.
Vertex AI / Google Cloud Enterprise-oriented operation with Cloud DPA, IAM, regional endpoints, data residency, monitoring, and zero-data-retention configurations.
Grounding / Tuning / Embeddings / Live API Advanced features for search, context enrichment, model customization, vector search, real-time audio, and multimodal applications.
Target audience
The Gemini API is aimed primarily at developers, start-ups, agency teams, internal automation and product teams, as well as companies that want to build their own LLM-powered applications. Google positions Gemini very clearly for API integration, app building, coding support, agentic workflows, and multimodal applications. Thanks to the tiering from Flash-Lite to Pro, the platform is suitable both for cost-sensitive mass processing and for more demanding reasoning and coding use cases.
Outstanding features
The most striking strengths lie in the combination of multimodality, agent/grounding capabilities, long context windows, tiered pricing, and close integration with Google’s developer and cloud ecosystem. Particularly interesting is the current three-part split: Gemini 3.1 Pro Preview for maximum intelligence and difficult tasks, Gemini 3 Flash Preview for fast, high-quality all-round workloads, and Gemini 3.1 Flash-Lite Preview for high volumes, translation, and simple data processing. Alongside these, the 2.5 models remain the more stable alternatives for everyday API use.
Key application areas
Gemini is particularly well suited for coding, agent workflows, document processing, translation, classification/extraction, internal knowledge systems, chatbots, research-supported applications, and multimodal business workflows. Google’s Vertex AI introduction cites, among other things, advanced reasoning, multiturn chat, code generation, and multimodal prompts; the model descriptions specifically add translation, simple data processing, high-volume agentic tasks, and complex coding/reasoning use cases.
Usage & notes
Operationally, you typically start with Google AI Studio and then migrate production applications to the Gemini API or, where higher governance requirements apply, to Vertex AI. For new projects, it makes sense to consciously weigh Preview models against Stable models: Preview models are often more powerful or more up to date, but they can still change. From a data protection perspective, you should also distinguish very carefully between Free/Unpaid, Paid, and Vertex AI Enterprise, because this results in relevant differences in product improvement, logging, DPA, and regional processing.
| Target audience | Assessment |
|---|---|
| Developers / product teams | Very suitable – for multimodal apps with text, image, video, audio, tool use, embeddings, and live/voice features. |
| Google Cloud teams | Very suitable – especially if Google Cloud, Vertex AI, Workspace, or BigQuery are already in use. |
| SaaS providers / startups | Suitable – thanks to the Free Tier, Paid Tier, wide model variety, and easy API integration. |
| SMEs / enterprises | Suitable to very suitable – especially via Paid Tier or Vertex AI with DPA, data controls, and regional options. |
| EU companies | Conditionally to well suited – Paid Services and Vertex AI setups are significantly easier to control than pure Free Tier usage. |
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Gemini 3.1 Pro Preview
Best suited for:
Complex reasoning, difficult coding tasks, agentic workflows with precise tool use, demanding multimodal analysis
Gemini 3 Flash Preview
Best suited for:
Fast, high-quality all-round apps, agentic work, multimodal understanding, coding-adjacent production systems with a good price-performance ratio
Gemini 3.1 Flash-Lite Preview
Best suited for:
High-volume agents, simple extraction, translation, extremely low latency, cheap production pipelines
Gemini 2.5 Pro
Best suited for:
Complex problems in code, mathematics, STEM, analysis of large datasets, codebases, and documents with long context
Gemini 2.5 Flash
Best suited for:
Productive standard applications, large processing loads, low latency, agentic use cases when reasoning is needed
Gemini 2.5 Flash-Lite
Best suited for:
Classification, simple data extraction, routing, very inexpensive fast pipelines, cost-critical standard tasks
Gemini 2.0 Flash
Best suited for:
Only for existing migrations or legacy setups that have not yet been switched over
Gemini 2.0 Flash-Lite
Best suited for:
Only for legacy workloads with an extremely simple scope
Hosting & Data
1) On-prem / local hosting
Meaning: The company operates the solution on its own hardware or within its own infrastructure. In the strictest sense, not only the application runs locally, but ideally the model as well.
2) Private cloud / data center
Meaning: The solution runs in a dedicated or more clearly separated cloud environment, often with a hosting provider or hyperscaler, but in a German data center or in a particularly controlled environment.
3) EU SaaS / managed
Meaning: The provider operates the solution itself as a service. The company uses the tool as a ready-made cloud service, ideally with EU data residency.
4) Hybrid
Meaning: One part of the processing remains internal / local / in a private cloud, while another part runs in an external cloud or EU SaaS.
5) AVV / DPA
Meaning: This is the data processing agreement or Data Processing Addendum. It governs that the provider processes personal data on behalf of the customer and is bound by the customer's instructions.
6) No training
Meaning: The provider does not use your prompts, uploads, attachments, chat histories, or outputs for training or improving the general model — ideally excluded by contract.
7) Open-source / transparency path
Meaning: There is a path toward greater technical transparency and sovereignty, for example through:
- open models
- documented components
- self-hostable parts
- traceable architecture
- export / switching options
| On-prem / local hosting | ❓ |
| Private cloud / data center | ⚠️ |
| EU SaaS / Managed | ⚠️ |
| Hybrid | ⚠️ |
| DPA / AVV | ✅ |
| No training on customer data | ⚠️ |
| Open source / transparency path | ❓ |
Overall assessment of hosting & data:
The Gemini API is a managed cloud API service for multimodal LLM applications with text, image, video, audio, embeddings, Live API, TTS, image generation, tool use, grounding, context caching, and batch processing. Local on-premises hosting of the Gemini models is not publicly documented as a standard option. Positive aspects include the free/paid tier, broad model range, paid-tier data controls, Vertex AI integration, regional data residency, zero-data-retention approaches in Vertex AI, and the Google Cloud DPA. A critical point is that the free tier may use data for product improvement, grounding functions have additional data rules, in-memory caching may be enabled by default, and some zero-retention goals require project-specific settings.
Conclusion:
Gemini is very strong for multimodal, cloud-native, and Google-centric AI applications; for EU companies, the paid tier or Vertex AI with DPA, regional settings, disableable caching, and clear grounding rules should be preferred.
Gemini API – Additional Terms Vertex AI and no data retention
| On-prem / local hosting | ❓ |
| Private cloud / data center | ⚠️ |
| EU SaaS / Managed | ⚠️ |
| Hybrid | ⚠️ |
| DPA / AVV | ✅ |
| No training on customer data | ⚠️ |
| Open source / transparency path | ❓ |
Overall assessment of hosting & data:
The Gemini API is a managed cloud API service for multimodal LLM applications with text, image, video, audio, embeddings, Live API, TTS, image generation, tool use, grounding, context caching, and batch processing. Local on-premises hosting of the Gemini models is not publicly documented as a standard option. Positive aspects include the free/paid tier, broad model range, paid-tier data controls, Vertex AI integration, regional data residency, zero-data-retention approaches in Vertex AI, and the Google Cloud DPA. A critical point is that the free tier may use data for product improvement, grounding functions have additional data rules, in-memory caching may be enabled by default, and some zero-retention goals require project-specific settings.
Conclusion:
Gemini is very strong for multimodal, cloud-native, and Google-centric AI applications; for EU companies, the paid tier or Vertex AI with DPA, regional settings, disableable caching, and clear grounding rules should be preferred.
Gemini API – Additional Terms Vertex AI and no data retention
Strengths & Weaknesses at a Glance
| Strengths | Weaknesses |
|---|---|
| - Very broad range from high-end reasoning to very low-cost high-volume processing. | - The portfolio is currently somewhat confusing because stable 2.5 models, 3.x previews, and deprecated 2.0 models coexist in parallel. |
| - Strong combination of multimodality, coding, agents, grounding, tooling, and long context windows. | - For the direct Gemini API, data localization is documented less clearly than for Vertex AI; according to the Terms, for Paid Services logs may be stored transiently or cached in countries where Google or its agents operate facilities. |
| - Clear production pricing logic with Standard, Batch, Flex, and in some cases Priority. | - The cheaper models are strong for volume and standard tasks, but not ideal for the most difficult analysis and precision use cases. |
| - For Paid Services, prompts/responses are not used for product improvement according to the Terms. | - Preview models may still change before GA and have more restrictive limits. |
| - For enterprise environments via Vertex AI, there are stronger security/compliance options and regional processing models. |
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GDPR-compliant use possible?
GDPR assessment: From a GDPR perspective, the Gemini API depends heavily on the usage path: Google AI Studio/Gemini API Free Tier, Paid Tier, or Vertex AI.
Positive is that Google states for Paid Services that prompts and responses are not used for product improvement and are processed in accordance with the Data Processing Addendum. For the Free Tier, however, the following applies: content and responses may be used to provide, improve, and develop Google products and ML technologies; human reviewers may examine API input and output, and Google explicitly warns against entering sensitive, confidential, or personal information into Unpaid Services. For the EEA/Switzerland/UK, the Gemini API Terms state: API clients for users in these regions may only use Paid Services.
Server location: For Gemini Developer API Paid Services, prompts/responses may be temporarily stored or cached in countries where Google or its agents operate facilities for safety/abuse detection; with Vertex AI, data at rest remains in the selected location, and ML processing takes place for supported models in the chosen region or multi-region. Further link: Gemini API Terms, Gemini API Pricing, and Vertex AI Data Residency.
Gemini API – Additional Terms Vertex AI and no data retention