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Scale your AI service with confidence using the Gemini API pay-as-you-go billing service. Set up billing easily in Google AI Studio by clicking on “Get API key”.
Sep 26, 2024 · Gemini API pricing is based on the following: Input token count; Output token count; Cached token count; Cached token storage duration; For pricing information, see the pricing page. Where can I view my quota? You can view your quota and system limits in the Google Cloud console. Can I use the Gemini API for free in EEA (including EU), the UK ...
Nov 6, 2024 · Here are a few things to keep in mind when using your Gemini API key: The Google AI Gemini API uses API keys for authorization. If others get access to your Gemini API key, they can make calls using your project's quota, which could result in lost quota or additional billing charges (if billing is enabled).
Build with Gemini 1.5 Flash and 1.5 Pro using the Gemini API and Google AI Studio, or access our Gemma open models.
3 days ago · Learn about Google's most advanced AI models, the Gemini model family, including Gemini 1.5 Flash, Gemini 1.5 Pro, and more
3 days ago · This API reference provides detailed information for the classes and methods available in the Gemini API SDKs. Pick a language and follow the setup steps to get started with building generative applications on your platform of choice.
3 days ago · import google.generativeai as genai genai. configure (api_key = "YOUR_API_KEY") model = genai. GenerativeModel ( "gemini-1.5-flash" ) response = model . generate_content ( "Explain how AI works" ) print ( response . text )
3 days ago · The easiest way to authenticate to the Gemini API is to configure an API key, as described in this section. If you need stricter access controls, you can use OAuth instead. If you don't already have an API key, create one in Google AI Studio.
Get started with the Gemini API on Google AI Studio. Quickly develop prompts for Gemini 1.5 Flash and 1.5 Pro with 2 million token context window.
Aug 8, 2024 · The embedding service in the Gemini API generates state-of-the-art embeddings for words, phrases, and sentences. The resulting embeddings can then be used for natural language processing (NLP) tasks, such as semantic search, text classification and clustering among many others.
