Fauna is a distributed relational database with a document data model delivered as a cloud API. Fauna offers advanced querying capabilities, robust consistency guarantees, and comprehensive support for data operations. Developers can leverage Fauna’s strongly typed database language, FQL v10, to express complex business logic in transactions. With multi-region, active-active compute and a distributed transaction engine, Fauna ensures fast, reliable, and secure performance. Fauna provides flexible data modeling, global data access, and integrates seamlessly with various development tools and platforms. Fauna empowers developers to build new powerful applications faster, scale existing applications with confidence, and eliminates the need for server management with its serverless native approach. Fauna is designed to handle complex data models and supports real-time data synchronization across multiple devices and regions.

Fauna offers flexible pricing options to cater to the diverse needs of businesses. The cost of using Fauna primarily depends on factors such as data storage, data transfer, and request volume. For detailed information on pricing plans and to estimate costs for your specific use case, please visit our pricing page on the Fauna website.

Fauna ensures high-performance and low-latency data access by leveraging its global edge computing infrastructure. The bandwidth available to you depends on your chosen Fauna plan. We offer scalable bandwidth options to accommodate the needs of various applications. For more details on bandwidth allocation and limitations, please refer to our documentation or contact our support team.

Canceling your Fauna account is a straightforward process. Simply log in to your account and navigate to the account settings page. From there, you can find the option to cancel your subscription. If you have any questions or need assistance during the cancellation process, our dedicated support team is available to guide you through the steps.

Generative AI refers to the use of artificial intelligence techniques to create new content, such as images, videos, text, or even music, that imitates or generates novel and realistic outputs. It involves training AI models on vast amounts of data to learn patterns and generate new content based on that learning. Generative AI enables machines to produce creative and original outputs, often resembling human-created content.

Databases play a crucial role in generative AI applications by storing and managing the vast amount of training data and generated content. During the training phase, the database is used to store the training dataset, which serves as the input for training the generative AI model. The database allows efficient retrieval and processing of the training data, enabling the model to learn from the patterns and correlations in the data. In the deployment phase, the database is utilized to store and retrieve the generated content. The generated outputs can be saved in the database for future reference or used to enhance user experiences in applications like recommendation systems, personalized content generation, or creative tools.