> For the complete documentation index, see [llms.txt](https://docs.release.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.release.com/release-instant-datasets/introduction.md). # Introduction {% hint style="info" %} The documentation in this Release Instant Datasets section is for the standalone product. For the Instant Datasets feature in the Release Environments as a Service product, [start here instead](https://docs.release.com/reference-documentation/instant-datasets-aws). {% endhint %} Access to data can be a challenge when developing software. Seed data allows you to generate a simple and consistent dataset, but such a dataset is unlikely to be reflective of what your application will be accumulating in production. As your application changes, access to data becomes yet another problem to manage. A more optimal solution to the problem of accessing data would be to have a pool of data resources in various forms – such as in a database or cache, in search infrastructure, and so on – ready to be used immediately by an environment for the duration of the environment's life. This is the solution Instant Datasets provides. With minimal set up, you can have production-replica data available to any of your environments instantly, regardless of dataset size. Instant Datasets supports databases on AWS (RDS and Aurora), Google Cloud SQL, [PlanetScale](/integrations/integrations-overview/planetscale-integration.md), and [Neon](/integrations/integrations-overview/neon-integration.md). ## Why use Instant Datasets? At Release, we've used Amazon RDS to power our production application from the beginning. Amazon RDS automatically takes daily snapshots of your application databases and provides them to you in a readily accessible manner. One of the major drawbacks to using RDS for environments other than production is the long spin-up time for any particular database, making it less than ideal for non-production environments. Instant Datasets solve this problem, while also providing your development or staging environments with production-like data. Instant Datasets are a collection of databases that are ready to be used by any environment. These datasets are based on the production snapshots created for you by Amazon RDS. Each time an environment that requires an Instant Dataset is created, another dataset is generated from the production snapshot so that you never run out of databases or have to wait for one to spin up. This process allows access to production-like data instantly for any environment you create. --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation by asking a question. Perform an HTTP GET request on the following URL with the `ask` and `goal` query parameters: ``` GET https://docs.release.com/release-instant-datasets/introduction.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is what the user is ultimately trying to achieve, the reason they need the answer. Sharing it helps GitBook give you a better, more relevant answer. A goal is most helpful when it describes the outcome the user wants rather than restating the question. For example, with `ask=how do I create an API token`, a goal like `automate deployments from our CI pipeline` lets GitBook tailor the answer to that use case. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.