> For the complete documentation index, see [llms.txt](https://docs.ai.neevcloud.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.ai.neevcloud.com/ai-inference/overview-1.md). # Model API ## What is NeevCloud Model API? NeevCloud Model APIs provide quick access to AI models, eliminating the complexity of GPU infrastructure management. Instead of provisioning compute resources, configuring environments, or managing deployments, you work with ready-to-use API endpoints that handle all infrastructure concerns automatically. ## What You Get ### Instant Access to AI Models Every model in the NeevCloud catalog is available as a production-ready API endpoint. You don't need to deploy containers, configure load balancers, or manage GPU clusters. Select a model, and you immediately receive an endpoint URL that's ready to handle inference requests. ### Zero Infrastructure Management The platform handles all DevOps concerns: autoscaling based on demand, load distribution across GPU instances, model loading and caching, health monitoring, and failover management. Your application simply sends requests to an endpoint and receives responses. ### Pay-Per-Use Pricing You're billed based on the number of tokens your requests consume—both input tokens (your prompts and context) and output tokens (the model's responses). There are no minimum commitments, upfront costs, or charges for idle time. If you send 1,000 requests one day and 100,000 the next, you pay proportionally for each. ## Who Should Use This This approach works well for: * Developers and startups who need to ship AI features quickly without building infrastructure * Enterprises running production workloads that require reliable, scalable inference * ML engineers prototyping with different models before committing to deployment * Teams that want to focus on application logic rather than infrastructure operations --- # 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 dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter: ``` GET https://docs.ai.neevcloud.com/ai-inference/overview-1.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal. 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.