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HiAPI fronts image, video, and audio generation models from every major provider behind one API.
Grounded in available product and source data
Integrating five separate generative-media providers usually means five different request formats, five sets of credentials, and a storage pipeline built for each — HiAPI is one unified API for image, video, and audio AI instead, fronting all of them behind a single endpoint and request shape.
Every generation runs through the same task lifecycle regardless of which model is called: submit a job with the base model name and its input parameters, and either poll for status or supply a callback URL to be notified on completion instead. Submitting with the same idempotency key on a retry doesn't create a second task, which matters specifically for production pipelines where a network retry shouldn't double-charge or double-generate.
Storage is handled as part of the same call rather than a separate integration: setting the storage option to persistent returns generated outputs as durable links, removing the need to build and maintain a storage pipeline per provider just to keep results reachable after generation.
Beyond direct API calls, HiAPI is built to be reachable by coding agents specifically — MCP exposes agent-accessible tools, Skills packages reusable instructions for common generation workflows, and an llms.txt index lets a coding agent read HiAPI's own documentation and self-configure the integration rather than a developer wiring it by hand.
The site states pricing is shown per model before use, flat per task with no token billing or surge pricing, and a Playground on each model's page lets parameters and example output be tested before any integration code gets written. New signups receive a stated free credit allowance to try real generations first.
The main gain is switching cost — since every model sits behind the same request shape and task lifecycle, trying or swapping to a different provider's model doesn't mean rewriting the integration from scratch each time.
Not necessarily — setting the storage option to persistent returns a durable link for the output directly in the task result, rather than requiring a separate storage pipeline to be built and maintained.
Either by polling the task status or, more directly, by supplying a callback URL — HiAPI then notifies that endpoint when the task reaches its final state instead of the caller having to keep checking.
Yes — an llms.txt documentation index is specifically meant to be handed to a coding agent so it can read HiAPI's own docs and work out the setup itself, alongside separate MCP tools and packaged Skills for common workflows.
Nothing gets duplicated as long as the same Idempotency-Key header is reused — a retried request with that same key returns the original task rather than creating and billing a second one.
