What it does
Replicate provides hosted APIs for machine-learning models and infrastructure for deploying your own models. Developers can explore model examples, call a model from an application and move custom inference code into a versioned deployment without operating the underlying GPU servers themselves.
- Explore models before integrating. The catalog contains official and community models across images, video, audio, speech and language. Each model has its own input schema and examples, so a team can test whether its outputs and controls fit the application before committing to an integration.
- Call models through familiar APIs. HTTP endpoints and client libraries for languages such as Python and JavaScript turn model execution into an application request. Developers supply model-specific inputs and handle the returned prediction or output, allowing AI features to be added to an existing backend.
- Handle inference as a prediction workflow. Model calls have a lifecycle rather than always returning an immediate finished asset. The documentation explains prediction status and output handling, helping applications represent running work, obtain completed results and manage failures instead of assuming every model behaves like a short synchronous call.
- Package a custom model with Cog. Replicate's deployment guide uses Cog to describe a model's environment, dependencies and prediction interface. Building and pushing that package creates a reproducible version that can run on the service, reducing manual differences between a developer's machine and hosted inference.
- Choose between shared models and your own deployment. A hosted catalog model is a quick evaluation path, while custom models give a team control over its inference code and versions. Deployment configuration and hardware requirements need to be matched to the workload, particularly for large or slow models.
- Evaluate fine-tuning and operating costs. Supported models expose training or fine-tuning workflows in addition to inference. Pricing, model licenses and output handling vary by model, so compare cost per useful result, request duration and commercial-use terms rather than treating all catalog entries as interchangeable APIs.
Check before you buyCheck each model's license, pricing, data handling, startup behavior and output retention. Community model availability and parameters can change; pin and test the version used by your application. No current startup credit award was confirmed.
Official sources · checked 2026-10-02Official model platform ↗ · API documentation ↗ · Custom model deployment ↗
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