reAPI.ai

reAPI is an AI model aggregator that provides one API endpoint for image, video, chat, music, and code generation. It gives developers access to models from multiple providers through a shared key, dashboard, and pay-as-you-go credit balance.

What it does

reAPI routes requests to supported AI models from providers including OpenAI, Anthropic, Google, ByteDance, Black Forest Labs, and Suno. The documentation describes a standard endpoint for image, video, and chat models, while provider-specific paths support services such as video and music generation.

The service says it uses vendor health, latency, and load information to select routes and can automatically fail over when a provider degrades. It advertises 99.96% uptime and says failed generations are refunded automatically. Pricing is usage-based, with charges calculated according to the model's unit, such as tokens, images, or video seconds; credits do not expire. Current rates and payment options are listed on the pricing page.

Notable capabilities

• One reAPI key for more than 100 listed models across image, video, chat, music, and code workloads.
• OpenAI-compatible access through https://reapi.ai/api/v1, with the site stating that OpenAI, Anthropic, and Google SDK users can generally change the base URL and key.
• Support for streaming, function calling, structured outputs, image inputs, and audio inputs on supported routes.
• Automatic failover, idempotency keys, per-key spend caps, and configurable region pinning.
• A zero-logging posture for request bodies and model outputs; reAPI says it retains billing and audit metadata such as model name, token counts, latency, status code, and key identifier.

Who it helps

reAPI is aimed at developers and teams building applications that need to test or deploy several AI modalities without maintaining separate provider credentials, billing workflows, and integrations. It may suit products that switch between models for image creation, video generation, conversational AI, music, or coding tasks, particularly when centralized spend controls and routing are useful.

How it fits a workflow

A typical setup involves creating a key in the dashboard, setting an existing client library's base URL to reAPI, and selecting the desired model in the request. The documentation provides model-specific request schemas, parameters, sizes, resolutions, and examples. Teams can use a shared prepaid balance and review per-call costs through the dashboard. The site also states that new accounts receive free credits without requiring a credit card.

Strengths and limits

The main practical strength is consolidation: one integration, key, and billing balance can cover multiple providers and media types. Routing, failover, spend caps, and audit-only metadata are also relevant for production applications.

Implementation details are not identical for every model. Each model has its own request schema, and some provider-specific services use their original request shapes rather than the common chat endpoint. Account defaults listed by reAPI are 20 requests per second, 10 concurrent LLM requests, and 10 concurrent media tasks; the site says higher limits can be requested. Availability, supported parameters, and pricing therefore need to be checked for the specific model and route being used.