Defapi

Defapi is a developer-focused orchestration layer that unifies API access to models from OpenAI, Anthropic, Google DeepMind and others, adding cost-aware routing, load balancing, usage monitoring, and enterprise security controls.

Defapi is an AI model orchestration platform that gives developers a single API endpoint for calling models from multiple providers instead of building and maintaining separate integrations for each vendor. Its live model catalog spans large language models such as OpenAI's GPT-5.6 family and Anthropic's Claude Opus 4.8 and Claude Fable 5, alongside vision models like Google DeepMind's Nano Banana 2 Lite, with input/output or per-request pricing published directly next to each listing. The platform is built for developers and technical teams who need to work with several AI providers at once: startups shipping AI features quickly, individual developers prototyping products, and enterprises running a multi-model strategy that requires centralized management, monitoring, and security oversight. Because access is entirely API-driven, it assumes a working knowledge of REST integrations rather than a no-code interface. In practice, a user registers for an account, generates an API key, and browses the model catalog to pick the right model for a given task based on capability and published cost. Requests are then routed through Defapi's unified interface, which handles intelligent routing toward more cost-effective models when appropriate, load balances traffic across providers, and logs usage for real-time monitoring. Defapi also states support for multi-region deployment and enterprise-grade security controls, positioning it for production workloads rather than one-off experiments. Within the broader AI-gateway space, Defapi's differentiator is breadth across modalities: language, vision, audio, and specialized APIs, combined with an enterprise security and auditing angle, rather than positioning itself purely as a cheap LLM router. Teams already committed to a single provider's native SDK may not need this layer, but organizations juggling several model vendors, or wanting a cost-monitoring and failover layer between their application and upstream AI APIs, are the clearest fit. Documentation, SDKs, and a Telegram community channel support onboarding, with dedicated technical support reserved for enterprise accounts.