Defapi

Defapi puts models from OpenAI, Anthropic, Google, and other providers behind one endpoint and one API key. You don't manage a separate account or credential per vendor. That's the core of the "one-stop access" idea, and it's the reason a small team can support several providers without several integration projects.

AI model routing here means requests go to the most cost-effective model that fits the job, and traffic is balanced rather than hammering a single provider. Defapi markets this mainly as cost control without sacrificing quality. In practice it's a way to steer routine calls to cheaper models while keeping stronger ones for the work that needs them, and it's the feature that makes an enterprise AI API worth considering over direct vendor access for high-volume teams.

The platform adds auditing on top of normal API access, so requests and usage are logged rather than invisible. For teams that need to show what AI systems are being called and when, that record matters more than raw throughput. Defapi advertises encryption, access controls, and compliance with industry standards as part of this layer.

A dashboard shows usage statistics, spend, and billing records in one place. Because costs vary by model and request type, this is where you catch a runaway model choice before it shows up on a bill. The per-model breakdown is the useful part, since a single blended number wouldn't tell you which call is expensive.

The supported range spans large language models, vision models for image generation and analysis, audio models for speech recognition and synthesis, and specialized APIs. That breadth means the same gateway can serve text, image, and voice features rather than requiring a different vendor per modality.

Defapi ships SDKs and documentation aimed at making the first call quick. The onboarding path is the standard one developers expect: register, generate a key, choose a model, send a request. Nothing unusual there, and that's fine. Familiar beats clever for infrastructure.