AI Gateway Platform and Model Infrastructure

Product challenge

AI providers expose different schemas, media types, pricing units, execution models, and failure modes. Developers needed one coherent journey from discovering a model to testing and integrating it, while the business needed accurate billing and the platform team needed reliable operations across more than 400 models.

Product and engineering approach

Normalize the complexity users should not carry

Onboarded image, video, audio, and multimodal models across multiple providers behind consistent API contracts.
Normalized provider-specific schemas, multipart inputs, asynchronous outputs, and error behavior across gateway versions.
Implemented pricing and billing support for request-, output-, and duration-based workloads, connecting technical model behavior to the commercial product.

Design the developer journey end to end

Built schema-driven playground controls, examples, code snippets, and media previews that adapt to each model's capabilities.
Improved multi-output experiences for image, video, and audio generations, including structured response handling and downloadable assets.
Extended shared UI and SDK layers so new model capabilities could ship without creating a different product experience for every provider.

Operate what ships

Worked across the Next.js Aurora platform and Python/Flask Heimdall gateway to deliver features end to end.
Used Redis and Celery for caching, background processing, and asynchronous inference workflows.
Built operational tooling for API health, latency, usage, cost visibility, rate limits, and reliable model releases.

Outcome

Expanded a unified AI gateway that supports more than 400 proprietary and open-source models.
Reduced integration friction by presenting varied provider APIs through consistent contracts, playgrounds, examples, and SDK behavior.
Improved the reliability and business readiness of model launches by connecting frontend schemas with gateway validation, pricing, billing, and observability.
Built practical product knowledge across generative image, video, audio, and multimodal workflows—not only the model APIs behind them.