The story behind BabySea

Five months ago, I set out to build a consumer app that let users generate images and videos using AI. The idea was simple: pick a model, write a prompt, and get results. I integrated more than 70 models, all running on a single provider. It should have worked. It didn't.

The problem wasn't the models, and it wasn't the infrastructure. It was how execution behaved across them. Even within the same provider, every model behaved differently. One model expected aspect_ratio, another called it image_size, and another required raw pixel values like 2848x1600. It was the exact same capability, but expressed through completely different request formats. Instead of building a product, I found myself writing converters for every model. Eventually, the approach became unsustainable.

That's when the real question surfaced: why hasn't anyone solved this properly?

The ecosystem already has routers, proxies, and failover systems. But generative media still breaks at the workload layer. Providers expose different request formats, different failure modes, and different operational behavior, so the integration problem never really disappears. As more providers and models emerge, these differences compound, making production behavior increasingly hard to manage.

I started thinking about what the system should have been. What if there was an execution layer that controlled how requests ran across providers? What if routing, failover, delivery, and observability were part of the same system instead of something every team had to build on its own?

So I stopped building the application and started building the infrastructure layer I wished had existed.

That's BabySea.

BabySea is execution infrastructure for generative media.

It runs image and video workloads across multiple AI providers with routing, failover, billing, and delivery built into a single execution system.

Developers define how workloads should run, including provider priority and failure behavior. BabySea enforces that at runtime and returns a single tracked lifecycle with visibility into provider selection, latency, and cost.

The goal is to make generative media execution reliable, observable, and controllable across providers.

This changes how teams build against generative media infrastructure. Instead of wiring provider-specific logic into the application, you integrate once and define how workloads should execute.

Whether the model runs on Alibaba Cloud, Black Forest LabsBlack Forest Labs, BytePlus, Cloudflare, Fal, OpenAIOpenAI, ReplicateReplicate, or RunwayRunway, the application logic stays stable. Routing, failover, and delivery are enforced by the execution layer.

BabySea absorbs the provider-specific complexity at the execution layer.

Reliability was a core design principle from the beginning. Provider failures are inevitable, but most systems treat them as fatal.

When a provider goes down, users see errors, and applications break. BabySea handles this differently. It continuously monitors provider health and uses automatic failover with circuit breaker logic. If one provider fails or times out, the request is routed to the next available provider.

If all providers fail, credits are refunded immediately. Every error is structured, predictable, and includes a retryable signal, so applications can handle failures programmatically without relying on provider-specific error messages.

The system is designed to be simple on the surface and powerful underneath. The TypeScript SDK is lightweight, zero-dependency, and works across Node.js, edge runtimes, and browsers. It includes typed generation methods, consistent error handling, and runtime controls for provider routing and failover.

Security is built in from the start. API keys are scoped with granular permissions, support IP allowlisting, and can be rotated without downtime. Webhooks are cryptographically signed, idempotent, and automatically retried, ensuring reliable delivery without additional infrastructure.

Execution should not be a black box.

BabySea tracks every generation with detailed metrics, including latency, success rate, provider usage, and cost. Logs and historical data are preserved, allowing teams to analyze performance trends and optimize both reliability and cost over time.

This turns model execution into something observable, measurable, and controllable.

The number of companies building with AI is growing rapidly, and most of them will face the same challenges: inconsistent APIs, provider instability, and increasing complexity as they scale.

Many teams try to solve this internally, but it quickly becomes a maintenance burden. BabySea exists to solve this once, at the infrastructure level, so developers don't have to rebuild the same system over and over again.

We are continuously expanding provider coverage, adding new models, and improving routing, reliability, and observability across regions. Our focus is to make the execution layer faster, more resilient, and easier to adopt as the ecosystem evolves.

But the goal remains the same:

to make generative media execution reliable, predictable, and controllable.

BabySea is just getting started, and I'm building it in public.

If you're building with AI, you'll eventually run into this problem.

When you do, this is for you.