The story behind BabySea

In May 2025, I started by building a consumer app for image and video generation. The idea was simple: pick a model, write a prompt, and get results. I integrated more than 70 models into the product. It should have worked. It didn't.

The problem was not model access. It was execution. 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. The same capability was expressed through different request formats, output formats, failure modes, and timing behavior.

Instead of building the product, I kept building adapters, converters, retry logic, billing guards, storage handling, and webhook coordination. Eventually, it became clear that the application was not the hard part. The missing layer underneath it was.

That is when the real question surfaced: why is every team expected to rebuild this layer?

The ecosystem already has model providers, routers, proxies, and SDKs. But generative media still breaks at the execution layer. Providers expose different request formats, async behavior, pricing models, file handling, error responses, and delivery paths. As more providers and models emerge, these differences compound.

I started thinking about what the system should have been. What if there was one execution system for generative media? What if request validation, provider selection, failover, credits, artifacts, events, webhooks, and observability were part of the same control plane?

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

That's BabySea.

BabySea is the execution control plane for generative media.

Developers send image and video workloads through one API. BabySea manages request validation, provider selection, failover, credit reservation and settlement, execution state, artifact delivery, events, webhooks, and observability across inference providers.

Every workload returns a consistent execution record with visibility into provider selection, latency, output, cost, and request state. Over time, provider selection adapts based on real execution outcomes, improving reliability, performance, and cost efficiency as the system processes more workloads.

The goal is to make generative media execution predictable in production.

This changes how teams build with generative media. Instead of wiring provider-specific behavior into the application, developers integrate once against BabySea's execution model.

Whether a model runs on Alibaba Cloud, Black Forest LabsBlack Forest Labs, BytePlus, Cloudflare, Fal, OpenAIOpenAI, ReplicateReplicate, or RunwayRunway, the application logic stays stable.

BabySea absorbs provider-specific complexity inside the execution control plane.

Reliability was a core design principle from the beginning. Provider failures are inevitable, but applications should not have to treat every provider failure as a customer failure.

BabySea continuously evaluates provider health, uses circuit breakers, and moves workloads through the best available provider path. If one provider fails or times out, execution can continue through another provider without requiring custom recovery logic in the application.

If all providers fail, credits are refunded and the API returns a structured, retryable error. Every failure is mapped into a consistent error model so applications can respond programmatically without depending on provider-specific error messages.

The system is designed to be simple on the surface and powerful underneath.

Developers use one API or the TypeScript SDK to run image and video workloads across providers. The SDK is lightweight, works across Node.js, edge runtimes, and browsers, and includes typed generation methods, consistent errors, retries, and webhook verification.

Security is built into the execution path. API keys support scopes, IP allowlists, and rotation. Webhooks are signed, idempotent, retried automatically, and backed by delivery logs and dead letter handling.

Execution should not be a black box.

BabySea tracks provider selection, latency, success rate, error behavior, credit usage, and cost for every workload. This makes execution observable across models, providers, and regions.

That visibility also improves the system itself. Provider selection can adapt based on real execution outcomes, so the control plane becomes more reliable and cost-efficient as more workloads run through it.

Generative media is becoming part of more products, but the infrastructure around it is still fragmented. Teams increasingly need multiple providers for cost, quality, coverage, and reliability.

Most teams solve this by building internal orchestration logic. That works at small scale, but becomes a maintenance burden as async jobs, retries, billing, storage, webhooks, and failures begin interacting.

BabySea exists to solve this once, at the infrastructure level, so developers do not have to rebuild the same execution system inside every application.

We are expanding provider coverage, adding models, and improving execution quality across regions.

The focus is not only more models. The focus is better execution: lower latency, stronger provider selection, clearer observability, safer billing, and more predictable behavior under production load.

The goal remains the same:

make generative media execution predictable, observable, and scalable.

BabySea is still early, but the execution layer is becoming inevitable.

If you are building with generative media, you will eventually run into this problem.

When you do, this is for you.