AI model aggregator: what it is and when to go direct

An AI model aggregator is a service that resells many AI labs' models behind one API key, one request format and one bill. You skip signing up with each lab and learning each lab's API. In exchange, you pay the aggregator's margin and put another company between you and the model.

Sume works this way for video and image models, so it serves as the worked example here. Its facts come from the Video Generation and Image API docs, read on 2026-09-28. For which video models one Sume key reaches, see One API for Kling, Seedance and other video models.

What does an AI aggregator actually give you?

Four things, and each one is worth checking on any aggregator you consider:

• One account and key. You sign up once instead of once per lab.
• One request shape. On Sume's POST /v1/videos, one set of request fields serves every model, and the model itself is one field, model, set to a bare catalog id such as seedance-2.5.
• One bill. Sume bills video and image jobs to the workspace's USD balance, and the response's usage.cost is the billed amount.
• One catalog. GET /v1/catalog lists available capabilities, model ids, endpoint paths, runtime readiness and pricing metadata.

How much does an aggregator charge?

Whatever it publishes, so look for the pricing rule, not only a price. Sume states its rule in the docs: video is reserved on submit at provider list × 1.25, for every model, plus a 5.5% agent fee by default. The rates themselves are on API pricing, and How Sume pricing works explains the wallet.

The lab's own list price is the other half of the comparison. It differs by lab and by model, so compare the same model, resolution and length on both sides.

What do you give up with an aggregator?

Mostly control over the layer below. The table shows how Sume answers the questions that decide it.

When should I go direct to the lab instead?

• You need a parameter or feature the aggregator does not pass through. On Sume, that includes provider options and seed.
• You run one model at high volume, and the margin over the lab's list price outweighs the convenience.
• You need to know exactly which provider and backend served each request.
• You need a direct contract or support relationship with the lab.

When does an aggregator make sense?

When you want several models without several integrations: trying models side by side, keeping a fallback model a one-field change away, or keeping every generation on one bill. fal.ai and Replicate alternatives for video lists the aggregators developers compare for video.

An aggregator still makes you pick the model. A router picks it for you: on Sume that is sume/auto, which AI model router explains, along with what you lose when the choice is not yours.