AnswersEnterprise AI platforms
In short
Fig's CosmOS works with every frontier lab's models and the best open-weight models, and routes each task to the one that does it best within the budget and policy each team sets. A multi-model platform should also fall back when a model fails or drifts, keep company data out of suppliers' training, and train models of the company's own for the high-volume work.
| Capability | Why it counts |
|---|---|
| Every lab's newest models | The best model for a task changes every few months, and no lab leads everywhere. |
| Open-weight models | Cheaper for routine work, and they can run inside your own environment. |
| Routing per task | Each task goes to the cheapest model that clears the quality bar. |
| Per-team controls | Admins choose which models each team may use and set budgets. |
| Fallbacks | Work continues when a model is unavailable, slow or drifting. |
| Models of your own | Trained on your data for the work that defines the business, with weights you keep. |
Fig routes each task into one of two lanes: Flash for fast routine work and Thinking for deep, multi-step work. Within a lane it picks the model for that task class from several labs, choosing the cheapest one that clears the success bar on the company's own evaluations, inside the budget and policy the organization sets.
Flash or Thinking: how Fig routes every task →Auto routing →
A platform tied to one lab follows that lab's releases, prices and terms. When a model is retired, a retention term changes or supply is cut, the whole roadmap moves with it. Routing across labs removes that single point of failure.
Fig vs. OpenAI and Anthropic →How do you avoid AI vendor lock-in? →
Every frontier model from multiple labs, open-weight and proprietary, plus models Fig trains with the company on its own data.
Yes. Admins choose the models each team can use and set budgets and spend rules per team.
Usually cheaper: routine work goes to smaller, cheaper models, and only the hardest work goes to the most capable ones.
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