Cognition is betting that owning the model and infrastructure gives it an edge in AI coding, while Factory is betting that flexibility across models and deployment setups is the better route. A new comparison says the two approaches are now playing out in enterprise software engineering, with very different trade-offs for buyers.
Watch Desk analysis
What happened
Cognition has become the first customer to run production agentic workloads on CoreWeave’s NVIDIA Vera Rubin NVL72 racks, and has scaled to thousands of GPUs, according to FourWeekMBA. The comparison says Cognition’s reported 4.8-times token-throughput-per-GPU result is for its own SWE-2 model and its vertically integrated stack. That is the company’s claim, not an independently reproduced benchmark.
Factory takes the opposite tack: its Router chooses between models, and Factory Private is designed to let customers control which models run and where, including on-premises or in air-gapped environments. The comparison’s account of Cognition and Factory’s competing bets draws on company and partner material.
Why it matters
This is a useful split for organisations weighing AI coding tools. A tightly integrated model-and-compute stack may offer more room to optimise performance; a model-agnostic router may offer more choice over providers and deployment. Neither architecture is an automatic win, and the costs, control requirements and workloads will differ between buyers.
The cost figures in the comparison should not be collapsed into one headline saving: Factory’s 20–25% figure is a benchmark, 63% is an aggregate production figure, and 72% is a median reported for router users at Adyen. Factory also reports benchmark results on coding tasks, but the article says these figures have not been independently reproduced.
Our read
The important question is not which company has the tidiest architecture diagram. It is whether either approach delivers the right mix of quality, cost and control on a buyer’s actual workloads. Treat the performance and savings figures as attributed claims, then ask vendors for measurements that match your own deployment.
What to watch
- Whether Cognition’s reported throughput advantage holds up beyond its own model and infrastructure.
- How Factory’s routed results compare across models, workloads and deployment environments.
- Whether enterprise buyers favour a vertically integrated stack or the ability to switch models and hosting setups.
Discussion spark: For enterprise AI coding, would you trust a tightly integrated model-and-compute stack more, or prioritise the freedom to change models and deployment environments?
Sources and evidence
- Cognition and Factory Bet Opposite Sides of the AI Stack – FourWeekMBA (1 October 2026, 05:33 UTC)
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