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Sources: Amazon is weighing using OpenAI's and its own Nova models to cut costs after Anthropic raised prices for using its models in Amazon products

Catherine Perloff /The Information:

The Information Catherine Perloff

Context & Ripple Effects

Amazon has been building its own model capability: earlier coverage described its AGI team targeting performance beyond Anthropic's Claude with a forthcoming model, while more recent reporting said Amazon and OpenAI were discussing customized-model work.

This reported reassessment follows broader coverage of companies routing work to cheaper AI models as costs rise, increasing pricing pressure on leading model providers. It matters because Amazon is both a major AI product distributor and a company with an in-house alternative in Nova.

First-order effects

  • Amazon can reduce its exposure to Anthropic's higher usage prices by shifting some product workloads toward Nova and potentially OpenAI models.
  • Anthropic faces an immediate risk that a major customer reduces model usage in Amazon products; OpenAI gains a potential route into those workloads.

Second-order effects

  • Anthropic will face stronger pressure to defend its Amazon footprint through pricing, model performance, or commercial terms, while Amazon gains leverage by demonstrating credible multi-model options.
  • Amazon's move would validate workload-by-workload model selection rather than a single-provider strategy, encouraging other enterprise buyers to compare model cost more aggressively.

Third-order effects

  • If large platforms routinely substitute among proprietary and external models, frontier-model providers may have less durable pricing power unless their performance or product differentiation is difficult to replace.
  • The market could increasingly separate into model builders and orchestration buyers that mix models by task, with cloud and product platforms using in-house models chiefly as cost and negotiating leverage.

The trend: Enterprise AI is moving from early reliance on a small number of frontier models toward multi-model sourcing optimized for cost, capability, and bargaining power.