Pay Per Token or Run Your Own AI? AWS Adds Another Option
AI Wallet
Amazon Web Services has introduced Reflection Beam, an open-weight AI model available through SageMaker JumpStart. Unlike traditional AI services that charge for every million tokens processed, businesses can deploy this model on their own cloud infrastructure and pay for the computing resources they use. This gives companies another way to manage AI spending, especially when they process large amounts of information every day.
This calculator provides illustrative estimates, not an official AWS price comparison. Actual costs depend on infrastructure, model performance, processing capacity, utilization and operating expenses. Different models may require different amounts of computing power to complete the same work.
Default values are illustrative assumptions, not AWS or Reflection Beam prices. Self-hosted cost assumes the provisioned capacity can handle the selected volume.
Running an AI model on dedicated infrastructure does not automatically make it cheaper. Companies need to consider how much they use AI, how powerful the hardware must be and whether the equipment is being used efficiently. For smaller workloads, paying per token may remain the simpler and cheaper option. For businesses with large and predictable workloads, running their own model could become attractive. The best choice depends on the total cost of completing the work, not only the advertised API price.