Bedrock On-Demand vs Provisioned Throughput: Economics Breakdown for Nova, Claude & Model Decision Tree

Amazon Bedrock Model Menu Overview (Nova, Anthropic, Meta, etc.) Amazon Bedrock offers B2B leaders a comprehensive selection of foundation models (FMs) from leading providers, facilitating seamless integration into operations like RAG pipelines and AI agents. Key offerings include: - Amazon Nova Family : Models such as Nova Premier, Pro, Lite, Micro, Canvas (for creative tasks), Reel (video), and Sonic (speech). These are optimized for multimodal workloads, covering understanding and generation across text, image, and audio. - Anthropic Claude Series : High-performance models like Claude 3.5 Sonnet (model ID: ), Haiku, and Opus variants, which excel in reasoning and tool use for agentic applications. - Meta Llama : Open-weight models like Llama 3.1, available for cost-effective, customizable inference. - Others : Cohere, Mistral AI, Stability AI, and Titan models, catering to specialized

needs from embeddings to image generation. Bedrock's serverless architecture abstracts infrastructure management, but pricing depends on throughput modes: on-demand for flexibility or provisioned for predictable scale. This model menu evolves rapidly—always consult the for the most current model IDs and specifications as of your evaluation date. On-Demand vs. Provisioned Throughput: Key Differences Amazon Bedrock provides two primary inference modes to balance cost, predictability, and scalability: - On-Demand : This is a pay-per-use model with no long-term commitments. It's ideal for variable or experimental workloads. You invoke models via APIs like Converse or Messages, and you are billed per input/output token (typically per 1,000 or 1 million tokens). There are no minimum commitments, and the service auto-scales, but rates reflect peak demand. - Provisioned Throughput (PT) : With P

T, you commit to dedicated capacity for a specific term (1, 6, or 12 months). This guarantees a certain tokens-per-minute (TPM) throughput, which can reduce latency and costs for steady, high-volume usage. You purchase PT via the AWS console or API, selecting the model ID and the desired TPM level (ranging from 10,000 to over 1 million TPM). While unused capacity does not roll over, PT is highly recommended for production RAG or agent applications. Key tradeoffs to consider: - Flexibility : On-demand is superior for spiky traffic patterns; PT involves a commitment. - Predictability : PT offers fixed hourly rates after the initial commitment. - Latency : PT prioritizes your requests, leading to lower latency. - Enterprise Perks : PT includes Service Level Agreements (SLAs) for 99.9% availability. According to AWS documentation, you can switch inference modes per model invocation, meaning

there's no full-service lock-in. Unit Economics Breakdown: How Throughput Modes Affect Costs The unit economics can shift dramatically between on-demand and provisioned throughput modes, especially at scale. On-demand is suitable for prototyping (e.g., fewer than 1 million tokens per day), while PT unlocks significant discounts for production workloads (e.g., 100 million+ tokens per month). Core Metrics - Per-Token Costs : On-demand pricing involves variable input and output rates. PT pricing is a blended hourly rate divided by the committed TPM. - Discount Mechanics : For sustained workloads, PT can reduce effective per-token costs by 30-75% compared to on-demand rates (based on AWS methodology; actual savings vary by model and commitment term). - Additional Factors : Image and video tokens are often multiplied (e.g., one image might equate to approximately 1,000 tokens for certain mode

ls). Batching requests or implementing caching strategies can further reduce overall costs. Formula for PT Economics : Effective cost per million output tokens = (Hourly PT rate × 730 hours/month) / (Committed TPM × 60 minutes × efficiency factor) Where the efficiency factor (less than 1) accounts for input tokens and potential padding. Utilize the AWS Cost Explorer for detailed simulations. For models like Amazon Nova or Anthropic Claude, PT is particularly beneficial for agent loops that exhibit consistent query volumes, allowing you to amortize commitment costs over high utilization (typically above 70%). Official Pricing for Top Models (Cite AWS Docs as of May 2026) Pricing is model-specific and region-dependent. Always refer to the and as of your evaluation date (e.g., May 4, 2026, UTC). Avoid relying on third-party aggregators for critical pricing information, especially when makin

g commitments. Example SKUs and Structure - Anthropic Claude 3.5 Sonnet ( ): - On-Demand: Input $X per 1 million tokens, Output $Y per 1 million tokens (example rates for US East; always check live rates). - PT: Hourly rates scale with TPM commitment (e.g., a 6-month term at Z% per hour for 100,000