Amazon Bedrock Provisioned Throughput: Unlocking Better Unit Economics vs On-Demand

Amazon Bedrock Model Menu Overview Amazon Bedrock offers enterprise access to a diverse menu of foundation models (FMs) from top providers, including Amazon's own Nova series, Anthropic's Claude family, Meta's Llama models, and others like Cohere, Mistral AI, and Stability AI. As of May 6, 2026 (UTC), the platform supports advanced model IDs such as , , , and newer releases like Claude Mythos Preview ( ). This model menu enables B2B teams to build production-grade applications like retrieval-augmented generation (RAG), multi-agent systems (e.g., platforms like LUMOS), and high-volume inference without managing infrastructure. Key features include latency-optimized inference for models like Claude 3.5 Haiku and Llama 3.1, Guardrails for safety, and Knowledge Bases for RAG workflows. For the latest catalog, refer to the . Bedrock's inference modes—on-demand and provisioned throughput—funda

mentally alter unit economics, especially for predictable enterprise workloads. On-Demand vs Provisioned Throughput Explained On-demand inference charges based on actual usage, typically per 1,000 input and output tokens processed. It's flexible for variable or low-volume workloads, with no upfront commitments. Pricing scales with consumption, making it ideal for prototyping or bursty traffic. Provisioned throughput , in contrast, reserves dedicated model capacity via hourly commitments. You purchase Provisioned Throughput (PT) in units that guarantee a specific throughput level (e.g., tokens per minute). This mode suits steady, high-volume production use, offering lower per-token costs at scale due to the commitment. Per AWS documentation as of May 6, 2026, PT comes in two flavors: - Standard PT : Balanced cost and performance. - Priority PT : Higher priority access with potentially low

er latency, at a premium. To view exact throughput units and eligibility (model-specific), check the . Provisioned throughput requires a minimum 1-month commitment, with options to commit for 6 months for deeper discounts. Unit Economics: Cost Breakdown by Mode Unit economics in Bedrock hinge on workload volume, predictability, and model choice. On-demand is straightforward: total cost = (input tokens input rate) + (output tokens output rate), billed per 1,000 tokens. Provisioned throughput shifts this to a hybrid: - Hourly capacity fee : Fixed cost for reserved throughput (e.g., X tokens/minute). - Per-token usage fee : Lower than on-demand, applied only to consumed tokens within the reserved capacity. For high-volume workloads (e.g., 1M tokens/day), PT can reduce effective per-token costs by 30-70% versus on-demand, depending on utilization. However, if utilization <50%, on-demand may

be cheaper due to no fixed fees. Methodology for estimation (as of May 6, 2026): 1. Query your workload: Estimate daily input/output tokens and peak tokens/minute. 2. Use AWS Pricing Calculator: Input model ID (e.g., ), region, and PT hours. 3. Compare: On-demand total vs. PT (capacity fee + usage fee). Always verify via or console, as rates vary by model, region (e.g., US East vs. Europe), and tier. No overclaims: Savings require sustained load; test with Bedrock's cost explorer. Key Models: Nova, Claude Opus, Meta, and Pricing Nuances Bedrock's menu features specialized profiles: - Amazon Nova 2 ( ): Multimodal, high-throughput for enterprise RAG/agents. PT available with high tokens/minute units; excels in cost-sensitive ops. - Anthropic Claude Opus 4.7 ( ): Reasoning powerhouse for complex agents. PT offers massive scale; check for token multipliers (e.g., images/videos). - Meta Llam

a 3.1/4 (e.g., ): Open-weight efficiency for coding/volume tasks. Lower PT entry barriers. Nuances: - Batch inference : Up to 50% cheaper across modes for non-latency-sensitive jobs. - Image/video tokens : Nova/Claude multiply tokens (e.g., 1 image 1K tokens); PT reserves accordingly. - Model updates : IDs evolve (e.g., Claude Opus 4.6 → 4.7); PT commitments lock to specific versions. For Bedrock model menu pricing specifics, see as of May 6, 2026. Decision Tree for Picking the Right Model Profile Use this decision tree to select between on-demand/PT and models for your workload: Tailor for platforms like LUMOS: Multi-agent routing favors PT for core models. Workload Scenarios: RAG, Agents, and High-Volume Use RAG Workloads : Knowledge Bases + embedding models. On-demand for dev; PT for prod queries ( 10K/day). Nova 2 optimizes context windows (up to 1M+ tokens). Agent Platforms (e.g., L

UMOS) : Tool-calling chains. Claude Opus via PT handles 100s TPS; unit economics drop with commitment. High-Volume : Batch jobs (e.g., data processing). PT + batch mode yields best ROI; estimate via AWS calculator. Real-world: A 1M token/day RAG app might save 50%+ on PT vs on-demand (hypothetical;