Skip to main content

Pricing

Key Takeaways​

Amazon Bedrock provides three core pricing and purchasing options for foundation model inference: On-Demand (pay-as-you-go per token/image), Batch Mode (asynchronous processing via Amazon S3 with a 50% cost discount), and Provisioned Throughput (reserving dedicated Model Units for consistent throughput).

Amazon Bedrock Pricing & Inference Modes​

  1. ON-DEMAND (Pay-As-You-Go)
    • Billed per 1,000 / 1,000,000 Input & Output tokens (or per generated image)
    • Zero upfront commitment; ideal for bursty, unpredictable, or dev workloads
  2. BATCH MODE (50% Cost Savings)
    • Asynchronous bulk processing: S3 Input File → Bedrock → S3 Output File
    • 50% discount vs. on-demand; ideal for non-real-time offline jobs
  3. PROVISIONED THROUGHPUT (Reserved Capacity)
    • Purchases dedicated Model Units (MUs) on 1-month or 6-month commitments
    • Guarantees max tokens/min throughput and zero throttling
    • MANDATORY requirement to run Custom / Fine-Tuned / Imported Models

When optimizing costs, note that inference parameters like Temperature, Top-K, and Top-P do not affect pricing—the primary cost driver is total input and output token volume.

Main Discussion​

The Three Bedrock Inference Pricing Modes​

Inference ModeBilling UnitCommitment TermsPrimary Architectural Fit
On-DemandPer Input/Output Token & Image GenNone (Pay-as-you-go)Interactive web chatbots, prototyping, variable load
Batch Inference50% of On-Demand Token RatesNone (Job-based)Offline document analysis, nightly report summaries
Provisioned ThroughputHourly rate per Model Unit (MU) committed1-month or 6-month (or no-commit 1 MU)Production SLAs, high QPS, custom/fine-tuned models
  • On-Demand Pricing: Billed independently for input tokens and output tokens. Output tokens generally carry a higher price per token due to the computational cost of sequential autoregressive token generation.
  • Batch Mode: You supply a JSONL file in Amazon S3 containing bulk prompts. Bedrock processes them asynchronously, outputs a single result file back to S3, and applies a flat 50% discount compared to standard on-demand token rates.
  • Provisioned Throughput: Designed for guaranteed capacity and strict latency SLAs. Crucial Rule: To run inference on custom fine-tuned models, you cannot use on-demand billing; you must purchase Provisioned Throughput.

The Cost Spectrum of Model Customization & Adaptation​

Adapting foundation models to business use cases follows a progressive cost and compute curve:

Engineering Cost: Prompt Engineering≪RAG<Instruction Fine-Tuning≪Domain Adaptation (Continued Pre-training)\text{Engineering Cost: } \text{Prompt Engineering} \ll \text{RAG} < \text{Instruction Fine-Tuning} \ll \text{Domain Adaptation (Continued Pre-training)}

  • Prompt Engineering ($): Zero additional model training, storage, or infrastructure overhead. You pay only standard inference token charges for prompt optimization.
  • Retrieval-Augmented Generation / RAG ($$): Base model weights remain untouched. Adds operational costs for document storage in Amazon S3, embeddings generation, and vector database capacity (such as Amazon OpenSearch Serverless OCUs).
  • Instruction-Based Fine-Tuning ($$$): Modifies model weights using labeled input-output pairs. Incurs one-time GPU training costs plus mandatory Provisioned Throughput for inference hosting.
  • Domain Adaptation / Continued Pre-training ($$$$$): Massive pre-training over large volumes of unlabeled raw domain text to expand foundational vocabulary and domain reasoning. Highest computational and financial expense.

Cost Optimization Levers & Token Management​

Bedrock Cost Optimization Levers​

  1. INPUT TOKEN PRUNING:
    • Strip redundant boilerplate and irrelevant conversational history
  2. OUTPUT LENGTH CONSTRAINTS (max_tokens):
    • Cap maximum completion tokens and enforce concise generation formats
  3. RIGHT-SIZING FOUNDATION MODELS:
    • Route short NLP/classification tasks to lightweight models (e.g., Nova Micro/Lite or Titan Text) instead of expensive flagship reasoning models
  4. LEVERAGING BATCH MODE:
    • Shift non-urgent, offline bulk jobs to batch inference for 50% savings
  • Inference Parameters vs. Cost: Adjusting Temperature, Top-P, or Top-K alters model creativity and token selection distributions, but has zero direct impact on pricing.
  • Token Volume is the Pricing Driver: The most effective lever for reducing Bedrock expenses is minimizing unnecessary input prompt tokens and capping generated output tokens.

Exam Guide​

Exam Tips​

  • Batch Mode Discount: Remember the exact savings figure: Batch inference provides a 50% discount compared to on-demand pricing for non-real-time workloads processed via Amazon S3.
  • Provisioned Throughput Mandate: If an exam question mentions deploying a customized, fine-tuned, or imported model for inference, you must select Provisioned Throughput (on-demand mode is not supported for custom models).
  • Provisioned Throughput is NOT a Default Cost Saver: Provisioned Throughput is designed for guaranteed capacity, rate-limit avoidance, and predictable SLAs, not general cost reduction. If utilized below capacity, it can be more expensive than on-demand.
  • Hyperparameters vs. Billing: Parameters like Temperature, Top-K, and Top-P do not change token pricing. Only total tokens consumed and model tier dictate cost.

Practice Test​

Question 1​

A media company needs to generate text summaries for 100,000 archived news articles every weekend. The summarization process is not time-sensitive, and results can be delivered within 24 hours. The engineering team wants to minimize inference costs on Amazon Bedrock. Which approach provides the highest cost savings?

  • A. Provision a dedicated Provisioned Throughput Model Unit with a 6-month commitment
  • B. Submit the documents as an asynchronous Batch Inference job reading and writing to Amazon S3
  • C. Increase the Temperature and Top-P inference parameters on an On-Demand endpoint
  • D. Perform supervised fine-tuning on an Amazon Titan model using labeled summaries
Correct Answer
  • B. Submit the documents as an asynchronous Batch Inference job reading and writing to Amazon S3
    • Explanation: Batch Inference on Amazon Bedrock processes non-real-time bulk workloads asynchronously via Amazon S3 at a 50% discount compared to standard on-demand pricing, making it the most cost-effective solution for non-urgent tasks.

Question 2​

A machine learning engineer has completed a supervised fine-tuning job on Amazon Bedrock to adapt an Amazon Nova model for medical billing classification. The engineer now needs to deploy the custom fine-tuned model for production inference. Which purchasing option is required?

  • A. On-Demand pricing per 1,000 tokens
  • B. Provisioned Throughput
  • C. Spot Model Units with automatic scale-in
  • D. AWS Free Tier on-demand allocation
Correct Answer
  • B. Provisioned Throughput
    • Explanation: Amazon Bedrock requires Provisioned Throughput to host and serve inference requests for custom, fine-tuned, or imported models. On-demand pay-as-you-go inference is supported only for base foundation models.

Question 3​

A media analytics company utilizes Amazon Bedrock to run inferences with its generative AI models to analyze large volumes of user-generated content and provide insights to its clients. The company frequently processes numerous inference requests and is looking for a way to minimize the costs associated with running these inferences while still maintaining the required level of service. Given that the company can tolerate some delays in receiving responses, it seeks a cost-effective inference method that optimizes resource usage without sacrificing too much on turnaround time.

Which inference approach would be the most suitable for the company to use in order to reduce its overall inference costs?

  • A. The company should use serverless inference, which automatically scales resources based on traffic
  • B. The company should use batch inference, thereby allowing it to run multiple inference requests in a single batch
  • C. The company should use real-time inference, which is designed for low-latency responses and continuous, immediate processing
  • D. The company should use on-demand inference, which allows the company to pay only for the resources consumed during each inference
Correct Answer
  • B. The company should use batch inference, thereby allowing it to run multiple inference requests in a single batch
    • Explanation: Batch inference allows you to run multiple inference requests asynchronously and improve the throughput of model inference on large datasets. In Amazon Bedrock, batch inference for supported foundation models is offered at a 50% discount compared to on-demand pricing, making it the most cost-effective option for non-latency-sensitive bulk processing.
Incorrect Answers
  • A. The company should use serverless inference, which automatically scales resources based on traffic
    • Explanation: Serverless inference is a deployment option in Amazon SageMaker, not a native inference mode in Amazon Bedrock.
  • C. The company should use real-time inference, which is designed for low-latency responses and continuous, immediate processing
    • Explanation: Real-time inference applies to Amazon SageMaker endpoint deployments. Furthermore, low-latency immediate processing is unnecessary and suboptimal for cost savings when delays can be tolerated.
  • D. The company should use on-demand inference, which allows the company to pay only for the resources consumed during each inference
    • Explanation: On-demand inference is billed per token at standard rates without the 50% bulk savings provided by batch inference, making it more expensive for processing large volumes of asynchronous requests.

Reference: