Understanding Costs for Running a Chatbot on Amazon Bedrock

Navigating Pricing Models and Key Components

Capacity Planning for Your Implementation

Estimating Total Cost of Ownership (TCO)

Comparing Foundation Models on Pricing and Performance

Conclusion: A Systematic Approach to Cost Estimation

Getting Started with Amazon Bedrock

Meet the Authors

How Much Will It Cost to Run Our Chatbot on Amazon Bedrock?

If you’re exploring AI solutions, one question likely looms large: “How much will it cost to run our chatbot on Amazon Bedrock?” Navigating the complexities of pricing for AI applications can feel overwhelming, filled with terms like tokens, embeddings, and myriad pricing options. For solution architects, technical leaders, and business decision-makers alike, grasping these costs is crucial for effective project planning and budgeting.

In this post, we’ll explore Amazon Bedrock pricing through a practical example: building a customer service chatbot. We’ll dissect essential cost components, walk through capacity planning for a mid-sized call center implementation, and provide detailed pricing calculations across different foundation models (FMs). By the end, you’ll have a framework for estimating your own Amazon Bedrock implementation costs and understanding the factors that influence them.

Understanding Amazon Bedrock

For those unfamiliar, Amazon Bedrock is a fully managed service that allows you to access high-performing foundation models from leading AI companies like AI21 Labs, Anthropic, Cohere, Meta, Stability AI, and Amazon, all through a single API. It equips you with tools for building generative AI applications while ensuring security, privacy, and a responsible AI framework.

This service encompasses pre-trained large language models (LLMs), Retrieval Augmented Generation (RAG) capabilities, and seamless integration with existing knowledge bases. These elements combine to create chatbots that can effectively understand and respond to customer queries with high accuracy and contextual relevance.

Solution Overview

Our chatbot example will utilize a curated set of data sources and employ RAG to retrieve relevant information in real time. By enriching our output with contextual information, we aim to enhance the customer experience significantly.

When diving into Amazon Bedrock pricing, it’s essential to familiarize yourself with several key terms:

• Data Sources: This refers to the documents, manuals, FAQs, etc., that comprise your chatbot’s knowledge base.
• Retrieval-Augmented Generation (RAG): A method that improves LLM output by referencing an authoritative knowledge base, ensuring responses are relevant and accurate.
• Tokens: Units of meaning, with Amazon Bedrock pricing based on the number of input and output tokens processed.
• Context Window: The maximum text length an LLM can process in one request, including both the input and the additional context needed for responses.
• Embeddings: Vector representations that capture semantic meaning, enabling effective search capabilities.
• Vector Store: A database containing the embeddings for your data sources, acting as your knowledge base.
• Large Language Models (LLMs): Models trained on extensive data sets that can generate original text for tasks like question-answering.

Estimating Pricing

One of the hardest parts of implementing an AI solution is predicting your capacity needs accurately. Without proper estimation, you risk over-provisioning, leading to needless expenses, or under-provisioning, resulting in compromised performance.

Let’s consider a typical call center implementation planning a customer service chatbot to handle product inquiries and support requests. For our analysis, we will look at the following capacity considerations:

1. Knowledge Base Size: 10,000 support documents, averaging 500 tokens each. This leads to a total of 5 million tokens.
2. User Queries: Handling 10,000 customer queries monthly, with varying lengths from 50 to 200 tokens.
3. Average Responses: Each response averaging 100 tokens.
4. Concurrency: Planning for 100 simultaneous users.

This gives us:

• 5 million tokens for the knowledge base processing.
• 50,000 embeddings generated from those documents.
• 500,000 tokens for user queries.
• 1 million tokens for generating responses.

These metrics will directly impact your costs:

• Initial setup costs for processing and embedding the knowledge base.
• Ongoing storage costs for your vector database.
• Monthly processing costs for user interactions.
• Infrastructure costs for concurrency.

Calculating Total Cost of Ownership (TCO)

Amazon Bedrock presents flexible pricing models, including:

1. On-Demand: Pay-as-you-go for infrequent or unpredictable usage.
2. Batch: Ideal for processing large data volumes at once.
3. Provisioned Throughput: For consistent workloads requiring upfront commitment.

Using the on-demand pricing model, let’s calculate TCO for our scenario by considering model costs, the knowledge base volume, query estimates, and concurrency.

On-Demand Pricing Formula

Total Cost = (((\text{Input Tokens} + \text{Context Size}) \times \text{Price per 1000 Input Tokens}) + (\text{Output Tokens} \times \text{Price per 1000 Output Tokens}) + \text{Embeddings Cost})

Input tokens and context size are summed with the output tokens to get the total token count incurred.

Embeddings Cost Example:
Using Amazon Titan Text Embeddings V2 (price per 1,000 tokens = $0.00002):

Cost = ((\text{Data Sources} + \text{User Queries}) \times \text{Embeddings cost per 1000 tokens})

Calculating the embeddings gives us:

[
(5,000,000 + 500,000) \times \frac{0.00002}{1000} = \$0.11
]

Now, evaluating costs based on various foundation models using the on-demand pricing formula will yield different figures.

Here’s a quick comparison for selected models:

• Anthropic Claude:
  • Claude 4 Sonnet: Approx. $21.11
  • Claude 3 Haiku: Approx. $1.86
• Amazon Nova:
  • Nova Pro: Approx. $4.91
  • Nova Lite: Approx. $0.47
• Meta Llama:
  • Llama 4 Maverick: Approx. $1.56
  • Llama 3.3 Instruct: Approx. $2.27

Conclusion

Estimating Amazon Bedrock costs need not be daunting. Through our customer service chatbot example, breaking down pricing into core components—token usage, embeddings, and model selection—makes it manageable and predictable.

Key Takeaways:

• Assess your knowledge base size and expected query volume.
• Consider one-time costs versus ongoing operational expenses.
• Compare foundation models based on performance and price.
• Factor in concurrency needs when selecting a pricing model.

By following this structured approach to cost estimation, you can confidently plan your Amazon Bedrock implementation and select a configuration that balances cost, performance, and your specific requirements.

Getting Started with Amazon Bedrock

Amazon Bedrock offers the flexibility to choose the right model and pricing structure for your use case. We encourage you to explore the AWS Pricing Calculator for tailored cost estimates.

For additional insights on building chatbots with Amazon Bedrock, check out the Building with Amazon Bedrock workshop.

We welcome your thoughts! Share your experiences or challenges in the comments below.

About the Authors

Srividhya Pallay is a Solutions Architect II at AWS, specializing in Generative AI and Games, with a background in Computational Data Science.

Prerna Mishra is a Solutions Architect at AWS focusing on Enterprise customers, with expertise in Generative AI and MLOps.

Brian Clark is a Solutions Architect at AWS in the financial services sector, specializing in AI and generative workflows.

Feel free to tailor this blog post based on the specific audiences and channels you’re targeting!